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Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms (1994)
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| Venue: | Evolutionary Computation |
| Citations: | 520 - 4 self |
BibTeX
@ARTICLE{Srinivas94multiobjectiveoptimization,
author = {N. Srinivas and Kalyanmoy Deb},
title = {Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms},
journal = {Evolutionary Computation},
year = {1994},
volume = {2},
pages = {221--248}
}
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Abstract
In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Pareto-optimal points, instead of a single point. Since genetic algorithms(GAs) work with a population of points, it seems natural to use GAs in multiobjective optimization problems to capture a number of solutions simultaneously. Although a vector evaluated GA (VEGA) has been implemented by Schaffer and has been tried to solve a number of multiobjective problems, the algorithm seems to have bias towards some regions. In this paper, we investigate Goldberg's notion of nondominated sorting in GAs along with a niche and speciation method to find multiple Pareto-optimal points sim...
Keyphrases
genetic algorithm multiobjective optimization using nondominated sorting multiobjective optimization problem multiobjective problem underlying problem pareto-optimal point multiple pareto-optimal point sim objective vector scalarization process speciation method single point many traditional method weight vector obtained solution single objective
