A modified competitive swarm optimizer guided by space sampling for large-scale multi-objective optimization

Multi-objective evolutionary algorithms have demonstrated promising performance in solving multi/manyobjective problems. However, their performance decreases sharply when dealing with multi-objective optimization problems with hundreds or thousands of decision variables, which prevent them from quickly converging to the Pareto front. To this end, this article proposes a modified competitive swarm optimizer guided by space sampling for large-scale multi-objective optimization (LSMCSO-SS). In the initialization phase of the algorithm, we propose a space sampling method, which samples a set of individuals according to the upper and lower bounds of the decision space. Then, they are added to the initial population to guide the evolution of the algorithm. In the iteration process of the algorithm, we propose a modified competitive swarm optimizer (CSO). Different from the original CSO algorithm, we add a new velocity component to the losers to further improve the convergence speed. In the experiments, we compare our algorithm with seven state -of -the -art large-scale multi-objective evolutionary algorithms based on the inverted generational distance plus (IGD+) indicator upon nine large-scale multi-objective optimization benchmark problems with up to 5000 decision variables, and the numerous experimental results manifest that the proposed method performs the best on most test instances, which further demonstrates that it outperforms all the seven comparison algorithms.