Influence of Mutation Mechanism on the Performance of Constrained Multi-objective Particle Swarm Optimization - Hao, Lupeng; Liu, Junhua; Wang, Rongchen (2022 18th International Conference on Computational Intelligence and Security (CIS), 2022)
For a large number of algorithms that use the dual archive mechanism to solve the constrained multi-objective optimization problem, there is no mutation mechanism strategy added to the archive. In order to explore the impact of the mutation mechanism on the performance of the constrained multi-objective particle swarm optimization algorithm after adding the mutation mechanism to the two types of archives, in this paper, different mutation operators for the feasible and infeasible archives are implemented respectively, and 6 typical constrained multi-objective benchmarks are used for testing our algorithm. The research found that the performance of the algorithm can be improved to a certain extent by mutating the feasible solution archive, but it does not play a decisive role in solving complex constrained multi-objective problems. Therefore, in the future research, we should also seek improvement strategies from other aspects to better to solve a constrained multi-objective problem.