@inproceedings{46408,
  abstract     = {{The integration of experts’ preferences is an important aspect in multi-objective optimization. Usually, one out of a set of Pareto optimal solutions has to be chosen based on expert knowledge. A combination of multi-objective particle swarm optimization (MOPSO) with the desirability concept is introduced to efficiently focus on desired and relevant regions of the true Pareto front of the optimization problem which facilitates the solution selection process. Desirability functions of the objectives are optimized, and the desirability index is used for selecting the global best particle in each iteration. The resulting MOPSO variant DF-MOPSO in most cases exclusively generates solutions in the desired area of the Pareto front. Approximations of the whole Pareto front result in cases of misspecified desired regions.}},
  author       = {{Mostaghim, Sanaz and Trautmann, Heike and Mersmann, Olaf}},
  booktitle    = {{Parallel Problem Solving from Nature, PPSN XI}},
  editor       = {{Schaefer, Robert and Cotta, Carlos and Kołodziej, Joanna and Rudolph, Günter}},
  isbn         = {{978-3-642-15871-1}},
  pages        = {{101–110}},
  publisher    = {{Springer Berlin Heidelberg}},
  title        = {{{Preference-Based Multi-Objective Particle Swarm Optimization Using Desirabilities}}},
  doi          = {{https://doi.org/10.1007/978-3-642-15871-1_11}},
  year         = {{2010}},
}

@inproceedings{46409,
  abstract     = {{Since many real-world optimization problems are noisy, vector optimization algorithms that can cope with noise and uncertainty are required. We propose new, robust selection strategies for evolutionary multi-objective optimization in the presence of noise. We apply new measures of uncertainty for estimating the recently introduced Pareto-dominance for uncertain and noisy environments (PDU). The first measure is the inter-quartile range of the outcomes of repeated function evaluations. The second is based on axis-aligned bounding boxes around the upper and lower quantiles of the sampled fitness values in objective space. Experiments on real and artificial problems show promising results.}},
  author       = {{Voß, Thomas and Trautmann, Heike and Igel, Christian}},
  booktitle    = {{Parallel Problem Solving from Nature, PPSN XI}},
  editor       = {{Schaefer, Robert and Cotta, Carlos and Kołodziej, Joanna and Rudolph, Günter}},
  isbn         = {{978-3-642-15871-1}},
  pages        = {{260–269}},
  publisher    = {{Springer Berlin Heidelberg}},
  title        = {{{New Uncertainty Handling Strategies in Multi-objective Evolutionary Optimization}}},
  doi          = {{https://doi.org/10.1007/978-3-642-15871-1_27}},
  year         = {{2010}},
}