A Two-Population Algorithm for Large-Scale Multiobjective Optimization Based on Fitness-Aware Operator and Adaptive Environmental Selection
针对大规模多目标优化问题，提出一种双种群算法LSTPA，通过适应度感知算子加速收敛，并采用自适应环境选择策略平衡收敛性与多样性，在100-2000维基准问题上表现优于现有算法。
Multi-objective optimization problems (MOPs) containing a large number of decision variables, which are also known as large-scale multi-objective optimization problems (LSMOPs), pose great challenges to most existing evolutionary algorithms. This is mainly because that a high dimensional decision space degrades the effectiveness of search operators notably, and balancing convergence and diversity becomes a challenging task. In this paper, we propose a two-population based algorithm for large-scale multi-objective optimization named LSTPA. In the proposed algorithm, solutions are classified in to two subpopulations: a Convergence subPopulation (CP) and a Diversity subPopulation (DP), aiming at convergence and diversity respectively. In order to improve convergence speed, a fitness-aware variation operator (FAVO) is applied to drive DP solutions towards CP. Besides, an adaptive penalty based boundary intersection (APBI) strategy is adopted for environmental selection in order to balance convergence and diversity temporally during different stages of evolution process. Experimental results on benchmark test problems with 100-2000 decision variables demonstrate that the proposed algorithm can achieve the best overall performance compared with several state-of-the-art large-scale multi-objective evolutionary algorithms.