---
_id: '46318'
abstract:
- lang: eng
text: 'Multi-objective (MO) optimization, i.e., the simultaneous optimization of
multiple conflicting objectives, is gaining more and more attention in various
research areas, such as evolutionary computation, machine learning (e.g., (hyper-)parameter
optimization), or logistics (e.g., vehicle routing). Many works in this domain
mention the structural problem property of multimodality as a challenge from two
classical perspectives: (1) finding all globally optimal solution sets, and (2)
avoiding to get trapped in local optima. Interestingly, these streams seem to
transfer many traditional concepts of single-objective (SO) optimization into
claims, assumptions, or even terminology regarding the MO domain, but mostly neglect
the understanding of the structural properties as well as the algorithmic search
behavior on a problem’s landscape. However, some recent works counteract this
trend, by investigating the fundamentals and characteristics of MO problems using
new visualization techniques and gaining surprising insights. Using these visual
insights, this work proposes a step towards a unified terminology to capture multimodality
and locality in a broader way than it is usually done. This enables us to investigate
current research activities in multimodal continuous MO optimization and to highlight
new implications and promising research directions for the design of benchmark
suites, the discovery of MO landscape features, the development of new MO (or
even SO) optimization algorithms, and performance indicators. For all these topics,
we provide a review of ideas and methods but also an outlook on future challenges,
research potential and perspectives that result from recent developments.'
author:
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Pelin
full_name: Aspar, Pelin
last_name: Aspar
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Mike
full_name: Preuss, Mike
last_name: Preuss
- first_name: André H.
full_name: Deutz, André H.
last_name: Deutz
- first_name: Hao
full_name: Wang, Hao
last_name: Wang
- first_name: Michael
full_name: Emmerich, Michael
last_name: Emmerich
citation:
ama: 'Grimme C, Kerschke P, Aspar P, et al. Peeking beyond peaks: Challenges and
research potentials of continuous multimodal multi-objective optimization. Computers
& Operations Research. 2021;136:105489. doi:https://doi.org/10.1016/j.cor.2021.105489'
apa: 'Grimme, C., Kerschke, P., Aspar, P., Trautmann, H., Preuss, M., Deutz, A.
H., Wang, H., & Emmerich, M. (2021). Peeking beyond peaks: Challenges and
research potentials of continuous multimodal multi-objective optimization. Computers
& Operations Research, 136, 105489. https://doi.org/10.1016/j.cor.2021.105489'
bibtex: '@article{Grimme_Kerschke_Aspar_Trautmann_Preuss_Deutz_Wang_Emmerich_2021,
title={Peeking beyond peaks: Challenges and research potentials of continuous
multimodal multi-objective optimization}, volume={136}, DOI={https://doi.org/10.1016/j.cor.2021.105489},
journal={Computers & Operations Research}, author={Grimme, Christian and Kerschke,
Pascal and Aspar, Pelin and Trautmann, Heike and Preuss, Mike and Deutz, André
H. and Wang, Hao and Emmerich, Michael}, year={2021}, pages={105489} }'
chicago: 'Grimme, Christian, Pascal Kerschke, Pelin Aspar, Heike Trautmann, Mike
Preuss, André H. Deutz, Hao Wang, and Michael Emmerich. “Peeking beyond Peaks:
Challenges and Research Potentials of Continuous Multimodal Multi-Objective Optimization.”
Computers & Operations Research 136 (2021): 105489. https://doi.org/10.1016/j.cor.2021.105489.'
ieee: 'C. Grimme et al., “Peeking beyond peaks: Challenges and research potentials
of continuous multimodal multi-objective optimization,” Computers & Operations
Research, vol. 136, p. 105489, 2021, doi: https://doi.org/10.1016/j.cor.2021.105489.'
mla: 'Grimme, Christian, et al. “Peeking beyond Peaks: Challenges and Research Potentials
of Continuous Multimodal Multi-Objective Optimization.” Computers & Operations
Research, vol. 136, 2021, p. 105489, doi:https://doi.org/10.1016/j.cor.2021.105489.'
short: C. Grimme, P. Kerschke, P. Aspar, H. Trautmann, M. Preuss, A.H. Deutz, H.
Wang, M. Emmerich, Computers & Operations Research 136 (2021) 105489.
date_created: 2023-08-04T07:28:34Z
date_updated: 2023-10-16T12:58:42Z
department:
- _id: '34'
- _id: '819'
doi: https://doi.org/10.1016/j.cor.2021.105489
intvolume: ' 136'
keyword:
- Multimodal optimization
- Multi-objective continuous optimization
- Landscape analysis
- Visualization
- Benchmarking
- Theory
- Algorithms
language:
- iso: eng
page: '105489'
publication: Computers & Operations Research
publication_identifier:
issn:
- 0305-0548
status: public
title: 'Peeking beyond peaks: Challenges and research potentials of continuous multimodal
multi-objective optimization'
type: journal_article
user_id: '15504'
volume: 136
year: '2021'
...
---
_id: '48853'
abstract:
- lang: eng
text: In practise, it is often desirable to provide the decision-maker with a rich
set of diverse solutions of decent quality instead of just a single solution.
In this paper we study evolutionary diversity optimization for the knapsack problem
(KP). Our goal is to evolve a population of solutions that all have a profit of
at least (1 - {$ϵ$}) {$\cdot$} OPT, where OPT is the value of an optimal solution.
Furthermore, they should differ in structure with respect to an entropy-based
diversity measure. To this end we propose a simple ({$\mu$} + 1)-EA with initial
approximate solutions calculated by a well-known FPTAS for the KP. We investigate
the effect of different standard mutation operators and introduce biased mutation
and crossover which puts strong probability on flipping bits of low and/or high
frequency within the population. An experimental study on different instances
and settings shows that the proposed mutation operators in most cases perform
slightly inferior in the long term, but show strong benefits if the number of
function evaluations is severely limited.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Aneta
full_name: Neumann, Aneta
last_name: Neumann
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
citation:
ama: 'Bossek J, Neumann A, Neumann F. Breeding Diverse Packings for the Knapsack
Problem by Means of Diversity-Tailored Evolutionary Algorithms. In: Proceedings
of the Genetic and Evolutionary Computation Conference. GECCO ’21. Association
for Computing Machinery; 2021:556–564. doi:10.1145/3449639.3459364'
apa: Bossek, J., Neumann, A., & Neumann, F. (2021). Breeding Diverse Packings
for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms.
Proceedings of the Genetic and Evolutionary Computation Conference, 556–564.
https://doi.org/10.1145/3449639.3459364
bibtex: '@inproceedings{Bossek_Neumann_Neumann_2021, place={New York, NY, USA},
series={GECCO ’21}, title={Breeding Diverse Packings for the Knapsack Problem
by Means of Diversity-Tailored Evolutionary Algorithms}, DOI={10.1145/3449639.3459364},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Bossek, Jakob and Neumann,
Aneta and Neumann, Frank}, year={2021}, pages={556–564}, collection={GECCO ’21}
}'
chicago: 'Bossek, Jakob, Aneta Neumann, and Frank Neumann. “Breeding Diverse Packings
for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms.”
In Proceedings of the Genetic and Evolutionary Computation Conference,
556–564. GECCO ’21. New York, NY, USA: Association for Computing Machinery, 2021.
https://doi.org/10.1145/3449639.3459364.'
ieee: 'J. Bossek, A. Neumann, and F. Neumann, “Breeding Diverse Packings for the
Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms,” in Proceedings
of the Genetic and Evolutionary Computation Conference, 2021, pp. 556–564,
doi: 10.1145/3449639.3459364.'
mla: Bossek, Jakob, et al. “Breeding Diverse Packings for the Knapsack Problem by
Means of Diversity-Tailored Evolutionary Algorithms.” Proceedings of the Genetic
and Evolutionary Computation Conference, Association for Computing Machinery,
2021, pp. 556–564, doi:10.1145/3449639.3459364.
short: 'J. Bossek, A. Neumann, F. Neumann, in: Proceedings of the Genetic and Evolutionary
Computation Conference, Association for Computing Machinery, New York, NY, USA,
2021, pp. 556–564.'
date_created: 2023-11-14T15:58:54Z
date_updated: 2023-12-13T10:45:22Z
department:
- _id: '819'
doi: 10.1145/3449639.3459364
extern: '1'
keyword:
- evolutionary algorithms
- evolutionary diversity optimization
- knapsack problem
- tailored operators
language:
- iso: eng
page: 556–564
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-8350-9
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’21
status: public
title: Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored
Evolutionary Algorithms
type: conference
user_id: '102979'
year: '2021'
...
---
_id: '48860'
abstract:
- lang: eng
text: In the area of evolutionary computation the calculation of diverse sets of
high-quality solutions to a given optimization problem has gained momentum in
recent years under the term evolutionary diversity optimization. Theoretical insights
into the working principles of baseline evolutionary algorithms for diversity
optimization are still rare. In this paper we study the well-known Minimum Spanning
Tree problem (MST) in the context of diversity optimization where population diversity
is measured by the sum of pairwise edge overlaps. Theoretical results provide
insights into the fitness landscape of the MST diversity optimization problem
pointing out that even for a population of {$\mu$} = 2 fitness plateaus (of constant
length) can be reached, but nevertheless diverse sets can be calculated in polynomial
time. We supplement our theoretical results with a series of experiments for the
unconstrained and constraint case where all solutions need to fulfill a minimal
quality threshold. Our results show that a simple ({$\mu$} + 1)-EA can effectively
compute a diversified population of spanning trees of high quality.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
citation:
ama: 'Bossek J, Neumann F. Evolutionary Diversity Optimization and the Minimum Spanning
Tree Problem. In: Proceedings of the Genetic and Evolutionary Computation Conference.
GECCO ’21. Association for Computing Machinery; 2021:198–206. doi:10.1145/3449639.3459363'
apa: Bossek, J., & Neumann, F. (2021). Evolutionary Diversity Optimization and
the Minimum Spanning Tree Problem. Proceedings of the Genetic and Evolutionary
Computation Conference, 198–206. https://doi.org/10.1145/3449639.3459363
bibtex: '@inproceedings{Bossek_Neumann_2021, place={New York, NY, USA}, series={GECCO
’21}, title={Evolutionary Diversity Optimization and the Minimum Spanning Tree
Problem}, DOI={10.1145/3449639.3459363},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Bossek, Jakob and Neumann,
Frank}, year={2021}, pages={198–206}, collection={GECCO ’21} }'
chicago: 'Bossek, Jakob, and Frank Neumann. “Evolutionary Diversity Optimization
and the Minimum Spanning Tree Problem.” In Proceedings of the Genetic and Evolutionary
Computation Conference, 198–206. GECCO ’21. New York, NY, USA: Association
for Computing Machinery, 2021. https://doi.org/10.1145/3449639.3459363.'
ieee: 'J. Bossek and F. Neumann, “Evolutionary Diversity Optimization and the Minimum
Spanning Tree Problem,” in Proceedings of the Genetic and Evolutionary Computation
Conference, 2021, pp. 198–206, doi: 10.1145/3449639.3459363.'
mla: Bossek, Jakob, and Frank Neumann. “Evolutionary Diversity Optimization and
the Minimum Spanning Tree Problem.” Proceedings of the Genetic and Evolutionary
Computation Conference, Association for Computing Machinery, 2021, pp. 198–206,
doi:10.1145/3449639.3459363.
short: 'J. Bossek, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation
Conference, Association for Computing Machinery, New York, NY, USA, 2021, pp.
198–206.'
date_created: 2023-11-14T15:58:55Z
date_updated: 2023-12-13T10:45:37Z
department:
- _id: '819'
doi: 10.1145/3449639.3459363
extern: '1'
keyword:
- evolutionary algorithms
- evolutionary diversity optimization
- minimum spanning tree
- runtime analysis
language:
- iso: eng
page: 198–206
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-8350-9
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’21
status: public
title: Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem
type: conference
user_id: '102979'
year: '2021'
...
---
_id: '48854'
abstract:
- lang: eng
text: We contribute to the theoretical understanding of randomized search heuristics
for dynamic problems. We consider the classical vertex coloring problem on graphs
and investigate the dynamic setting where edges are added to the current graph.
We then analyze the expected time for randomized search heuristics to recompute
high quality solutions. The (1+1) Evolutionary Algorithm and RLS operate in a
setting where the number of colors is bounded and we are minimizing the number
of conflicts. Iterated local search algorithms use an unbounded color palette
and aim to use the smallest colors and, consequently, the smallest number of colors.
We identify classes of bipartite graphs where reoptimization is as hard as or
even harder than optimization from scratch, i.e., starting with a random initialization.
Even adding a single edge can lead to hard symmetry problems. However, graph classes
that are hard for one algorithm turn out to be easy for others. In most cases
our bounds show that reoptimization is faster than optimizing from scratch. We
further show that tailoring mutation operators to parts of the graph where changes
have occurred can significantly reduce the expected reoptimization time. In most
settings the expected reoptimization time for such tailored algorithms is linear
in the number of added edges. However, tailored algorithms cannot prevent exponential
times in settings where the original algorithm is inefficient.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
- first_name: Pan
full_name: Peng, Pan
last_name: Peng
- first_name: Dirk
full_name: Sudholt, Dirk
last_name: Sudholt
citation:
ama: Bossek J, Neumann F, Peng P, Sudholt D. Time Complexity Analysis of Randomized
Search Heuristics for the Dynamic Graph Coloring Problem. Algorithmica.
2021;83(10):3148–3179. doi:10.1007/s00453-021-00838-3
apa: Bossek, J., Neumann, F., Peng, P., & Sudholt, D. (2021). Time Complexity
Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem.
Algorithmica, 83(10), 3148–3179. https://doi.org/10.1007/s00453-021-00838-3
bibtex: '@article{Bossek_Neumann_Peng_Sudholt_2021, title={Time Complexity Analysis
of Randomized Search Heuristics for the Dynamic Graph Coloring Problem}, volume={83},
DOI={10.1007/s00453-021-00838-3},
number={10}, journal={Algorithmica}, author={Bossek, Jakob and Neumann, Frank
and Peng, Pan and Sudholt, Dirk}, year={2021}, pages={3148–3179} }'
chicago: 'Bossek, Jakob, Frank Neumann, Pan Peng, and Dirk Sudholt. “Time Complexity
Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem.”
Algorithmica 83, no. 10 (2021): 3148–3179. https://doi.org/10.1007/s00453-021-00838-3.'
ieee: 'J. Bossek, F. Neumann, P. Peng, and D. Sudholt, “Time Complexity Analysis
of Randomized Search Heuristics for the Dynamic Graph Coloring Problem,” Algorithmica,
vol. 83, no. 10, pp. 3148–3179, 2021, doi: 10.1007/s00453-021-00838-3.'
mla: Bossek, Jakob, et al. “Time Complexity Analysis of Randomized Search Heuristics
for the Dynamic Graph Coloring Problem.” Algorithmica, vol. 83, no. 10,
2021, pp. 3148–3179, doi:10.1007/s00453-021-00838-3.
short: J. Bossek, F. Neumann, P. Peng, D. Sudholt, Algorithmica 83 (2021) 3148–3179.
date_created: 2023-11-14T15:58:54Z
date_updated: 2023-12-13T10:51:34Z
department:
- _id: '819'
doi: 10.1007/s00453-021-00838-3
intvolume: ' 83'
issue: '10'
keyword:
- Dynamic optimization
- Evolutionary algorithms
- Running time analysis
language:
- iso: eng
page: 3148–3179
publication: Algorithmica
publication_identifier:
issn:
- 0178-4617
status: public
title: Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph
Coloring Problem
type: journal_article
user_id: '102979'
volume: 83
year: '2021'
...
---
_id: '37136'
abstract:
- lang: eng
text: This study examines the relation between voluntary audit and the cost of debt
in private firms. We use a sample of 4,058 small private firms operating in the
period 2006‐2017 that are not subject to mandatory audits. Firms decide for a
voluntary audit of financial statements either because the economic setting in
which they operate effectively forces them to do so (e.g., ownership complexity,
export‐oriented supply chain, subsidiary status) or because firm fundamentals
and/or financial reporting practices limit their access to financial debt, both
reflected in earnings quality. We use these factors to model the decision for
voluntary audit. In the outcome analyses, we find robust evidence that voluntary
audits are associated with higher, rather than lower, interest rate by up to 3.0
percentage points. This effect is present regardless of the perceived audit quality
(Big‐4 vs. non‐Big‐4), but is stronger for non‐Big‐4 audits where auditees have
a stronger position relative to auditors. Audited firms’ earnings are less informative
about future operating performance relative to unaudited counterparts. We conclude
that voluntary audits facilitate access to financial debt for firms with higher
risk that may otherwise have no access to this form of financing. The price paid
is reflected in higher interest rates charged to firms with voluntary audits –
firms with higher information and/or fundamental risk.
author:
- first_name: Riste
full_name: Ichev, Riste
last_name: Ichev
- first_name: Jernej
full_name: Koren, Jernej
last_name: Koren
- first_name: Urska
full_name: Kosi, Urska
id: '54068'
last_name: Kosi
- first_name: Katarina
full_name: Sitar Sustar, Katarina
last_name: Sitar Sustar
- first_name: Aljosa
full_name: Valentincic, Aljosa
last_name: Valentincic
citation:
ama: 'Ichev R, Koren J, Kosi U, Sitar Sustar K, Valentincic A. Cost of Debt for
Private Firms Revisited: Voluntary Audits as a Reflection of Risk.; 2021.'
apa: 'Ichev, R., Koren, J., Kosi, U., Sitar Sustar, K., & Valentincic, A. (2021).
Cost of Debt for Private Firms Revisited: Voluntary Audits as a Reflection
of Risk.'
bibtex: '@book{Ichev_Koren_Kosi_Sitar Sustar_Valentincic_2021, title={Cost of Debt
for Private Firms Revisited: Voluntary Audits as a Reflection of Risk}, author={Ichev,
Riste and Koren, Jernej and Kosi, Urska and Sitar Sustar, Katarina and Valentincic,
Aljosa}, year={2021} }'
chicago: 'Ichev, Riste, Jernej Koren, Urska Kosi, Katarina Sitar Sustar, and Aljosa
Valentincic. Cost of Debt for Private Firms Revisited: Voluntary Audits as
a Reflection of Risk, 2021.'
ieee: 'R. Ichev, J. Koren, U. Kosi, K. Sitar Sustar, and A. Valentincic, Cost
of Debt for Private Firms Revisited: Voluntary Audits as a Reflection of Risk.
2021.'
mla: 'Ichev, Riste, et al. Cost of Debt for Private Firms Revisited: Voluntary
Audits as a Reflection of Risk. 2021.'
short: 'R. Ichev, J. Koren, U. Kosi, K. Sitar Sustar, A. Valentincic, Cost of Debt
for Private Firms Revisited: Voluntary Audits as a Reflection of Risk, 2021.'
date_created: 2023-01-17T15:03:08Z
date_updated: 2023-01-18T13:40:40Z
department:
- _id: '635'
- _id: '186'
- _id: '551'
keyword:
- private firms
- voluntary audit
- cost of debt
- self‐selection bias
- risk
language:
- iso: eng
main_file_link:
- url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3853927
status: public
title: 'Cost of Debt for Private Firms Revisited: Voluntary Audits as a Reflection
of Risk'
type: working_paper
user_id: '88603'
year: '2021'
...
---
_id: '21436'
abstract:
- lang: eng
text: Ultrasonic wire bonding is a solid-state joining process, used in the electronics
industry to form electrical connections, e.g. to connect electrical terminals
within semiconductor modules. Many process parameters affect the bond strength,
such like the bond normal force, ultrasonic power, wire material and bonding frequency.
Today, process design, development, and optimization is most likely based on the
knowledge of process engineers and is mainly performed by experimental testing.
In this contribution, a newly developed simulation tool is presented, to reduce
time and costs and efficiently determine optimized process parameter. Based on
a co-simulation of MATLAB and ANSYS, the different physical phenomena of the wire
bonding process are considered using finite element simulation for the complex
plastic deformation of the wire and reduced order models for the transient dynamics
of the transducer, wire, substrate and bond formation. The model parameters such
as the coefficients of friction between bond tool and wire and between wire and
substrate were determined for aluminium and copper wire in experiments with a
test rig specially developed for the requirements of heavy wire bonding. To reduce
simulation time, for the finite element simulation a restart analysis and high
performance computing is utilized. Detailed analysis of the bond formation showed,
that the normal pressure distribution in the contact between wire and substrate
has high impact on bond formation and distribution of welded areas in the contact
area.
author:
- first_name: Reinhard
full_name: Schemmel, Reinhard
id: '28647'
last_name: Schemmel
- first_name: Viktor
full_name: Krieger, Viktor
last_name: Krieger
- first_name: Tobias
full_name: Hemsel, Tobias
id: '210'
last_name: Hemsel
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: Schemmel R, Krieger V, Hemsel T, Sextro W. Co-simulation of MATLAB and ANSYS
for ultrasonic wire bonding process optimization. Microelectronics Reliability.
2021;119:114077. doi:https://doi.org/10.1016/j.microrel.2021.114077
apa: Schemmel, R., Krieger, V., Hemsel, T., & Sextro, W. (2021). Co-simulation
of MATLAB and ANSYS for ultrasonic wire bonding process optimization. Microelectronics
Reliability, 119, 114077. https://doi.org/10.1016/j.microrel.2021.114077
bibtex: '@article{Schemmel_Krieger_Hemsel_Sextro_2021, title={Co-simulation of MATLAB
and ANSYS for ultrasonic wire bonding process optimization}, volume={119}, DOI={https://doi.org/10.1016/j.microrel.2021.114077},
journal={Microelectronics Reliability}, author={Schemmel, Reinhard and Krieger,
Viktor and Hemsel, Tobias and Sextro, Walter}, year={2021}, pages={114077} }'
chicago: 'Schemmel, Reinhard, Viktor Krieger, Tobias Hemsel, and Walter Sextro.
“Co-Simulation of MATLAB and ANSYS for Ultrasonic Wire Bonding Process Optimization.”
Microelectronics Reliability 119 (2021): 114077. https://doi.org/10.1016/j.microrel.2021.114077.'
ieee: 'R. Schemmel, V. Krieger, T. Hemsel, and W. Sextro, “Co-simulation of MATLAB
and ANSYS for ultrasonic wire bonding process optimization,” Microelectronics
Reliability, vol. 119, p. 114077, 2021, doi: https://doi.org/10.1016/j.microrel.2021.114077.'
mla: Schemmel, Reinhard, et al. “Co-Simulation of MATLAB and ANSYS for Ultrasonic
Wire Bonding Process Optimization.” Microelectronics Reliability, vol.
119, 2021, p. 114077, doi:https://doi.org/10.1016/j.microrel.2021.114077.
short: R. Schemmel, V. Krieger, T. Hemsel, W. Sextro, Microelectronics Reliability
119 (2021) 114077.
date_created: 2021-03-10T09:37:02Z
date_updated: 2023-09-21T14:15:33Z
department:
- _id: '151'
doi: https://doi.org/10.1016/j.microrel.2021.114077
intvolume: ' 119'
keyword:
- Ultrasonic heavy wire bonding
- Co-simulation
- ANSYS
- MATLAB
- Process optimization
- Friction coefficient
- Copper-copper
- Aluminium-copper
language:
- iso: eng
page: '114077'
publication: Microelectronics Reliability
publication_identifier:
issn:
- 0026-2714
publication_status: published
quality_controlled: '1'
status: public
title: Co-simulation of MATLAB and ANSYS for ultrasonic wire bonding process optimization
type: journal_article
user_id: '210'
volume: 119
year: '2021'
...
---
_id: '22930'
abstract:
- lang: eng
text: Self-piercing riveting is an established technique for joining multi-material
structures in car body manufacturing. Rivets for self-piercing riveting differ
in their geometry, the material used, the condition of the material and their
surface condition. To shorten the manufacturing process by omitting the heat treatment
and the coating process, the authors have elaborated a concept for the use of
stainless steel with high strain hardening as a rivet material. The focus of the
present investigation is on the evaluation of the influences of the rivet’s geometry
and material on its deformation behaviour. Conventional rivets of types P and
HD2, a rivet with an improved geometry made of treatable steel 38B2, and rivets
made of the stainless steels 1.3815 and 1.4541 are examined. The analysis is conducted
by means of multi-step joining tests for two material combinations comprising
high-strength steel HCT70X and aluminium EN AW-5083. The joints are cut to provide
a cross-section and the deformation behaviour of the different rivets is analysed
on the basis of the measured changes in geometry and hardness. In parallel, an
examination of the force-stroke curves provides further insights. It can be demonstrated
that, besides the geometry, the material strength, in particular, has a significant
influence on the deformation behaviour of the rivet. The strength of steel 1.4541
is seen to be too low for the joining task, while the strength of steel 1.3815
is sufficient, and hence the investigation confirms the capability of rivets made
of 1.3815 for joining even challenging material combinations.
author:
- first_name: Benedikt
full_name: Uhe, Benedikt
id: '38131'
last_name: Uhe
- first_name: Clara-Maria
full_name: Kuball, Clara-Maria
last_name: Kuball
- first_name: Marion
full_name: Merklein, Marion
last_name: Merklein
- first_name: Gerson
full_name: Meschut, Gerson
id: '32056'
last_name: Meschut
orcid: 0000-0002-2763-1246
citation:
ama: 'Uhe B, Kuball C-M, Merklein M, Meschut G. Self-Piercing Riveting Using Rivets
Made of Stainless Steel with High Strain Hardening. In: Daehn G, Cao J, Kinsey
B, Tekkaya E, Vivek A, Yoshida Y, eds. Forming the Future - Proceedings of
the 13th International Conference on the Technology of Plasticity. The Minerals,
Metals & Materials Series. Springer; 2021:1495-1506. doi:10.1007/978-3-030-75381-8_124'
apa: Uhe, B., Kuball, C.-M., Merklein, M., & Meschut, G. (2021). Self-Piercing
Riveting Using Rivets Made of Stainless Steel with High Strain Hardening. In G.
Daehn, J. Cao, B. Kinsey, E. Tekkaya, A. Vivek, & Y. Yoshida (Eds.), Forming
the Future - Proceedings of the 13th International Conference on the Technology
of Plasticity. The Minerals, Metals & Materials Series. (pp. 1495–1506).
Springer. https://doi.org/10.1007/978-3-030-75381-8_124
bibtex: '@inbook{Uhe_Kuball_Merklein_Meschut_2021, place={Cham}, title={Self-Piercing
Riveting Using Rivets Made of Stainless Steel with High Strain Hardening}, DOI={10.1007/978-3-030-75381-8_124},
booktitle={Forming the Future - Proceedings of the 13th International Conference
on the Technology of Plasticity. The Minerals, Metals & Materials Series.},
publisher={Springer}, author={Uhe, Benedikt and Kuball, Clara-Maria and Merklein,
Marion and Meschut, Gerson}, editor={Daehn, Glenn and Cao, Jian and Kinsey, Brad
and Tekkaya, Erman and Vivek, Anupam and Yoshida, Yoshinori}, year={2021}, pages={1495–1506}
}'
chicago: 'Uhe, Benedikt, Clara-Maria Kuball, Marion Merklein, and Gerson Meschut.
“Self-Piercing Riveting Using Rivets Made of Stainless Steel with High Strain
Hardening.” In Forming the Future - Proceedings of the 13th International Conference
on the Technology of Plasticity. The Minerals, Metals & Materials Series.,
edited by Glenn Daehn, Jian Cao, Brad Kinsey, Erman Tekkaya, Anupam Vivek, and
Yoshinori Yoshida, 1495–1506. Cham: Springer, 2021. https://doi.org/10.1007/978-3-030-75381-8_124.'
ieee: 'B. Uhe, C.-M. Kuball, M. Merklein, and G. Meschut, “Self-Piercing Riveting
Using Rivets Made of Stainless Steel with High Strain Hardening,” in Forming
the Future - Proceedings of the 13th International Conference on the Technology
of Plasticity. The Minerals, Metals & Materials Series., G. Daehn, J.
Cao, B. Kinsey, E. Tekkaya, A. Vivek, and Y. Yoshida, Eds. Cham: Springer, 2021,
pp. 1495–1506.'
mla: Uhe, Benedikt, et al. “Self-Piercing Riveting Using Rivets Made of Stainless
Steel with High Strain Hardening.” Forming the Future - Proceedings of the
13th International Conference on the Technology of Plasticity. The Minerals, Metals
& Materials Series., edited by Glenn Daehn et al., Springer, 2021, pp.
1495–506, doi:10.1007/978-3-030-75381-8_124.
short: 'B. Uhe, C.-M. Kuball, M. Merklein, G. Meschut, in: G. Daehn, J. Cao, B.
Kinsey, E. Tekkaya, A. Vivek, Y. Yoshida (Eds.), Forming the Future - Proceedings
of the 13th International Conference on the Technology of Plasticity. The Minerals,
Metals & Materials Series., Springer, Cham, 2021, pp. 1495–1506.'
date_created: 2021-08-04T14:02:32Z
date_updated: 2026-02-27T10:40:39Z
department:
- _id: '157'
doi: 10.1007/978-3-030-75381-8_124
editor:
- first_name: Glenn
full_name: Daehn, Glenn
last_name: Daehn
- first_name: Jian
full_name: Cao, Jian
last_name: Cao
- first_name: Brad
full_name: Kinsey, Brad
last_name: Kinsey
- first_name: Erman
full_name: Tekkaya, Erman
last_name: Tekkaya
- first_name: Anupam
full_name: Vivek, Anupam
last_name: Vivek
- first_name: Yoshinori
full_name: Yoshida, Yoshinori
last_name: Yoshida
keyword:
- Self-piercing riveting
- Lightweight design
- Deformation behaviour
- Stainless steel
- High nitrogen steel
language:
- iso: eng
page: 1495-1506
place: Cham
publication: Forming the Future - Proceedings of the 13th International Conference
on the Technology of Plasticity. The Minerals, Metals & Materials Series.
publication_status: published
publisher: Springer
quality_controlled: '1'
status: public
title: Self-Piercing Riveting Using Rivets Made of Stainless Steel with High Strain
Hardening
type: book_chapter
user_id: '53912'
year: '2021'
...
---
_id: '22274'
abstract:
- lang: eng
text: 'The use of high-strength steel and aluminium is rising due to the intensified
efforts being made in lightweight design, and self-piercing riveting is becoming
increasingly important. Conventional rivets for self-piercing riveting differ
in their geometry, the material used, the condition of the material and the coating.
To shorten the manufacturing process, the use of stainless steel with high strain
hardening as the rivet material represents a promising approach. This allows the
coating of the rivets to be omitted due to the corrosion resistance of the material
and, since the strength of the stainless steel is achieved by cold forming, heat
treatment is no longer required. In addition, it is possible to adjust the local
strength within the rivet. Because of that, the authors have elaborated a concept
for using high nitrogen steel 1.3815 as the rivet material. The present investigation
focusses on the joint strength in order to evaluate the capability of rivets in
high nitrogen steel by comparison to conventional rivets made of treatable steel.
Due to certain challenges in the forming process of the high nitrogen steel rivets,
deviations result from the targeted rivet geometry. Mainly these deviations cause
a lower joint strength with these rivets, which is, however, adequate. All in
all, the capability of the new rivet is proven by the results of this investigation. '
author:
- first_name: Benedikt
full_name: Uhe, Benedikt
id: '38131'
last_name: Uhe
- first_name: Clara-Maria
full_name: Kuball, Clara-Maria
last_name: Kuball
- first_name: Marion
full_name: Merklein, Marion
last_name: Merklein
- first_name: Gerson
full_name: Meschut, Gerson
id: '32056'
last_name: Meschut
orcid: 0000-0002-2763-1246
citation:
ama: 'Uhe B, Kuball C-M, Merklein M, Meschut G. Strength of self-piercing riveted
Joints with conventional Rivets and Rivets made of High Nitrogen Steel. In: ;
2021. doi:10.25518/esaform21.1911'
apa: Uhe, B., Kuball, C.-M., Merklein, M., & Meschut, G. (2021). Strength
of self-piercing riveted Joints with conventional Rivets and Rivets made of High
Nitrogen Steel. 24th International Conference on Material Forming (ESAFORM),
Liège, Belgien. https://doi.org/10.25518/esaform21.1911
bibtex: '@inproceedings{Uhe_Kuball_Merklein_Meschut_2021, title={Strength of self-piercing
riveted Joints with conventional Rivets and Rivets made of High Nitrogen Steel},
DOI={10.25518/esaform21.1911},
author={Uhe, Benedikt and Kuball, Clara-Maria and Merklein, Marion and Meschut,
Gerson}, year={2021} }'
chicago: Uhe, Benedikt, Clara-Maria Kuball, Marion Merklein, and Gerson Meschut.
“Strength of Self-Piercing Riveted Joints with Conventional Rivets and Rivets
Made of High Nitrogen Steel,” 2021. https://doi.org/10.25518/esaform21.1911.
ieee: 'B. Uhe, C.-M. Kuball, M. Merklein, and G. Meschut, “Strength of self-piercing
riveted Joints with conventional Rivets and Rivets made of High Nitrogen Steel,”
presented at the 24th International Conference on Material Forming (ESAFORM),
Liège, Belgien, 2021, doi: 10.25518/esaform21.1911.'
mla: Uhe, Benedikt, et al. Strength of Self-Piercing Riveted Joints with Conventional
Rivets and Rivets Made of High Nitrogen Steel. 2021, doi:10.25518/esaform21.1911.
short: 'B. Uhe, C.-M. Kuball, M. Merklein, G. Meschut, in: 2021.'
conference:
end_date: 2021-04-16
location: Liège, Belgien
name: 24th International Conference on Material Forming (ESAFORM)
start_date: 2021-04-14
date_created: 2021-05-31T10:17:37Z
date_updated: 2026-02-27T10:25:13Z
department:
- _id: '157'
doi: 10.25518/esaform21.1911
keyword:
- Self-piercing Riveting
- Joining Technology
- Rivet Geometry
- Rivet Material
- High Nitrogen Steel
- Joint Strength
language:
- iso: eng
quality_controlled: '1'
status: public
title: Strength of self-piercing riveted Joints with conventional Rivets and Rivets
made of High Nitrogen Steel
type: conference
user_id: '53912'
year: '2021'
...
---
_id: '32558'
abstract:
- lang: eng
text: With the rapid progress of technological development, self-efficacy in reference
to digital devices (i.e., information and computer technology [ICT] self-efficacy)
is an important driver that helps students to deal with technological problems
and support their lifelong learning processes. Schools, peers, and home learning
environments are important sources for the development of positive self-efficacy.
Expanding on previous research, we investigated the associations between different
aspects of the digital home learning environment and students’ ICT self-efficacy.
The moderation effects of gender were also tested. A total of 651 children answered
a questionnaire about different digital home learning environment dimensions and
estimated their ICT self-efficacy using an adapted scale—Schwarzer and Jerusalem’s
(1999) general self-efficacy scale. Using the structural equation modeling technique,
a digital home learning environment containing six different qualities of parental
support was investigated. Families’ cultural capital, parents’ attitudes toward
the Internet, and shared Internet activities at home contributed positively to
ICT self-efficacy. We observed small gender differences, with the moderation effect
being nonsignificant. The results help researchers and practitioners to understand
how different dimensions of the digital home learning environment support ICT
self-efficacy. We will discuss how parents can enhance the home learning environment
and how teachers can integrate this knowledge into formal education.
author:
- first_name: Sabrina
full_name: Bonanati, Sabrina
last_name: Bonanati
- first_name: Heike M.
full_name: Buhl, Heike M.
id: '27152'
last_name: Buhl
citation:
ama: Bonanati S, Buhl HM. The digital home learning environment and its relation
to children’s ICT self-efficacy. Learning Environments Research. 2021;25(2):485-505.
doi:10.1007/s10984-021-09377-8
apa: Bonanati, S., & Buhl, H. M. (2021). The digital home learning environment
and its relation to children’s ICT self-efficacy. Learning Environments Research,
25(2), 485–505. https://doi.org/10.1007/s10984-021-09377-8
bibtex: '@article{Bonanati_Buhl_2021, title={The digital home learning environment
and its relation to children’s ICT self-efficacy}, volume={25}, DOI={10.1007/s10984-021-09377-8},
number={2}, journal={Learning Environments Research}, publisher={Springer Science
and Business Media LLC}, author={Bonanati, Sabrina and Buhl, Heike M.}, year={2021},
pages={485–505} }'
chicago: 'Bonanati, Sabrina, and Heike M. Buhl. “The Digital Home Learning Environment
and Its Relation to Children’s ICT Self-Efficacy.” Learning Environments Research
25, no. 2 (2021): 485–505. https://doi.org/10.1007/s10984-021-09377-8.'
ieee: 'S. Bonanati and H. M. Buhl, “The digital home learning environment and its
relation to children’s ICT self-efficacy,” Learning Environments Research,
vol. 25, no. 2, pp. 485–505, 2021, doi: 10.1007/s10984-021-09377-8.'
mla: Bonanati, Sabrina, and Heike M. Buhl. “The Digital Home Learning Environment
and Its Relation to Children’s ICT Self-Efficacy.” Learning Environments Research,
vol. 25, no. 2, Springer Science and Business Media LLC, 2021, pp. 485–505, doi:10.1007/s10984-021-09377-8.
short: S. Bonanati, H.M. Buhl, Learning Environments Research 25 (2021) 485–505.
date_created: 2022-08-03T05:53:48Z
date_updated: 2025-07-16T08:38:51Z
department:
- _id: '427'
doi: 10.1007/s10984-021-09377-8
intvolume: ' 25'
issue: '2'
keyword:
- Digital media use
- Gender
- Home learning environment
- ICT self-efcacy
- Motivation
- Parental involvement
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://link.springer.com/content/pdf/10.1007/s10984-021-09377-8.pdf
oa: '1'
page: 485-505
project:
- _id: '378'
grant_number: 01JD1814A
name: 'DigHomE: DigHomE: Digital Home Learning Environment – Gelingensbedingungen
elterlicher Unterstützung bei der informationsorientierten Internetnutzung'
publication: Learning Environments Research
publication_identifier:
issn:
- 1387-1579
- 1573-1855
publication_status: published
publisher: Springer Science and Business Media LLC
status: public
title: The digital home learning environment and its relation to children’s ICT self-efficacy
type: journal_article
user_id: '27152'
volume: 25
year: '2021'
...
---
_id: '19609'
abstract:
- lang: eng
text: "Modern services comprise interconnected components,\r\ne.g., microservices
in a service mesh, that can scale and\r\nrun on multiple nodes across the network
on demand. To process\r\nincoming traffic, service components have to be instantiated
and\r\ntraffic assigned to these instances, taking capacities and changing\r\ndemands
into account. This challenge is usually solved with\r\ncustom approaches designed
by experts. While this typically\r\nworks well for the considered scenario, the
models often rely\r\non unrealistic assumptions or on knowledge that is not available\r\nin
practice (e.g., a priori knowledge).\r\n\r\nWe propose a novel deep reinforcement
learning approach that\r\nlearns how to best coordinate services and is geared
towards\r\nrealistic assumptions. It interacts with the network and relies on\r\navailable,
possibly delayed monitoring information. Rather than\r\ndefining a complex model
or an algorithm how to achieve an\r\nobjective, our model-free approach adapts
to various objectives\r\nand traffic patterns. An agent is trained offline without
expert\r\nknowledge and then applied online with minimal overhead. Compared\r\nto
a state-of-the-art heuristic, it significantly improves flow\r\nthroughput and
overall network utility on real-world network\r\ntopologies and traffic traces.
It also learns to optimize different\r\nobjectives, generalizes to scenarios with
unseen, stochastic traffic\r\npatterns, and scales to large real-world networks."
author:
- first_name: Stefan Balthasar
full_name: Schneider, Stefan Balthasar
id: '35343'
last_name: Schneider
orcid: 0000-0001-8210-4011
- first_name: Adnan
full_name: Manzoor, Adnan
last_name: Manzoor
- first_name: Haydar
full_name: Qarawlus, Haydar
last_name: Qarawlus
- first_name: Rafael
full_name: Schellenberg, Rafael
last_name: Schellenberg
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
- first_name: Ramin
full_name: Khalili, Ramin
last_name: Khalili
- first_name: Artur
full_name: Hecker, Artur
last_name: Hecker
citation:
ama: 'Schneider SB, Manzoor A, Qarawlus H, et al. Self-Driving Network and Service
Coordination Using Deep Reinforcement Learning. In: IEEE International Conference
on Network and Service Management (CNSM). IEEE; 2020.'
apa: Schneider, S. B., Manzoor, A., Qarawlus, H., Schellenberg, R., Karl, H., Khalili,
R., & Hecker, A. (2020). Self-Driving Network and Service Coordination Using
Deep Reinforcement Learning. In IEEE International Conference on Network and
Service Management (CNSM). IEEE.
bibtex: '@inproceedings{Schneider_Manzoor_Qarawlus_Schellenberg_Karl_Khalili_Hecker_2020,
title={Self-Driving Network and Service Coordination Using Deep Reinforcement
Learning}, booktitle={IEEE International Conference on Network and Service Management
(CNSM)}, publisher={IEEE}, author={Schneider, Stefan Balthasar and Manzoor, Adnan
and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Khalili, Ramin
and Hecker, Artur}, year={2020} }'
chicago: Schneider, Stefan Balthasar, Adnan Manzoor, Haydar Qarawlus, Rafael Schellenberg,
Holger Karl, Ramin Khalili, and Artur Hecker. “Self-Driving Network and Service
Coordination Using Deep Reinforcement Learning.” In IEEE International Conference
on Network and Service Management (CNSM). IEEE, 2020.
ieee: S. B. Schneider et al., “Self-Driving Network and Service Coordination
Using Deep Reinforcement Learning,” in IEEE International Conference on Network
and Service Management (CNSM), 2020.
mla: Schneider, Stefan Balthasar, et al. “Self-Driving Network and Service Coordination
Using Deep Reinforcement Learning.” IEEE International Conference on Network
and Service Management (CNSM), IEEE, 2020.
short: 'S.B. Schneider, A. Manzoor, H. Qarawlus, R. Schellenberg, H. Karl, R. Khalili,
A. Hecker, in: IEEE International Conference on Network and Service Management
(CNSM), IEEE, 2020.'
date_created: 2020-09-22T06:28:22Z
date_updated: 2022-01-06T06:54:08Z
ddc:
- '006'
department:
- _id: '75'
file:
- access_level: open_access
content_type: application/pdf
creator: stschn
date_created: 2020-09-22T06:29:16Z
date_updated: 2020-09-22T06:36:00Z
file_id: '19610'
file_name: ris_with_copyright.pdf
file_size: 642999
relation: main_file
file_date_updated: 2020-09-22T06:36:00Z
has_accepted_license: '1'
keyword:
- self-driving networks
- self-learning
- network coordination
- service coordination
- reinforcement learning
- deep learning
- nfv
language:
- iso: eng
oa: '1'
project:
- _id: '1'
name: SFB 901
- _id: '4'
name: SFB 901 - Project Area C
- _id: '16'
name: SFB 901 - Subproject C4
publication: IEEE International Conference on Network and Service Management (CNSM)
publisher: IEEE
status: public
title: Self-Driving Network and Service Coordination Using Deep Reinforcement Learning
type: conference
user_id: '35343'
year: '2020'
...
---
_id: '17370'
abstract:
- lang: eng
text: " We consider a natural extension to the metric uncapacitated Facility Location
Problem (FLP) in which requests ask for different commodities out of a finite
set \\( S \\) of commodities.\r\n Ravi and Sinha (SODA 2004) introduced the model
as the \\emph{Multi-Commodity Facility Location Problem} (MFLP) and considered
it an offline optimization problem.\r\n The model itself is similar to the FLP:
i.e., requests are located at points of a finite metric space and the task of
an algorithm is to construct facilities and assign requests to facilities while
minimizing the construction cost and the sum over all assignment distances.\r\n
\ In addition, requests and facilities are heterogeneous; they request or offer
multiple commodities out of $S$.\r\n A request has to be connected to a set of
facilities jointly offering the commodities demanded by it.\r\n In comparison
to the FLP, an algorithm has to decide not only if and where to place facilities,
but also which commodities to offer at each.\r\n\r\n To the best of our knowledge
we are the first to study the problem in its online variant in which requests,
their positions and their commodities are not known beforehand but revealed over
time.\r\n We present results regarding the competitive ratio.\r\n On the one
hand, we show that heterogeneity influences the competitive ratio by developing
a lower bound on the competitive ratio for any randomized online algorithm of
\\( \\Omega ( \\sqrt{|S|} + \\frac{\\log n}{\\log \\log n} ) \\) that already
holds for simple line metrics.\r\n Here, \\( n \\) is the number of requests.\r\n
\ On the other side, we establish a deterministic \\( \\mathcal{O}(\\sqrt{|S|}
\\cdot \\log n) \\)-competitive algorithm and a randomized \\( \\mathcal{O}(\\sqrt{|S|}
\\cdot \\frac{\\log n}{\\log \\log n} ) \\)-competitive algorithm.\r\n Further,
we show that when considering a more special class of cost functions for the construction
cost of a facility, the competitive ratio decreases given by our deterministic
algorithm depending on the function."
author:
- first_name: Jannik
full_name: Castenow, Jannik
id: '38705'
last_name: Castenow
- first_name: Björn
full_name: Feldkord, Björn
id: '22704'
last_name: Feldkord
- first_name: Till
full_name: Knollmann, Till
id: '39241'
last_name: Knollmann
orcid: 0000-0003-2014-4696
- first_name: Manuel
full_name: Malatyali, Manuel
last_name: Malatyali
- first_name: Friedhelm
full_name: Meyer auf der Heide, Friedhelm
id: '15523'
last_name: Meyer auf der Heide
citation:
ama: 'Castenow J, Feldkord B, Knollmann T, Malatyali M, Meyer auf der Heide F. The
Online Multi-Commodity Facility Location Problem. In: Proceedings of the 32nd
ACM Symposium on Parallelism in Algorithms and Architectures. ; 2020. doi:10.1145/3350755.3400281'
apa: Castenow, J., Feldkord, B., Knollmann, T., Malatyali, M., & Meyer auf der
Heide, F. (2020). The Online Multi-Commodity Facility Location Problem. In Proceedings
of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures.
https://doi.org/10.1145/3350755.3400281
bibtex: '@inproceedings{Castenow_Feldkord_Knollmann_Malatyali_Meyer auf der Heide_2020,
title={The Online Multi-Commodity Facility Location Problem}, DOI={10.1145/3350755.3400281},
booktitle={Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms
and Architectures}, author={Castenow, Jannik and Feldkord, Björn and Knollmann,
Till and Malatyali, Manuel and Meyer auf der Heide, Friedhelm}, year={2020} }'
chicago: Castenow, Jannik, Björn Feldkord, Till Knollmann, Manuel Malatyali, and
Friedhelm Meyer auf der Heide. “The Online Multi-Commodity Facility Location Problem.”
In Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures,
2020. https://doi.org/10.1145/3350755.3400281.
ieee: J. Castenow, B. Feldkord, T. Knollmann, M. Malatyali, and F. Meyer auf der
Heide, “The Online Multi-Commodity Facility Location Problem,” in Proceedings
of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures,
2020.
mla: Castenow, Jannik, et al. “The Online Multi-Commodity Facility Location Problem.”
Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures,
2020, doi:10.1145/3350755.3400281.
short: 'J. Castenow, B. Feldkord, T. Knollmann, M. Malatyali, F. Meyer auf der Heide,
in: Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures,
2020.'
date_created: 2020-07-14T07:53:20Z
date_updated: 2022-01-06T06:53:10Z
ddc:
- '000'
department:
- _id: '63'
doi: 10.1145/3350755.3400281
external_id:
arxiv:
- '2005.08391'
file:
- access_level: closed
content_type: application/pdf
creator: tillk
date_created: 2020-07-14T07:56:52Z
date_updated: 2020-07-14T07:56:52Z
file_id: '17373'
file_name: 3350755.3400281.pdf
file_size: 1271416
relation: main_file
success: 1
file_date_updated: 2020-07-14T07:56:52Z
has_accepted_license: '1'
keyword:
- Online Multi-Commodity Facility Location
- Competitive Ratio
- Online Optimization
- Facility Location Problem
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '2'
name: SFB 901 - Project Area A
- _id: '5'
name: SFB 901 - Subproject A1
publication: Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and
Architectures
publication_identifier:
isbn:
- '9781450369350'
publication_status: published
status: public
title: The Online Multi-Commodity Facility Location Problem
type: conference
user_id: '39241'
year: '2020'
...
---
_id: '20143'
author:
- first_name: Mortaza
full_name: Otroshi, Mortaza
id: '71269'
last_name: Otroshi
orcid: 0000-0002-8652-9209
- first_name: Moritz
full_name: Rossel, Moritz
last_name: Rossel
- first_name: Gerson
full_name: Meschut, Gerson
id: '32056'
last_name: Meschut
orcid: 0000-0002-2763-1246
citation:
ama: Otroshi M, Rossel M, Meschut G. Stress state dependent damage modeling of self-pierce
riveting process simulation using GISSMO damage model. Journal of Advanced
Joining Processes. 2020;1. doi:10.1016/j.jajp.2020.100015
apa: Otroshi, M., Rossel, M., & Meschut, G. (2020). Stress state dependent damage
modeling of self-pierce riveting process simulation using GISSMO damage model.
Journal of Advanced Joining Processes, 1. https://doi.org/10.1016/j.jajp.2020.100015
bibtex: '@article{Otroshi_Rossel_Meschut_2020, title={Stress state dependent damage
modeling of self-pierce riveting process simulation using GISSMO damage model},
volume={1}, DOI={10.1016/j.jajp.2020.100015},
journal={Journal of Advanced Joining Processes}, publisher={Elsevier}, author={Otroshi,
Mortaza and Rossel, Moritz and Meschut, Gerson}, year={2020} }'
chicago: Otroshi, Mortaza, Moritz Rossel, and Gerson Meschut. “Stress State Dependent
Damage Modeling of Self-Pierce Riveting Process Simulation Using GISSMO Damage
Model.” Journal of Advanced Joining Processes 1 (2020). https://doi.org/10.1016/j.jajp.2020.100015.
ieee: 'M. Otroshi, M. Rossel, and G. Meschut, “Stress state dependent damage modeling
of self-pierce riveting process simulation using GISSMO damage model,” Journal
of Advanced Joining Processes, vol. 1, 2020, doi: 10.1016/j.jajp.2020.100015.'
mla: Otroshi, Mortaza, et al. “Stress State Dependent Damage Modeling of Self-Pierce
Riveting Process Simulation Using GISSMO Damage Model.” Journal of Advanced
Joining Processes, vol. 1, Elsevier, 2020, doi:10.1016/j.jajp.2020.100015.
short: M. Otroshi, M. Rossel, G. Meschut, Journal of Advanced Joining Processes
1 (2020).
date_created: 2020-10-20T14:49:15Z
date_updated: 2022-04-25T07:49:50Z
department:
- _id: '157'
doi: 10.1016/j.jajp.2020.100015
intvolume: ' 1'
keyword:
- Self-pierce riveting
- Ductile fracture
- Damage modeling
- GISSMO damage model
language:
- iso: eng
main_file_link:
- open_access: '1'
oa: '1'
publication: Journal of Advanced Joining Processes
publisher: Elsevier
quality_controlled: '1'
status: public
title: Stress state dependent damage modeling of self-pierce riveting process simulation
using GISSMO damage model
type: journal_article
user_id: '71269'
volume: 1
year: '2020'
...
---
_id: '35298'
abstract:
- lang: ger
text: Im Artikel werden drei verschiedene Lernzugänge (kom-petenzorientiertes, ästhetisches und biographisches Lernen) vorgestellt und aus
theoretischer Perspektive deren motivierender Gehalt für selbstreguliertes Lernen
in Praxisphasen des Lehramtsstudiumsherausgearbeitet. Als theoretische Grund-lage
dient die Selbstbestimmungstheorie als zentrale motivationale Theorie zur Erklärung
selbstbestimmten Handelns.
- lang: eng
text: The article addresses how motivational learning approaches (competency-oriented, aesthetic and biographical) can contribute to the professionalization
of preservice teachers during a long-term internship. As a theoretical basis,
the self-determination theory serves as a central motivational theory for explaining
self-determined action.
alternative_title:
- Ein Blick auf kompetenzorientiertes, ästhetisches und biographisches Lernen im Lehramtsstudium
author:
- first_name: Carina
full_name: Caruso, Carina
id: '23123'
last_name: Caruso
- first_name: Christine
full_name: Adammek, Christine
last_name: Adammek
- first_name: Sabrina
full_name: Bonanati, Sabrina
last_name: Bonanati
- first_name: Sybille
full_name: Wiescholek, Sybille
last_name: Wiescholek
citation:
ama: Caruso C, Adammek C, Bonanati S, Wiescholek S. Motivierende Lernzugänge als
Ausgangspunkt der Professionalisierung angehender Lehrer_innen. Herausforderung
Lehrer*innenbildung - Zeitschrift Zur Konzeption, Gestaltung Und Diskussion.
2020;3(1):18-33. doi:10.4119/hlz-2540
apa: Caruso, C., Adammek, C., Bonanati, S., & Wiescholek, S. (2020). Motivierende
Lernzugänge als Ausgangspunkt der Professionalisierung angehender Lehrer_innen.
Herausforderung Lehrer*innenbildung - Zeitschrift Zur Konzeption, Gestaltung
Und Diskussion, 3(1), 18–33. https://doi.org/10.4119/hlz-2540
bibtex: '@article{Caruso_Adammek_Bonanati_Wiescholek_2020, title={Motivierende Lernzugänge
als Ausgangspunkt der Professionalisierung angehender Lehrer_innen}, volume={3},
DOI={10.4119/hlz-2540}, number={1},
journal={Herausforderung Lehrer*innenbildung - Zeitschrift Zur Konzeption, Gestaltung
Und Diskussion}, author={Caruso, Carina and Adammek, Christine and Bonanati, Sabrina
and Wiescholek, Sybille}, year={2020}, pages={18–33} }'
chicago: 'Caruso, Carina, Christine Adammek, Sabrina Bonanati, and Sybille Wiescholek.
“Motivierende Lernzugänge als Ausgangspunkt der Professionalisierung angehender
Lehrer_innen.” Herausforderung Lehrer*innenbildung - Zeitschrift Zur Konzeption,
Gestaltung Und Diskussion 3, no. 1 (2020): 18–33. https://doi.org/10.4119/hlz-2540.'
ieee: 'C. Caruso, C. Adammek, S. Bonanati, and S. Wiescholek, “Motivierende Lernzugänge
als Ausgangspunkt der Professionalisierung angehender Lehrer_innen,” Herausforderung
Lehrer*innenbildung - Zeitschrift Zur Konzeption, Gestaltung Und Diskussion,
vol. 3, no. 1, pp. 18–33, 2020, doi: 10.4119/hlz-2540.'
mla: Caruso, Carina, et al. “Motivierende Lernzugänge als Ausgangspunkt der Professionalisierung
angehender Lehrer_innen.” Herausforderung Lehrer*innenbildung - Zeitschrift
Zur Konzeption, Gestaltung Und Diskussion, vol. 3, no. 1, 2020, pp. 18–33,
doi:10.4119/hlz-2540.
short: C. Caruso, C. Adammek, S. Bonanati, S. Wiescholek, Herausforderung Lehrer*innenbildung
- Zeitschrift Zur Konzeption, Gestaltung Und Diskussion 3 (2020) 18–33.
date_created: 2023-01-05T13:58:28Z
date_updated: 2023-01-06T12:18:16Z
doi: 10.4119/hlz-2540
intvolume: ' 3'
issue: '1'
keyword:
- ästhetische Forschung
- Biographiearbeit
- Praxissemester
- Professionalisierung
- selbstreguliertes Lernen
- Motivation / aesthetic research
- biographical work
- long-term internship
- profes-sionalization
- self-regulated learning
- motivation
language:
- iso: other
page: 18-33
publication: Herausforderung Lehrer*innenbildung - Zeitschrift Zur Konzeption, Gestaltung
Und Diskussion
publication_identifier:
issn:
- 2625-0675
publication_status: published
status: public
title: Motivierende Lernzugänge als Ausgangspunkt der Professionalisierung angehender
Lehrer_innen
type: journal_article
user_id: '86519'
volume: 3
year: '2020'
...
---
_id: '48847'
abstract:
- lang: eng
text: Dynamic optimization problems have gained significant attention in evolutionary
computation as evolutionary algorithms (EAs) can easily adapt to changing environments.
We show that EAs can solve the graph coloring problem for bipartite graphs more
efficiently by using dynamic optimization. In our approach the graph instance
is given incrementally such that the EA can reoptimize its coloring when a new
edge introduces a conflict. We show that, when edges are inserted in a way that
preserves graph connectivity, Randomized Local Search (RLS) efficiently finds
a proper 2-coloring for all bipartite graphs. This includes graphs for which RLS
and other EAs need exponential expected time in a static optimization scenario.
We investigate different ways of building up the graph by popular graph traversals
such as breadth-first-search and depth-first-search and analyse the resulting
runtime behavior. We further show that offspring populations (e. g. a (1 + {$\lambda$})
RLS) lead to an exponential speedup in {$\lambda$}. Finally, an island model using
3 islands succeeds in an optimal time of {$\Theta$}(m) on every m-edge bipartite
graph, outperforming offspring populations. This is the first example where an
island model guarantees a speedup that is not bounded in the number of islands.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
- first_name: Pan
full_name: Peng, Pan
last_name: Peng
- first_name: Dirk
full_name: Sudholt, Dirk
last_name: Sudholt
citation:
ama: 'Bossek J, Neumann F, Peng P, Sudholt D. More Effective Randomized Search Heuristics
for Graph Coloring through Dynamic Optimization. In: Proceedings of the Genetic
and Evolutionary Computation Conference. GECCO ’20. Association for Computing
Machinery; 2020:1277–1285. doi:10.1145/3377930.3390174'
apa: Bossek, J., Neumann, F., Peng, P., & Sudholt, D. (2020). More Effective
Randomized Search Heuristics for Graph Coloring through Dynamic Optimization.
Proceedings of the Genetic and Evolutionary Computation Conference, 1277–1285.
https://doi.org/10.1145/3377930.3390174
bibtex: '@inproceedings{Bossek_Neumann_Peng_Sudholt_2020, place={New York, NY, USA},
series={GECCO ’20}, title={More Effective Randomized Search Heuristics for Graph
Coloring through Dynamic Optimization}, DOI={10.1145/3377930.3390174},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Bossek, Jakob and Neumann,
Frank and Peng, Pan and Sudholt, Dirk}, year={2020}, pages={1277–1285}, collection={GECCO
’20} }'
chicago: 'Bossek, Jakob, Frank Neumann, Pan Peng, and Dirk Sudholt. “More Effective
Randomized Search Heuristics for Graph Coloring through Dynamic Optimization.”
In Proceedings of the Genetic and Evolutionary Computation Conference,
1277–1285. GECCO ’20. New York, NY, USA: Association for Computing Machinery,
2020. https://doi.org/10.1145/3377930.3390174.'
ieee: 'J. Bossek, F. Neumann, P. Peng, and D. Sudholt, “More Effective Randomized
Search Heuristics for Graph Coloring through Dynamic Optimization,” in Proceedings
of the Genetic and Evolutionary Computation Conference, 2020, pp. 1277–1285,
doi: 10.1145/3377930.3390174.'
mla: Bossek, Jakob, et al. “More Effective Randomized Search Heuristics for Graph
Coloring through Dynamic Optimization.” Proceedings of the Genetic and Evolutionary
Computation Conference, Association for Computing Machinery, 2020, pp. 1277–1285,
doi:10.1145/3377930.3390174.
short: 'J. Bossek, F. Neumann, P. Peng, D. Sudholt, in: Proceedings of the Genetic
and Evolutionary Computation Conference, Association for Computing Machinery,
New York, NY, USA, 2020, pp. 1277–1285.'
date_created: 2023-11-14T15:58:53Z
date_updated: 2023-12-13T10:43:41Z
department:
- _id: '819'
doi: 10.1145/3377930.3390174
extern: '1'
keyword:
- dynamic optimization
- evolutionary algorithms
- running time analysis
- theory
language:
- iso: eng
page: 1277–1285
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-7128-5
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’20
status: public
title: More Effective Randomized Search Heuristics for Graph Coloring through Dynamic
Optimization
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '48849'
abstract:
- lang: eng
text: One-shot optimization tasks require to determine the set of solution candidates
prior to their evaluation, i.e., without possibility for adaptive sampling. We
consider two variants, classic one-shot optimization (where our aim is to find
at least one solution of high quality) and one-shot regression (where the goal
is to fit a model that resembles the true problem as well as possible). For both
tasks it seems intuitive that well-distributed samples should perform better than
uniform or grid-based samples, since they show a better coverage of the decision
space. In practice, quasi-random designs such as Latin Hypercube Samples and low-discrepancy
point sets are indeed very commonly used designs for one-shot optimization tasks.
We study in this work how well low star discrepancy correlates with performance
in one-shot optimization. Our results confirm an advantage of low-discrepancy
designs, but also indicate the correlation between discrepancy values and overall
performance is rather weak. We then demonstrate that commonly used designs may
be far from optimal. More precisely, we evolve 24 very specific designs that each
achieve good performance on one of our benchmark problems. Interestingly, we find
that these specifically designed samples yield surprisingly good performance across
the whole benchmark set. Our results therefore give strong indication that significant
performance gains over state-of-the-art one-shot sampling techniques are possible,
and that evolutionary algorithms can be an efficient means to evolve these.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Carola
full_name: Doerr, Carola
last_name: Doerr
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Aneta
full_name: Neumann, Aneta
last_name: Neumann
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
citation:
ama: 'Bossek J, Doerr C, Kerschke P, Neumann A, Neumann F. Evolving Sampling Strategies
for One-Shot Optimization Tasks. In: Parallel Problem Solving from Nature (PPSN
XVI). Springer-Verlag; 2020:111–124. doi:10.1007/978-3-030-58112-1_8'
apa: Bossek, J., Doerr, C., Kerschke, P., Neumann, A., & Neumann, F. (2020).
Evolving Sampling Strategies for One-Shot Optimization Tasks. Parallel Problem
Solving from Nature (PPSN XVI), 111–124. https://doi.org/10.1007/978-3-030-58112-1_8
bibtex: '@inproceedings{Bossek_Doerr_Kerschke_Neumann_Neumann_2020, place={Berlin,
Heidelberg}, title={Evolving Sampling Strategies for One-Shot Optimization Tasks},
DOI={10.1007/978-3-030-58112-1_8},
booktitle={Parallel Problem Solving from Nature (PPSN XVI)}, publisher={Springer-Verlag},
author={Bossek, Jakob and Doerr, Carola and Kerschke, Pascal and Neumann, Aneta
and Neumann, Frank}, year={2020}, pages={111–124} }'
chicago: 'Bossek, Jakob, Carola Doerr, Pascal Kerschke, Aneta Neumann, and Frank
Neumann. “Evolving Sampling Strategies for One-Shot Optimization Tasks.” In Parallel
Problem Solving from Nature (PPSN XVI), 111–124. Berlin, Heidelberg: Springer-Verlag,
2020. https://doi.org/10.1007/978-3-030-58112-1_8.'
ieee: 'J. Bossek, C. Doerr, P. Kerschke, A. Neumann, and F. Neumann, “Evolving Sampling
Strategies for One-Shot Optimization Tasks,” in Parallel Problem Solving from
Nature (PPSN XVI), 2020, pp. 111–124, doi: 10.1007/978-3-030-58112-1_8.'
mla: Bossek, Jakob, et al. “Evolving Sampling Strategies for One-Shot Optimization
Tasks.” Parallel Problem Solving from Nature (PPSN XVI), Springer-Verlag,
2020, pp. 111–124, doi:10.1007/978-3-030-58112-1_8.
short: 'J. Bossek, C. Doerr, P. Kerschke, A. Neumann, F. Neumann, in: Parallel Problem
Solving from Nature (PPSN XVI), Springer-Verlag, Berlin, Heidelberg, 2020, pp.
111–124.'
date_created: 2023-11-14T15:58:53Z
date_updated: 2023-12-13T10:43:53Z
department:
- _id: '819'
doi: 10.1007/978-3-030-58112-1_8
extern: '1'
keyword:
- Continuous optimization
- Fully parallel search
- One-shot optimization
- Regression
- Surrogate-assisted optimization
language:
- iso: eng
page: 111–124
place: Berlin, Heidelberg
publication: Parallel Problem Solving from Nature (PPSN XVI)
publication_identifier:
isbn:
- 978-3-030-58111-4
publication_status: published
publisher: Springer-Verlag
status: public
title: Evolving Sampling Strategies for One-Shot Optimization Tasks
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '48851'
abstract:
- lang: eng
text: Several important optimization problems in the area of vehicle routing can
be seen as variants of the classical Traveling Salesperson Problem (TSP). In the
area of evolutionary computation, the Traveling Thief Problem (TTP) has gained
increasing interest over the last 5 years. In this paper, we investigate the effect
of weights on such problems, in the sense that the cost of traveling increases
with respect to the weights of nodes already visited during a tour. This provides
abstractions of important TSP variants such as the Traveling Thief Problem and
time dependent TSP variants, and allows to study precisely the increase in difficulty
caused by weight dependence. We provide a 3.59-approximation for this weight dependent
version of TSP with metric distances and bounded positive weights. Furthermore,
we conduct experimental investigations for simple randomized local search with
classical mutation operators and two variants of the state-of-the-art evolutionary
algorithm EAX adapted to the weighted TSP. Our results show the impact of the
node weights on the position of the nodes in the resulting tour.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Katrin
full_name: Casel, Katrin
last_name: Casel
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
citation:
ama: 'Bossek J, Casel K, Kerschke P, Neumann F. The Node Weight Dependent Traveling
Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics.
In: Proceedings of the Genetic and Evolutionary Computation Conference.
GECCO ’20. Association for Computing Machinery; 2020:1286–1294. doi:10.1145/3377930.3390243'
apa: 'Bossek, J., Casel, K., Kerschke, P., & Neumann, F. (2020). The Node Weight
Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized
Search Heuristics. Proceedings of the Genetic and Evolutionary Computation
Conference, 1286–1294. https://doi.org/10.1145/3377930.3390243'
bibtex: '@inproceedings{Bossek_Casel_Kerschke_Neumann_2020, place={New York, NY,
USA}, series={GECCO ’20}, title={The Node Weight Dependent Traveling Salesperson
Problem: Approximation Algorithms and Randomized Search Heuristics}, DOI={10.1145/3377930.3390243},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Bossek, Jakob and Casel,
Katrin and Kerschke, Pascal and Neumann, Frank}, year={2020}, pages={1286–1294},
collection={GECCO ’20} }'
chicago: 'Bossek, Jakob, Katrin Casel, Pascal Kerschke, and Frank Neumann. “The
Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms
and Randomized Search Heuristics.” In Proceedings of the Genetic and Evolutionary
Computation Conference, 1286–1294. GECCO ’20. New York, NY, USA: Association
for Computing Machinery, 2020. https://doi.org/10.1145/3377930.3390243.'
ieee: 'J. Bossek, K. Casel, P. Kerschke, and F. Neumann, “The Node Weight Dependent
Traveling Salesperson Problem: Approximation Algorithms and Randomized Search
Heuristics,” in Proceedings of the Genetic and Evolutionary Computation Conference,
2020, pp. 1286–1294, doi: 10.1145/3377930.3390243.'
mla: 'Bossek, Jakob, et al. “The Node Weight Dependent Traveling Salesperson Problem:
Approximation Algorithms and Randomized Search Heuristics.” Proceedings of
the Genetic and Evolutionary Computation Conference, Association for Computing
Machinery, 2020, pp. 1286–1294, doi:10.1145/3377930.3390243.'
short: 'J. Bossek, K. Casel, P. Kerschke, F. Neumann, in: Proceedings of the Genetic
and Evolutionary Computation Conference, Association for Computing Machinery,
New York, NY, USA, 2020, pp. 1286–1294.'
date_created: 2023-11-14T15:58:53Z
date_updated: 2023-12-13T10:43:33Z
department:
- _id: '819'
doi: 10.1145/3377930.3390243
extern: '1'
keyword:
- dynamic optimization
- evolutionary algorithms
- running time analysis
- theory
language:
- iso: eng
page: 1286–1294
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-7128-5
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’20
status: public
title: 'The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms
and Randomized Search Heuristics'
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '48845'
abstract:
- lang: eng
text: In practice, e.g. in delivery and service scenarios, Vehicle-Routing-Problems
(VRPs) often imply repeated decision making on dynamic customer requests. As in
classical VRPs, tours have to be planned short while the number of serviced customers
has to be maximized at the same time resulting in a multi-objective problem. Beyond
that, however, dynamic requests lead to the need for re-planning of not yet realized
tour parts, while already realized tour parts are irreversible. In this paper
we study this type of bi-objective dynamic VRP including sequential decision making
and concurrent realization of decisions. We adopt a recently proposed Dynamic
Evolutionary Multi-Objective Algorithm (DEMOA) for a related VRP problem and extend
it to the more realistic (here considered) scenario of multiple vehicles. We empirically
show that our DEMOA is competitive with a multi-vehicle offline and clairvoyant
variant of the proposed DEMOA as well as with the dynamic single-vehicle approach
proposed earlier.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: 'Bossek J, Grimme C, Trautmann H. Dynamic Bi-Objective Routing of Multiple
Vehicles. In: Proceedings of the Genetic and Evolutionary Computation Conference.
GECCO ’20. Association for Computing Machinery; 2020:166–174. doi:10.1145/3377930.3390146'
apa: Bossek, J., Grimme, C., & Trautmann, H. (2020). Dynamic Bi-Objective Routing
of Multiple Vehicles. Proceedings of the Genetic and Evolutionary Computation
Conference, 166–174. https://doi.org/10.1145/3377930.3390146
bibtex: '@inproceedings{Bossek_Grimme_Trautmann_2020, place={New York, NY, USA},
series={GECCO ’20}, title={Dynamic Bi-Objective Routing of Multiple Vehicles},
DOI={10.1145/3377930.3390146},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Bossek, Jakob and Grimme,
Christian and Trautmann, Heike}, year={2020}, pages={166–174}, collection={GECCO
’20} }'
chicago: 'Bossek, Jakob, Christian Grimme, and Heike Trautmann. “Dynamic Bi-Objective
Routing of Multiple Vehicles.” In Proceedings of the Genetic and Evolutionary
Computation Conference, 166–174. GECCO ’20. New York, NY, USA: Association
for Computing Machinery, 2020. https://doi.org/10.1145/3377930.3390146.'
ieee: 'J. Bossek, C. Grimme, and H. Trautmann, “Dynamic Bi-Objective Routing of
Multiple Vehicles,” in Proceedings of the Genetic and Evolutionary Computation
Conference, 2020, pp. 166–174, doi: 10.1145/3377930.3390146.'
mla: Bossek, Jakob, et al. “Dynamic Bi-Objective Routing of Multiple Vehicles.”
Proceedings of the Genetic and Evolutionary Computation Conference, Association
for Computing Machinery, 2020, pp. 166–174, doi:10.1145/3377930.3390146.
short: 'J. Bossek, C. Grimme, H. Trautmann, in: Proceedings of the Genetic and Evolutionary
Computation Conference, Association for Computing Machinery, New York, NY, USA,
2020, pp. 166–174.'
date_created: 2023-11-14T15:58:52Z
date_updated: 2023-12-13T10:43:24Z
department:
- _id: '819'
doi: 10.1145/3377930.3390146
extern: '1'
keyword:
- decision making
- dynamic optimization
- evolutionary algorithms
- multi-objective optimization
- vehicle routing
language:
- iso: eng
page: 166–174
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-7128-5
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’20
status: public
title: Dynamic Bi-Objective Routing of Multiple Vehicles
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '48850'
abstract:
- lang: eng
text: Sequential model-based optimization (SMBO) approaches are algorithms for solving
problems that require computationally or otherwise expensive function evaluations.
The key design principle of SMBO is a substitution of the true objective function
by a surrogate, which is used to propose the point(s) to be evaluated next. SMBO
algorithms are intrinsically modular, leaving the user with many important design
choices. Significant research efforts go into understanding which settings perform
best for which type of problems. Most works, however, focus on the choice of the
model, the acquisition function, and the strategy used to optimize the latter.
The choice of the initial sampling strategy, however, receives much less attention.
Not surprisingly, quite diverging recommendations can be found in the literature.
We analyze in this work how the size and the distribution of the initial sample
influences the overall quality of the efficient global optimization (EGO) algorithm,
a well-known SMBO approach. While, overall, small initial budgets using Halton
sampling seem preferable, we also observe that the performance landscape is rather
unstructured. We furthermore identify several situations in which EGO performs
unfavorably against random sampling. Both observations indicate that an adaptive
SMBO design could be beneficial, making SMBO an interesting test-bed for automated
algorithm design.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Carola
full_name: Doerr, Carola
last_name: Doerr
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
citation:
ama: 'Bossek J, Doerr C, Kerschke P. Initial Design Strategies and Their Effects
on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB.
In: Proceedings of the Genetic and Evolutionary Computation Conference.
GECCO ’20. Association for Computing Machinery; 2020:778–786. doi:10.1145/3377930.3390155'
apa: 'Bossek, J., Doerr, C., & Kerschke, P. (2020). Initial Design Strategies
and Their Effects on Sequential Model-Based Optimization: An Exploratory Case
Study Based on BBOB. Proceedings of the Genetic and Evolutionary Computation
Conference, 778–786. https://doi.org/10.1145/3377930.3390155'
bibtex: '@inproceedings{Bossek_Doerr_Kerschke_2020, place={New York, NY, USA}, series={GECCO
’20}, title={Initial Design Strategies and Their Effects on Sequential Model-Based
Optimization: An Exploratory Case Study Based on BBOB}, DOI={10.1145/3377930.3390155},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Bossek, Jakob and Doerr,
Carola and Kerschke, Pascal}, year={2020}, pages={778–786}, collection={GECCO
’20} }'
chicago: 'Bossek, Jakob, Carola Doerr, and Pascal Kerschke. “Initial Design Strategies
and Their Effects on Sequential Model-Based Optimization: An Exploratory Case
Study Based on BBOB.” In Proceedings of the Genetic and Evolutionary Computation
Conference, 778–786. GECCO ’20. New York, NY, USA: Association for Computing
Machinery, 2020. https://doi.org/10.1145/3377930.3390155.'
ieee: 'J. Bossek, C. Doerr, and P. Kerschke, “Initial Design Strategies and Their
Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based
on BBOB,” in Proceedings of the Genetic and Evolutionary Computation Conference,
2020, pp. 778–786, doi: 10.1145/3377930.3390155.'
mla: 'Bossek, Jakob, et al. “Initial Design Strategies and Their Effects on Sequential
Model-Based Optimization: An Exploratory Case Study Based on BBOB.” Proceedings
of the Genetic and Evolutionary Computation Conference, Association for Computing
Machinery, 2020, pp. 778–786, doi:10.1145/3377930.3390155.'
short: 'J. Bossek, C. Doerr, P. Kerschke, in: Proceedings of the Genetic and Evolutionary
Computation Conference, Association for Computing Machinery, New York, NY, USA,
2020, pp. 778–786.'
date_created: 2023-11-14T15:58:53Z
date_updated: 2023-12-13T10:44:01Z
department:
- _id: '819'
doi: 10.1145/3377930.3390155
extern: '1'
keyword:
- continuous black-box optimization
- design of experiments
- initial design
- sequential model-based optimization
language:
- iso: eng
page: 778–786
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-7128-5
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’20
status: public
title: 'Initial Design Strategies and Their Effects on Sequential Model-Based Optimization:
An Exploratory Case Study Based on BBOB'
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '48848'
abstract:
- lang: eng
text: We build upon a recently proposed multi-objective view onto performance measurement
of single-objective stochastic solvers. The trade-off between the fraction of
failed runs and the mean runtime of successful runs \textendash both to be minimized
\textendash is directly analyzed based on a study on algorithm selection of inexact
state-of-the-art solvers for the famous Traveling Salesperson Problem (TSP). Moreover,
we adopt the hypervolume indicator (HV) commonly used in multi-objective optimization
for simultaneously assessing both conflicting objectives and investigate relations
to commonly used performance indicators, both theoretically and empirically. Next
to Penalized Average Runtime (PAR) and Penalized Quantile Runtime (PQR), the HV
measure is used as a core concept within the construction of per-instance algorithm
selection models offering interesting insights into complementary behavior of
inexact TSP solvers. \textbullet The multi-objective perspective is naturally
generalizable to multiple objectives. \textbullet Proof of relationship between
HV and the PAR in the considered bi-objective space. \textbullet New insights
into complementary behavior of stochastic optimization algorithms.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: Bossek J, Kerschke P, Trautmann H. A Multi-Objective Perspective on Performance
Assessment and Automated Selection of Single-Objective Optimization Algorithms.
Applied Soft Computing. 2020;88(C). doi:10.1016/j.asoc.2019.105901
apa: Bossek, J., Kerschke, P., & Trautmann, H. (2020). A Multi-Objective Perspective
on Performance Assessment and Automated Selection of Single-Objective Optimization
Algorithms. Applied Soft Computing, 88(C). https://doi.org/10.1016/j.asoc.2019.105901
bibtex: '@article{Bossek_Kerschke_Trautmann_2020, title={A Multi-Objective Perspective
on Performance Assessment and Automated Selection of Single-Objective Optimization
Algorithms}, volume={88}, DOI={10.1016/j.asoc.2019.105901},
number={C}, journal={Applied Soft Computing}, author={Bossek, Jakob and Kerschke,
Pascal and Trautmann, Heike}, year={2020} }'
chicago: Bossek, Jakob, Pascal Kerschke, and Heike Trautmann. “A Multi-Objective
Perspective on Performance Assessment and Automated Selection of Single-Objective
Optimization Algorithms.” Applied Soft Computing 88, no. C (2020). https://doi.org/10.1016/j.asoc.2019.105901.
ieee: 'J. Bossek, P. Kerschke, and H. Trautmann, “A Multi-Objective Perspective
on Performance Assessment and Automated Selection of Single-Objective Optimization
Algorithms,” Applied Soft Computing, vol. 88, no. C, 2020, doi: 10.1016/j.asoc.2019.105901.'
mla: Bossek, Jakob, et al. “A Multi-Objective Perspective on Performance Assessment
and Automated Selection of Single-Objective Optimization Algorithms.” Applied
Soft Computing, vol. 88, no. C, 2020, doi:10.1016/j.asoc.2019.105901.
short: J. Bossek, P. Kerschke, H. Trautmann, Applied Soft Computing 88 (2020).
date_created: 2023-11-14T15:58:53Z
date_updated: 2023-12-13T10:52:17Z
department:
- _id: '819'
doi: 10.1016/j.asoc.2019.105901
intvolume: ' 88'
issue: C
keyword:
- Algorithm selection
- Combinatorial optimization
- Multi-objective optimization
- Performance measurement
- Traveling Salesperson Problem
language:
- iso: eng
publication: Applied Soft Computing
publication_identifier:
issn:
- 1568-4946
status: public
title: A Multi-Objective Perspective on Performance Assessment and Automated Selection
of Single-Objective Optimization Algorithms
type: journal_article
user_id: '102979'
volume: 88
year: '2020'
...
---
_id: '46334'
abstract:
- lang: eng
text: We build upon a recently proposed multi-objective view onto performance measurement
of single-objective stochastic solvers. The trade-off between the fraction of
failed runs and the mean runtime of successful runs – both to be minimized – is
directly analyzed based on a study on algorithm selection of inexact state-of-the-art
solvers for the famous Traveling Salesperson Problem (TSP). Moreover, we adopt
the hypervolume indicator (HV) commonly used in multi-objective optimization for
simultaneously assessing both conflicting objectives and investigate relations
to commonly used performance indicators, both theoretically and empirically. Next
to Penalized Average Runtime (PAR) and Penalized Quantile Runtime (PQR), the HV
measure is used as a core concept within the construction of per-instance algorithm
selection models offering interesting insights into complementary behavior of
inexact TSP solvers.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: Bossek J, Kerschke P, Trautmann H. A multi-objective perspective on performance
assessment and automated selection of single-objective optimization algorithms.
Applied Soft Computing. 2020;88:105901. doi:https://doi.org/10.1016/j.asoc.2019.105901
apa: Bossek, J., Kerschke, P., & Trautmann, H. (2020). A multi-objective perspective
on performance assessment and automated selection of single-objective optimization
algorithms. Applied Soft Computing, 88, 105901. https://doi.org/10.1016/j.asoc.2019.105901
bibtex: '@article{Bossek_Kerschke_Trautmann_2020, title={A multi-objective perspective
on performance assessment and automated selection of single-objective optimization
algorithms}, volume={88}, DOI={https://doi.org/10.1016/j.asoc.2019.105901},
journal={Applied Soft Computing}, author={Bossek, Jakob and Kerschke, Pascal and
Trautmann, Heike}, year={2020}, pages={105901} }'
chicago: 'Bossek, Jakob, Pascal Kerschke, and Heike Trautmann. “A Multi-Objective
Perspective on Performance Assessment and Automated Selection of Single-Objective
Optimization Algorithms.” Applied Soft Computing 88 (2020): 105901. https://doi.org/10.1016/j.asoc.2019.105901.'
ieee: 'J. Bossek, P. Kerschke, and H. Trautmann, “A multi-objective perspective
on performance assessment and automated selection of single-objective optimization
algorithms,” Applied Soft Computing, vol. 88, p. 105901, 2020, doi: https://doi.org/10.1016/j.asoc.2019.105901.'
mla: Bossek, Jakob, et al. “A Multi-Objective Perspective on Performance Assessment
and Automated Selection of Single-Objective Optimization Algorithms.” Applied
Soft Computing, vol. 88, 2020, p. 105901, doi:https://doi.org/10.1016/j.asoc.2019.105901.
short: J. Bossek, P. Kerschke, H. Trautmann, Applied Soft Computing 88 (2020) 105901.
date_created: 2023-08-04T07:42:26Z
date_updated: 2024-06-10T12:00:46Z
department:
- _id: '34'
- _id: '819'
doi: https://doi.org/10.1016/j.asoc.2019.105901
intvolume: ' 88'
keyword:
- Algorithm selection
- Multi-objective optimization
- Performance measurement
- Combinatorial optimization
- Traveling Salesperson Problem
language:
- iso: eng
page: '105901'
publication: Applied Soft Computing
publication_identifier:
issn:
- 1568-4946
status: public
title: A multi-objective perspective on performance assessment and automated selection
of single-objective optimization algorithms
type: journal_article
user_id: '15504'
volume: 88
year: '2020'
...