Computational Optimization and Applications

, Volume 63, Issue 3, pp 875–902 | Cite as

On multiobjective selection for multimodal optimization

Article

Abstract

Multiobjective selection operators are a popular and straightforward tool for preserving diversity in evolutionary optimization algorithms. One application area where diversity is essential is multimodal optimization with its goal of finding a diverse set of either globally or locally optimal solutions of a single-objective problem. We therefore investigate multiobjective selection methods that identify good quality and diverse solutions from a larger set of candidates. Simultaneously, unary quality indicators from multiobjective optimization also turn out to be useful for multimodal optimization. We focus on experimentally detecting the best selection operators and indicators in two different contexts, namely a one-time subset selection and an iterative application in optimization. Experimental results indicate that certain design decisions generally have advantageous tendencies regarding run time and quality. One such positive example is using a concept of nearest better neighbors instead of the common nearest-neighbor distances.

Keywords

Multimodal optimization Multiobjectivization Nearest neighbor Selection Quality indicator Benchmarking 

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Chair of Algorithm Engineering, Department of Computer ScienceTU DortmundDortmundGermany
  2. 2.European Research Center for Information Systems (ERCIS), WWU MünsterMünsterGermany

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