A novel differential evolution algorithm based on Ɛ -domination and orthogonal design method for multiobjective optimization

Authors:
Zhihua Cai;Wenyin Gong;Yongqin Huang
Affiliations:
School of Computer Science, China University of Geosciences, Wuhan, P.R. China and Dept. of computer science, The University of Western Ontario, London, Ontario, Canada;School of Computer Science, China University of Geosciences, Wuhan, P.R. China;School of Computer Science, China University of Geosciences, Wuhan, P.R. China
Venue:
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Year:
2007

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Abstract

To find solutions as close to the Pareto front as possible, and to make them as diverse as possible in the obtained non-dominated front is a challenging task for any multiobjective optimization algorithm.∈-dominance is a concept which can make genetic algorithm obtain a good distribution of Pareto-optimal solutions and has low computational time complexity, and the orthogonal design method can generate an initial population of points that are scattered uniformly over the feasible solution space.In this paper, combining Ɛ-dominance and orthogonal design method, we propose a novel Differential Evolution (DE) algorithm for multiobjective optimization. Experiments on a number of two- and three-objective test problems of diverse complexities show that our approach is able to obtain a good distribution with a small computational time in all cases. Compared with several other state-of-the-art evolutionary algorithms, it achieves not only comparable results in terms of convergence and diversity metrics, but also a considerable reduction of the computational effort.