Self-adaptation for multi-objective evolutionary algorithms

  • Authors:

  • Dirk Büche;Sibylle Müller;Petros Koumoutsakos

  • Affiliations:

  • Institute of Computational Science, Swiss Federal Institute of Technology, Zürich, Switzerland;Institute of Computational Science, Swiss Federal Institute of Technology, Zürich, Switzerland;Institute of Computational Science, Swiss Federal Institute of Technology, Zürich, Switzerland

  • Venue:
  • EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
  • Year:
  • 2003

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Abstract

Evolutionary Algorithms are a standard tool for multi-objective optimization that are able to approximate the Pareto front in a single optimization run. However, for some selection operators, the algorithm stagnates at a certain distance from the Pareto front without convergence for further iterations. We analyze this observation for different multi-objective selection operators. We derive a simple analytical estimate of the stagnation distance for several selection operators, that use the dominance criterion for the fitness assignment. Two of the examined operators are shown to converge with arbitrary precision to the Pareto front. We exploit this property and propose a novel algorithm to increase their convergence speed by introducing suitable self-adaptive mutation. This adaptive mutation takes into account the distance to the Pareto front. All algorithms are analyzed on a 2- and 3-objective test function.