An Improved Competitive Swarm Optimizer with Super-Particle-Leading

Competitive swarm optimizer (CSO) has been concerned in recent years due to its achievements in solving global optimization problems. However, the CSO algorithm still suffers from issues such as low solution precision and premature convergence since it only relies on the winners to guide the population evolution. To address this issue, an improved competitive swarm optimizer with super-particle-leading is proposed in this paper. First, the super particle obtained by the cumulative learning strategy is used to provide a promising evolution direction for the population. Next, the weight-based dynamic omnidirectional strategy is employed to enhance the population exploration ability. Finally, CEC2017 benchmark problems are used to evaluate the efficiency of the proposed algorithm. The experimental results demonstrate that the proposed algorithm is competitive with the contender algorithms due to its better balance between exploration and exploitation.