Improving the Non-dominate Sorting Genetic Algorithm for Multi-objective Optimization

Authors:
V. Seydi Ghomsheh;M. Ahmadieh Khanehsar;M. Teshnehlab
Affiliations:
-;-;-
Venue:
CISW '07 Proceedings of the 2007 International Conference on Computational Intelligence and Security Workshops
Year:
2007

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Multi-objective evolutionary algorithms based on the summation of normalized objectives and diversified selection

Information Sciences: an International Journal

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Abstract

(NSGA-II) is a relatively recent technique for finding or approximating the Pareto-optimal set for multi-objective optimization problems. In different studies NSGA-II has shown good performance in comparison to other multi-objective evolutionary algorithms [10]. In this paper an improved version which is named Niching-NSGA-II (n-NSGA-II) is proposed. This algorithm uses new method after Non-dominate sorting procedure for keeping diversity. The comparison of n-NSGA-II with NSGA-II and other methods on ZDT test problems yields promising results.