Stopping Criteria for Multimodal Optimization

Wessing S, Preuss M, Trautmann H


Abstract
Multimodal optimization requires maintenance of a good search space coverage and approximation of several optima at the same time. We analyze two constitutive optimization algorithms and show that in many cases, a phase transition occurs at some point, so that either diversity collapses or optimization stagnates. But how to derive suitable stopping criteria for multimodal optimization? Experimental results indicate that an algorithm’s population contains sufficient information to estimate the point in time when several performance indicators reach their optimum. Thus, stopping criteria are formulated based on summary characteristics employing objective values and mutation strength.

Keywords
Multimodal optimization; global optimization; multiobjective selection; convergence detection; stopping criteria



Publication type
Conference Paper

Peer reviewed
Yes

Publication status
Published

Year
2014

Conference
Parallel Problem Solving from Nature - PPSN XIII

Venue
Ljubljana, Slovenia

Editor
Bartz-Beielstein T, Branke J, Filipic B, Smith J

Pages range
141-150

Volume
8672

Title of series
Lecture Notes in Computer Science

Publisher
Springer

Language
English

DOI

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