A new orthogonal evolutionary algorithm based on decomposition for multi-objective optimization

Cai Dai, Yuping Wang and Wei Yue
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Cai Dai: School of Computer Science and Technology, Xidian University, Xian, P.R. China
Yuping Wang: School of Computer Science and Technology, Xidian University, Xian, P.R. China
Wei Yue: School of Computer Science and Technology, Xidian University, Xian, P.R. China

Journal of the Operational Research Society, 2015, vol. 66, issue 10, 1686-1698

Abstract: The diversity of solutions is very important for multi-objective evolutionary algorithms to deal with multi-objective optimization problems (MOPs). In order to achieve the goal, a new orthogonal evolutionary algorithm based on objective space decomposition (OEA/D) is proposed in this paper. To be specific, the objective space of an MOP is firstly decomposed into a set of sub-regions via a set of direction vectors, and OEA/D maintains the diversity of solutions by making each sub-region have a solution to the maximum extent. Also, the quantization orthogonal crossover (QOX) is used to enhance the search ability of OEA/D. Experimental studies have been conducted to compare this proposed algorithm with classic MOEA/D, NSGAII, NICA and D2MOPSO. Simulation results on six multi-objective benchmark functions show that the proposed algorithm is able to obtain better diversity and more evenly distributed Pareto fronts than other four algorithms.

Date: 2015
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