Stopping Criteria for Multimodal Optimization

  • Simon Wessing
  • Mike Preuss
  • Heike Trautmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8672)


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.


Multimodal optimization global optimization multiobjective selection convergence detection stopping criteria 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Simon Wessing
    • 1
  • Mike Preuss
    • 2
  • Heike Trautmann
    • 2
  1. 1.Department of Computer ScienceTU DortmundGermany
  2. 2.Information Systems and Statistics GroupUniversity of MünsterGermany

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