A Two-Population Algorithm for Large-Scale Multi-objective Optimization Based on Fitness-Aware Operator and Adaptive Environmental Selection

Multiobjective optimization problems (MOPs) containing a large number of decision variables, which are also known as large-scale MOPs (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 article, we propose a two-population-based algorithm for large-scale multiobjective optimization named large-scale two population algorithm. In the proposed algorithm, solutions are classified in to two subpopulations: 1) a convergence subpopulation (CP) and 2) 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 toward 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 multiobjective evolutionary algorithms.