A Novel Opposition-Based Multi-objective Differential Evolution Algorithm for Multi-objective Optimization

Authors:
Lei Peng;Yuanzhen Wang;Guangming Dai
Affiliations:
College of Computer Science, Huazhong University of Science and Technology, Wuhan, China 430074 and School of Computer, China University of Geosciences, Wuhan, China 430074;College of Computer Science, Huazhong University of Science and Technology, Wuhan, China 430074;School of Computer, China University of Geosciences, Wuhan, China 430074
Venue:
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Year:
2008

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Abstract

Multiobjective optimization is of increasing importance in various fields and has very broad applications. The purpose of this paper is to describe a novel multiobjective optimization algorithm---opposition-based multi-objective differential evolution algorithm(OMODE). In the paper, OMODE uses the opposition-based population to generate the initial population of points, The important scaling factor is controlled by self-adaptive method. Performance of OMODE is demonstrated with a set of benchmark test functions and Earth-Mars double transfer problem. The results show that OMODE achieves better performance than other methods.