Cooperative coevolutionary competition swarm optimizer with perturbation for high-dimensional multi-objective optimization

Abstract

In the realm of high-dimensional problem spaces, particle swarm optimizers have been found to exhibit unnecessary roaming behavior. In response, this paper proposes a cooperative coevolutionary competition swarm optimizer with perturbation (CPCSO) that reduces computational resource consumption. The CPCSO is both simple and effective. Specifically, this optimizer divides the swarm into two sub-swarms, denoted NP1 and NP2. A modified CSO algorithm is used in NP1 to facilitate search space exploration while ensuring that the swarm is well diversified. In NP2, perturbation is introduced to each loser particle to guide it along a smooth granular trajectory, thereby avoiding unnecessary oscillations and improving its capacity to exploit the search space. The two sub-swarms exchange information to balance convergence and distribution, with excellent particles shared between them. Finally, we demonstrate the efficacy of the proposed CPCSO algorithm and several state-of-the-art high-dimensional multi-objective optimizers on the high-dimensional benchmark set LSMOP. Our experimental results indicate that the proposed CPCSO outperforms other algorithms regarding solution quality, convergence speed, and computational cost. Notably, the proposed optimizer demonstrates robust performance across various landscape problems.