A Self-Learning Particle Swarm Optimization Algorithm with Multi-Strategy Selection

Particle swarm optimization has been widely utilized to tackle various real optimization problems as an effective and simple optimization approach. However, the phenomenon of premature convergence has always existed. To ameliorate the drawback, a self-learning particle swarm optimization algorithm with multi-strategy selection (MSLSPSO) is proposed in this study. In MSLSPSO, an elite particles guidance strategy is proposed. This strategy makes elite particles participate in the search process to strengthen the guidance of the population. A Lévy Flight perturbation strategy is designed, which utilizes the random walk characteristic of Lévy Flight to perturb the historical and global optimal of particles, increasing the diversity of the population. In addition, a fitness distance correlation self-learning mechanism is proposed, which can self-learning according to the characteristics of the population in the search process. A set of test functions are utilized for experimental analysis and compared with four well-known PSO variants. Experimental results show that the MSLSPSO algorithm has efficient robustness and competitive solutions can be obtained.