Evolutionary Multi-Objective Optimization Algorithms using Pareto Dominance Bank and Density Weighting


The KIPS Transactions:PartB , Vol. 11, No. 2, pp. 213-220, Apr. 2004
10.3745/KIPSTB.2004.11.2.213,   PDF Download:

Abstract

Evolutionary algorithms are well-suited for multi-objective optimization problems involving several, often conflicting objective. Pareto-based evolutionary algorithms, in particular, have shown better performance than other multi-objective evolutionary algorithms in comparison. Recently, pareto-based evolutionary algorithms uses a density information in fitness assignment scheme for generating uniform distributed global pareto optimal front. However, the usage of density information is not important elements in a whole evolution path but plays an auxiliary role in order to makeuniform distribution. In this paper, we propose an evolutionary algorithms for multi-objective optimization which assigns the fitness using pareto dominance rank and density weighting, and thuspareto dominance rank and density have similar influence on the whole evolution path. Furthermore, the experimental results, which applied our method to the six multi-objective optimization problems, show that the proposed algorithms show more promising results.


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Cite this article
[IEEE Style]
J. S. Hyeon, "Evolutionary Multi-Objective Optimization Algorithms using Pareto Dominance Bank and Density Weighting," The KIPS Transactions:PartB , vol. 11, no. 2, pp. 213-220, 2004. DOI: 10.3745/KIPSTB.2004.11.2.213.

[ACM Style]
Jang Su Hyeon. 2004. Evolutionary Multi-Objective Optimization Algorithms using Pareto Dominance Bank and Density Weighting. The KIPS Transactions:PartB , 11, 2, (2004), 213-220. DOI: 10.3745/KIPSTB.2004.11.2.213.