基于差分进化邻域自适应的大规模多目标算法

对于大规模决策变量给求解大规模多目标优化问题带来的难以收敛及解集分布不均匀问题, 通过分析变量特征将其分类再分别优化是当前较为有效的求解方法, 但存在变量分类不够准确、变量处理不够有针对性等不足。对此, 提出一种基于差分进化邻域自适应策略的大规模多目标优化算法。首先，通过分析扰动解的支配关系将混合变量分为多样性变量和收敛性变量, 使变量分类更为准确。其次，通过对收敛性变量主成分分析降噪，降低计算成本, 并设计种群的交替进化策略及差分进化的邻域自适应更新操作以提升种群进化过程中的收敛性。实验结果表明, 所提算法在收敛速度和解集的分布均匀性上表现出良好的性能。

Large-scale multi-objective algorithm based on neighborhood adaptive of differential evolution

Since there are large scale decision variables in large-scale multi-objective optimization problems (LSMOPs), the algorithm is difficult to converge and the distribution of the solution set is uneven. It is an effective way to classify the decision variables and optimize them respectively by analyzing the characteristics of the variables. However, there are some shortcomings, such as inaccurate variable classification and insufficient pertinence of variable processing. Therefore, a large-scale multi-objective optimization based on differential evolution with neighborhood adaptive strategy (NAS-MOEA) is proposed to solve LSMOPs. Firstly, by analyzing the dominant relationship of the disturbance solution, the mixed variables are divided into diversity variables and convergence variables to make the variable classification more accurate. Secondly, the principal component analysis of convergence variables is used to reduce noise and computational cost. The alternative evolution strategy of population and the neighborhood adaptive update operation of differential evolution are designed to improve the convergence in the process of population evolution. Experimental results show that the proposed algorithm has good performance in convergence speed and uniformity distribution of the solution set.