@article{63053, author = {{Hernández, Carlos and Rodriguez-Fernandez, Angel E. and Schäpermeier, Lennart and Cuate, Oliver and Trautmann, Heike and Schütze, Oliver}}, journal = {{IEEE Transactions on Evolutionary Computation}}, keywords = {{Optimization, Evolutionary computation, Hands, Proposals, Convergence, Computational efficiency, Artificial intelligence, Accuracy, Approximation algorithms, Aerospace electronics, Multi-objective optimization, evolutionary algorithms, nearly optimal solutions, multimodal optimization, archiving, continuation}}, pages = {{1--1}}, title = {{{An Evolutionary Approach for the Computation of ∈-Locally Optimal Solutions for Multi-Objective Multimodal Optimization}}}, doi = {{10.1109/TEVC.2025.3637276}}, year = {{2025}}, } @article{56221, author = {{Rodriguez-Fernandez, Angel E. and Schäpermeier, Lennart and Hernández, Carlos and Kerschke, Pascal and Trautmann, Heike and Schütze, Oliver}}, journal = {{IEEE Transactions on Evolutionary Computation}}, keywords = {{Optimization, Evolutionary computation, Approximation algorithms, Benchmark testing, Vectors, Surveys, Pareto optimization, multi-objective optimization, evolutionary computation, multimodal optimization, local solutions}}, pages = {{1--1}}, title = {{{Finding ϵ-Locally Optimal Solutions for Multi-Objective Multimodal Optimization}}}, doi = {{10.1109/TEVC.2024.3458855}}, year = {{2024}}, } @inproceedings{48882, abstract = {{In multimodal multi-objective optimization (MMMOO), the focus is not solely on convergence in objective space, but rather also on explicitly ensuring diversity in decision space. We illustrate why commonly used diversity measures are not entirely appropriate for this task and propose a sophisticated basin-based evaluation (BBE) method. Also, BBE variants are developed, capturing the anytime behavior of algorithms. The set of BBE measures is tested by means of an algorithm configuration study. We show that these new measures also transfer properties of the well-established hypervolume (HV) indicator to the domain of MMMOO, thus also accounting for objective space convergence. Moreover, we advance MMMOO research by providing insights into the multimodal performance of the considered algorithms. Specifically, algorithms exploiting local structures are shown to outperform classical evolutionary multi-objective optimizers regarding the BBE variants and respective trade-off with HV.}}, author = {{Heins, Jonathan and Rook, Jeroen and Schäpermeier, Lennart and Kerschke, Pascal and Bossek, Jakob and Trautmann, Heike}}, booktitle = {{Parallel Problem Solving from Nature (PPSN XVII)}}, editor = {{Rudolph, Günter and Kononova, Anna V. and Aguirre, Hernán and Kerschke, Pascal and Ochoa, Gabriela and Tusar, Tea}}, isbn = {{978-3-031-14714-2}}, keywords = {{Anytime behavior, Benchmarking, Continuous optimization, Multi-objective optimization, Multimodality, Performance metric}}, pages = {{192–206}}, publisher = {{Springer International Publishing}}, title = {{{BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems}}}, doi = {{10.1007/978-3-031-14714-2_14}}, year = {{2022}}, } @inproceedings{48896, abstract = {{Hardness of Multi-Objective (MO) continuous optimization problems results from an interplay of various problem characteristics, e. g. the degree of multi-modality. We present a benchmark study of classical and diversity focused optimizers on multi-modal MO problems based on automated algorithm configuration. We show the large effect of the latter and investigate the trade-off between convergence in objective space and diversity in decision space.}}, author = {{Rook, Jeroen and Trautmann, Heike and Bossek, Jakob and Grimme, Christian}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference Companion}}, isbn = {{978-1-4503-9268-6}}, keywords = {{configuration, multi-modality, multi-objective optimization}}, pages = {{356–359}}, publisher = {{Association for Computing Machinery}}, title = {{{On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems}}}, doi = {{10.1145/3520304.3528998}}, year = {{2022}}, } @inproceedings{31066, abstract = {{While trade-offs between modeling effort and model accuracy remain a major concern with system identification, resorting to data-driven methods often leads to a complete disregard for physical plausibility. To address this issue, we propose a physics-guided hybrid approach for modeling non-autonomous systems under control. Starting from a traditional physics-based model, this is extended by a recurrent neural network and trained using a sophisticated multi-objective strategy yielding physically plausible models. While purely data-driven methods fail to produce satisfying results, experiments conducted on real data reveal substantial accuracy improvements by our approach compared to a physics-based model. }}, author = {{Schön, Oliver and Götte, Ricarda-Samantha and Timmermann, Julia}}, booktitle = {{14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)}}, keywords = {{neural networks, physics-guided, data-driven, multi-objective optimization, system identification, machine learning, dynamical systems}}, location = {{Casablanca, Morocco}}, number = {{12}}, pages = {{19--24}}, title = {{{Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems}}}, doi = {{https://doi.org/10.1016/j.ifacol.2022.07.282}}, volume = {{55}}, year = {{2022}}, } @inproceedings{48845, abstract = {{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 = {{Bossek, Jakob and Grimme, Christian and Trautmann, Heike}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-7128-5}}, keywords = {{decision making, dynamic optimization, evolutionary algorithms, multi-objective optimization, vehicle routing}}, pages = {{166–174}}, publisher = {{Association for Computing Machinery}}, title = {{{Dynamic Bi-Objective Routing of Multiple Vehicles}}}, doi = {{10.1145/3377930.3390146}}, year = {{2020}}, } @article{48848, abstract = {{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 = {{Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}}, issn = {{1568-4946}}, journal = {{Applied Soft Computing}}, keywords = {{Algorithm selection, Combinatorial optimization, Multi-objective optimization, Performance measurement, Traveling Salesperson Problem}}, number = {{C}}, title = {{{A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms}}}, doi = {{10.1016/j.asoc.2019.105901}}, volume = {{88}}, year = {{2020}}, } @article{46334, abstract = {{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 = {{Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}}, issn = {{1568-4946}}, journal = {{Applied Soft Computing}}, keywords = {{Algorithm selection, Multi-objective optimization, Performance measurement, Combinatorial optimization, Traveling Salesperson Problem}}, pages = {{105901}}, title = {{{A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms}}}, doi = {{https://doi.org/10.1016/j.asoc.2019.105901}}, volume = {{88}}, year = {{2020}}, } @inproceedings{48841, abstract = {{We tackle a bi-objective dynamic orienteering problem where customer requests arise as time passes by. The goal is to minimize the tour length traveled by a single delivery vehicle while simultaneously keeping the number of dismissed dynamic customers to a minimum. We propose a dynamic Evolutionary Multi-Objective Algorithm which is grounded on insights gained from a previous series of work on an a-posteriori version of the problem, where all request times are known in advance. In our experiments, we simulate different decision maker strategies and evaluate the development of the Pareto-front approximations on exemplary problem instances. It turns out, that despite severely reduced computational budget and no oracle-knowledge of request times the dynamic EMOA is capable of producing approximations which partially dominate the results of the a-posteriori EMOA and dynamic integer linear programming strategies.}}, author = {{Bossek, Jakob and Grimme, Christian and Meisel, Stephan and Rudolph, Günter and Trautmann, Heike}}, booktitle = {{Evolutionary Multi-Criterion Optimization (EMO)}}, editor = {{Deb, Kalyanmoy and Goodman, Erik and Coello Coello, Carlos A. and Klamroth, Kathrin and Miettinen, Kaisa and Mostaghim, Sanaz and Reed, Patrick}}, isbn = {{978-3-030-12598-1}}, keywords = {{Combinatorial optimization, Dynamic optimization, Metaheuristics, Multi-objective optimization, Vehicle routing}}, pages = {{516–528}}, publisher = {{Springer International Publishing}}, title = {{{Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm}}}, doi = {{10.1007/978-3-030-12598-1_41}}, year = {{2019}}, } @inproceedings{48840, abstract = {{Research has shown that for many single-objective graph problems where optimum solutions are composed of low weight sub-graphs, such as the minimum spanning tree problem (MST), mutation operators favoring low weight edges show superior performance. Intuitively, similar observations should hold for multi-criteria variants of such problems. In this work, we focus on the multi-criteria MST problem. A thorough experimental study is conducted where we estimate the probability of edges being part of non-dominated spanning trees as a function of the edges’ non-domination level or domination count, respectively. Building on gained insights, we propose several biased one-edge-exchange mutation operators that differ in the used edge-selection probability distribution (biased towards edges of low rank). Our empirical analysis shows that among different graph types (dense and sparse) and edge weight types (both uniformly random and combinations of Euclidean and uniformly random) biased edge-selection strategies perform superior in contrast to the baseline uniform edge-selection. Our findings are in particular strong for dense graphs.}}, author = {{Bossek, Jakob and Grimme, Christian and Neumann, Frank}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-6111-8}}, keywords = {{biased mutation, combinatorial optimization, minimum spanning tree, multi-objective optimization}}, pages = {{516–523}}, publisher = {{Association for Computing Machinery}}, title = {{{On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem}}}, doi = {{10.1145/3321707.3321818}}, year = {{2019}}, } @inproceedings{9999, abstract = {{Ultrasonic wire bonding is an indispensable process in the industrial manufacturing of semiconductor devices. Copper wire is increasingly replacing the well-established aluminium wire because of its superior electrical, thermal and mechanical properties. Copper wire processes differ significantly from aluminium processes and are more sensitive to disturbances, which reduces the range of parameter values suitable for a stable process. Disturbances can be compensated by an adaption of process parameters, but finding suitable parameters manually is difficult and time-consuming. This paper presents a physical model of the ultrasonic wire bonding process including the friction contact between tool and wire. This model yields novel insights into the process. A prototype of a multi-objective optimizing bonding machine (MOBM) is presented. It uses multi-objective optimization, based on the complete process model, to automatically select the best operating point as a compromise of concurrent objectives.}}, author = {{Unger, Andreas and Hunstig, Matthias and Meyer, Tobias and Brökelmann, Michael and Sextro, Walter}}, booktitle = {{In Proceedings of IMAPS 2018 – 51st Symposium on Microelectronics, Pasadena, CA, 2018}}, keywords = {{wire bonding, multi-objective optimization, process model, copper wire, self-optimization}}, title = {{{Intelligent Production of Wire Bonds using Multi-Objective Optimization – Insights, Opportunities and Challenges}}}, doi = {{10.4071/2380-4505-2018.1.000572}}, volume = {{Vol. 2018, No. 1, pp. 000572-000577.}}, year = {{2018}}, } @inproceedings{48839, abstract = {{We analyze the effects of including local search techniques into a multi-objective evolutionary algorithm for solving a bi-objective orienteering problem with a single vehicle while the two conflicting objectives are minimization of travel time and maximization of the number of visited customer locations. Experiments are based on a large set of specifically designed problem instances with different characteristics and it is shown that local search techniques focusing on one of the objectives only improve the performance of the evolutionary algorithm in terms of both objectives. The analysis also shows that local search techniques are capable of sending locally optimal solutions to foremost fronts of the multi-objective optimization process, and that these solutions then become the leading factors of the evolutionary process.}}, author = {{Bossek, Jakob and Grimme, Christian and Meisel, Stephan and Rudolph, Günter and Trautmann, Heike}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-5618-3}}, keywords = {{combinatorial optimization, metaheuristics, multi-objective optimization, orienteering, transportation}}, pages = {{585–592}}, publisher = {{Association for Computing Machinery}}, title = {{{Local Search Effects in Bi-Objective Orienteering}}}, doi = {{10.1145/3205455.3205548}}, year = {{2018}}, } @inproceedings{29973, abstract = {{Haushaltsgeräte aus der Klasse der "Weißen Ware" tragen mit etwa einem Drittel ($34,2%$ \citeBDEW2013) zum privaten Energieverbrauch bei. Diese Veröffentlichung präsentiert eine Struktur und die dafür notwendige optimale Betriebsstrategie für Weiße Ware in einer Umgebung mit Strompreisen, die wegen der Volatilität der Regenerativen Energien stark fluktuieren. Das vorgeschlagene Konzept nutzt dafür ein dezentrales Energiemanagementsystem, das über drei Hierarchieebenen verteilt ist: die Geräteebene, die Haushaltsebene und die Ortsnetzebene. Auf der Geräteebene nutzt dieses Konzept zusätzlich Betriebsflexibilitäten der Haushaltsgeräte aus.}}, author = {{Stille, Karl Stephan Christian and Böcker, Joachim and Bettentrup, Ralf and Kaiser, Ingo}}, booktitle = {{ETG-Fachtagung "Von Smart Grids zu Smart Markets"}}, keywords = {{Energy management, hybrid energy storage system, self-optimization, multi-objective optimization, adaptive systems, pareto set, SFB614-D1, SFB614-D2, LEA-Publikation, Eigene}}, publisher = {{VDE}}, title = {{{Hierarchisches Optimierungskonzept für die Laststeuerung von Haushaltsgeräten}}}, year = {{2015}}, } @inproceedings{48887, abstract = {{We evaluate the performance of a multi-objective evolutionary algorithm on a class of dynamic routing problems with a single vehicle. In particular we focus on relating algorithmic performance to the most prominent characteristics of problem instances. The routing problem considers two types of customers: mandatory customers must be visited whereas optional customers do not necessarily have to be visited. Moreover, mandatory customers are known prior to the start of the tour whereas optional customers request for service at later points in time with the vehicle already being on its way. The multi-objective optimization problem then results as maximizing the number of visited customers while simultaneously minimizing total travel time. As an a-posteriori evaluation tool, the evolutionary algorithm aims at approximating the related Pareto set for specifically designed benchmarking instances differing in terms of number of customers, geographical layout, fraction of mandatory customers, and request times of optional customers. Conceptional and experimental comparisons to online heuristic procedures are provided.}}, author = {{Meisel, Stephan and Grimme, Christian and Bossek, Jakob and Wölck, Martin and Rudolph, Günter and Trautmann, Heike}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference }}, isbn = {{978-1-4503-3472-3}}, keywords = {{combinatorial optimization, metaheuristics, multi-objective optimization, online algorithms, transportation}}, pages = {{425–432}}, publisher = {{Association for Computing Machinery}}, title = {{{Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle}}}, doi = {{10.1145/2739480.2754705}}, year = {{2015}}, }