一种新环境选择策略的多模态多目标优化算法

Multi-model Multi-objective Optimization Algorithms with a New Environmental Selection Strategy

为了获得多模态多目标优化问题较优解集,本文针对差分进化算法提出了一种新的环境选择策略,一方面通过保留种群中非支配解确保目标空间的收敛性,另一方面通过和参考向量关联获得目标空间分布性较好的种群,通过同时考虑目标空间收敛性和决策空间多样性来选择下一代父代个体。在11个多模态多目标测试函数上的结果表明,本文算法在求解多模态多目标优化问题上是有效的。

In order to achieve the optimal solutions for multi-model multi-objective problems, in this paper a new environmental selection strategy is proposed for differential evolution approach. First, non-dominated solutions are kept to ensure the convergence in objective space;second, a population with good distribution in objective space is obtained by using its correlation with reference vectors;and then the next parent population is selected by simultaneously considering the convergence performance in objective space and the diversity performance in decision space. Experimental results on 11 multi-model multiobjective test problems show that the proposed method is efficient in solving multi-model multi-objective problems.