A Particle Swarm Optimization Algorithm with Dynamic Population Synergy

Particle swarm optimization (PSO) algorithms have been successfully applied to all kinds of optimization problems. However, the standard PSO algorithm can easily fall into local optimal regions when solving complex problems. It is difficult for the particles to get out of these regions, resulting in the inability to obtain the global optimal solution. This paper proposes a dynamic population synergy particle swarm optimization algorithm (DPSPSO) to solve the above problem. The DPSPSO algorithm dynamically divides the population into three sub-populations based on the function-adapted value ordering strategy. Each sub-population has a different task depending on its potential. Moreover, the algorithm employs an integrated learning strategy. This strategy aims to fully utilize the practical information provided by the particles to prevent premature convergence of the particles. Finally, this paper compares the proposed algorithm with five state-of-the-art PSO variants on the CEC2022 test function to investigate the algorithm’s effectiveness. The experimental results show that DPSPSO can achieve competitive performance compared to the other PSO variants.