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Combining convergence and diversity in evolutionary multiobjective optimization.
Journal article

Combining convergence and diversity in evolutionary multiobjective optimization.

  • Laumanns M Department of Information Technology and Electrical Engineering, Swiss Federal Institute of Technology Zurich, 8092 Zurich, Switzerland. laumanns@tik.ee.ethz.ch
  • Thiele L
  • Deb K
  • Zitzler E
  • 2002-09-14
Published in:
  • Evolutionary computation. - 2002
Algorithms
Biological Evolution
Computer Simulation
Genetic Variation
Models, Biological
English Over the past few years, the research on evolutionary algorithms has demonstrated their niche in solving multiobjective optimization problems, where the goal is to find a number of Pareto-optimal solutions in a single simulation run. Many studies have depicted different ways evolutionary algorithms can progress towards the Pareto-optimal set with a widely spread distribution of solutions. However, none of the multiobjective evolutionary algorithms (MOEAs) has a proof of convergence to the true Pareto-optimal solutions with a wide diversity among the solutions. In this paper, we discuss why a number of earlier MOEAs do not have such properties. Based on the concept of epsilon-dominance, new archiving strategies are proposed that overcome this fundamental problem and provably lead to MOEAs that have both the desired convergence and distribution properties. A number of modifications to the baseline algorithm are also suggested. The concept of epsilon-dominance introduced in this paper is practical and should make the proposed algorithms useful to researchers and practitioners alike.
Language
  • English
Open access status
closed
Identifiers
Persistent URL
https://sonar.ch/global/documents/213402
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