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Combining convergence and diversity in evolutionary multi-objective optimization (2002)
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| Venue: | Evolutionary Computation |
| Citations: | 159 - 15 self |
BibTeX
@ARTICLE{Laumanns02combiningconvergence,
author = {Marco Laumanns and Lothar Thiele and Kalyanmoy Deb and Eckart Zitzler},
title = {Combining convergence and diversity in evolutionary multi-objective optimization},
journal = {Evolutionary Computation},
year = {2002},
volume = {10},
pages = {2002}
}
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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 �nd a number of Pareto-optimal solutions in a single simulation run. Many studies have depicted different ways evolutionary algorithms can progress towards the Paretooptimal 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-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 modi�cations to the baseline algorithm are also suggested. The concept of-dominance introduced in this paper is practical and should make the proposed algorithms useful to researchers and practitioners alike.
Keyphrases
evolutionary multi-objective optimization concept of-dominance many study true pareto-optimal solution pareto-optimal solution spread distribution paretooptimal set wide diversity multiobjective optimization problem modi cation single simulation run baseline algorithm different way evolutionary algorithm past year multiobjective evolutionary algorithm distribution property fundamental problem evolutionary algorithm
