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Approximating the nondominated front using the Pareto Archived Evolution Strategy (2000)
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| Venue: | EVOLUTIONARY COMPUTATION |
| Citations: | 321 - 19 self |
BibTeX
@ARTICLE{Knowles00approximatingthe,
author = {Joshua D. Knowles and David W. Corne},
title = {Approximating the nondominated front using the Pareto Archived Evolution Strategy},
journal = {EVOLUTIONARY COMPUTATION},
year = {2000},
volume = {8},
number = {2},
pages = {149--172}
}
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Abstract
We introduce a simple evolution scheme for multiobjective optimization problems, called the Pareto Archived Evolution Strategy (PAES). We argue that PAES may represent the simplest possible nontrivial algorithm capable of generating diverse solutions in the Pareto optimal set. The algorithm, in its simplest form, is a (1 + 1) evolution strategy employing local search but using a reference archive of previously found solutions in order to identify the approximate dominance ranking of the current and candidate solution vectors. (1 + 1)-PAES is intended to be a baseline approach against which more involved methods may be compared. It may also serve well in some real-world applications when local search seems superior to or competitive with population-based methods. We introduce (1 + λ) and (μ | λ) variants of PAES as extensions to the basic algorithm. Six variants of PAES are compared to variants of the Niched Pareto Genetic Algorithm and the Nondominated Sorting Genetic Algorithm over a diverse suite of six test functions. Results are analyzed and presented using techniques that reduce the attainment surfaces generated from several optimization runs into a set of univariate distributions. This allows standard statistical analysis to be carried out for comparative purposes. Our results provide strong evidence that PAES performs consistently well on a range of multiobjective optimization tasks.
Keyphrases
pareto archived evolution strategy nondominated front local search baseline approach pareto optimal set reference archive test function strong evidence nondominated sorting genetic algorithm diverse solution standard statistical analysis evolution strategy multiobjective optimization task diverse suite several optimization basic algorithm simple evolution scheme population-based method comparative purpose niched pareto genetic algorithm candidate solution vector multiobjective optimization problem approximate dominance ranking possible nontrivial algorithm involved method real-world application univariate distribution attainment surface
