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

作者：Dai, Cai^*; Wang, Yuping; Yue, Wei

来源：Journal of the Operational Research Society, 2015, 66(10): 1686-1698.

DOI：10.1057/jors.2014.80

• multi-objective optimization
• evolutionary algorithm
• quantization orthogonal crossover
• decomposition

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.

• 出版日期2015-10
• 单位西安电子科技大学