A Novel Particle Swarm Optimization Algorithm for Meta-heuristic Analysis Mechanism Based on Population Learning Strategies and Adaptive Selection of Leadership Particles

To improve the particle swarm optimization algorithm's population diversity and global search ability, this study proposes a novel particle swarm optimization algorithm for metaheuristic analysis mechanism based on population learning strategies and adaptive selection of leadership particles (ASLPSO) to achieve the fusion of population learning strategy and adaptive selection method of leadership particles. The particle swarm is adaptively separated into several populations through density peak clustering. Meanwhile, a new learning strategy is designed for analyzing the local optimal particles of each subgroup, that might enable the ordinary particles to learn effectively. The global optimum is relatively ensured by comparing the fitness value of each local optimal particle, which might obtain the best performances of multi-peak reference functions. After comparing with the approximate algorithms on multiple benchmark functions, it is found that the standard deviation and error mean value are improved. Therefore, ASLPSO has enhanced the population diversity and global search ability, prevented the algorithm from falling into premature too early.