Research on an orthogonal and model based multi-objective genetic algorithm

Authors:
Guangming Dai;Yanzhi Li;Wei Zheng
Affiliations:
School of Computer, China University of Geosciences, Wuhan, China;School of Computer, China University of Geosciences, Wuhan, China;School of Computer, China University of Geosciences, Wuhan, China
Venue:
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Year:
2009

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Abstract

Against low efficiency of traditional multi-objective evolutionary algorithms and poor utilization of Pareto-optimal solutions distribution regularity etc, in this paper, a new approach OMEA is proposed. It uses that distribution regularity to obtain good solutions, we also apply the orthogonal design to initialize population. Compared with SPEA2, NSGA-II and PAES, Pareto solutions by OMEA are closer to Pareto-optimal Front. The result of experiments shows a group of Pareto solutions with better convergence and diversity can be achieved, which gives strong supports to actual applications.