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Evolutionary Algorithms for Multiobjective Optimization (2002)
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BibTeX
@MISC{Zitzler02evolutionaryalgorithms,
author = {Eckart Zitzler},
title = {Evolutionary Algorithms for Multiobjective Optimization},
year = {2002}
}
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Abstract
Multiple, often conflicting objectives arise naturally in most real-world optimization scenarios. As evolutionary algorithms possess several characteristics due to which they are well suited to this type of problem, evolution-based methods have been used for multiobjective optimization for more than a decade. Meanwhile evolutionary multiobjective optimization has become established as a separate subdiscipline combining the fields of evolutionary computation and classical multiple criteria decision making. In this paper, the basic principles of evolutionary multiobjective optimization are discussed from an algorithm design perspective. The focus is on the major issues such as fitness assignment, diversity preservation, and elitism in general rather than on particular algorithms. Different techniques to implement these strongly related concepts will be discussed, and further important aspects such as constraint handling and preference articulation are treated as well. Finally, two applications will presented and some recent trends in the field will be outlined.
Keyphrases
evolutionary algorithm multiobjective optimization evolutionary multiobjective optimization multiobjective optimization classical multiple criterion decision making important aspect fitness assignment real-world optimization scenario different technique diversity preservation particular algorithm preference articulation basic principle algorithm design perspective recent trend evolutionary computation evolutionary algorithm separate subdiscipline evolution-based method constraint handling evolutionary algorithm posse several characteristic key word major issue
