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A Niched Pareto Genetic Algorithm for Multiobjective Optimization (1994)
| Venue: | IN PROCEEDINGS OF THE FIRST IEEE CONFERENCE ON EVOLUTIONARY COMPUTATION, IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE |
| Citations: | 392 - 6 self |
BibTeX
@INPROCEEDINGS{Horn94aniched,
author = {Jeffrey Horn and Nicholas Nafpliotis and David E. Goldberg},
title = {A Niched Pareto Genetic Algorithm for Multiobjective Optimization},
booktitle = {IN PROCEEDINGS OF THE FIRST IEEE CONFERENCE ON EVOLUTIONARY COMPUTATION, IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE},
year = {1994},
pages = {82--87},
publisher = {}
}
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Abstract
Many, if not most, optimization problems have multiple objectives. Historically, multiple objectives have been combined ad hoc to form a scalar objective function, usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives into constraints. The genetic algorithm (GA), however, is readily modified to deal with multiple objectives by incorporating the concept of Pareto domination in its selection operator, and applying a niching pressure to spread its population out along the Pareto optimal tradeoff surface. We introduce the Niched Pareto GA as an algorithm for finding the Pareto optimal set. We demonstrate its ability to find and maintain a diverse "Pareto optimal population" on two artificial problems and an open problem in hydrosystems.
Keyphrases
multiple objective multiobjective optimization niched pareto genetic algorithm multiple attribute niched pareto ga genetic algorithm open problem pareto optimal set linear combination weighted sum artificial problem optimization problem pareto domination diverse pareto optimal population scalar objective function ad hoc selection operator niching pressure
