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PubMed · 12227996

Combining convergence and diversity in evolutionary multiobjective optimization.

Marco Laumanns·Lothar Thiele·Kalyanmoy Deb·Eckart Zitzler
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Abstract

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.

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AlgorithmsBiological EvolutionComputer SimulationGenetic VariationModels, BiologicalJournal ArticleResearch Support, Non-U.S. Gov't

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Marco Laumanns, Lothar Thiele, Kalyanmoy Deb, Eckart Zitzler. 2002. Combining convergence and diversity in evolutionary multiobjective optimization.. https://doi.org/10.1162/106365602760234108

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