Mutation Operators Based on Variable Grouping for Multi-Objective Large-Scale Optimization


In this paper, we study the influence of using variable grouping inside mutation operators for large-scale multi-objective optimization. We introduce three new mutation operators based on the well-known Polynomial Mutation. The variable grouping in these operators is performed using two different grouping mechanisms, including Differential Grouping from the literature. In our experiments, two popular algorithms (SMPSO and NSGA-II) are used with the proposed operators on the WFG1-9 test problems. We examine the performance of the proposed mutation operators and take a look at the impact of the different grouping mechanisms on the performance. Using the Hypervolume and IGD indicators, we show that mutation using variable grouping can significantly improve the results on all tested problems in terms of both convergence and diversity of solutions. Furthermore, the performance of SMPSO and NSGA-II with the proposed operators is compared with a large-scale multi-objective algorithm (CCGDE3). The results show that the proposed operators can significantly outperform CCGDE3.