Two-Population Comprehensive Learning PSO Algorithm Based on Particle Per-mutation

Abstract

In order to solve the problems of low population diversity and easy to fall into local optimization of particle swarm optimization (PSO), a two-population comprehensive learning PSO algorithm based on particle permutation (PP-CLPSO) is proposed. According to the convergence characteristic of PSO algorithm and the chaotic idea of Logistic mapping, the PSO population and chaotic population of parallel evolution are designed. Combined with the particle numbering mechanism, the same sign structure and the same position structure of particles in the two-population system are formed, in which the inertia weight of particles is adaptively adjusted according to the fitness value. When the search process falls into local optimization, the particles with poor fitness under the same position structure of the PSO population carry out the particle replacement operation according to the same sign structure between the chaotic population and the chaotic population, which realizes the reasonable scheduling of the resources of the two-population system and increases the diversity of the population. Furthermore, the global exploration and local search are carried out by combining the co-particle learning strategy of two-way search and the local learning strategy of linearly decreasing search step, which improves the accuracy of the algorithm. Nine benchmark functions are selected in the experiment, and the proposed algorithm is compared with four improved particle swarm optimization algorithms and four swarm intelligence algorithms at the same time. The experimental results show that the PP-CLPSO algorithm has better comprehensive performance in terms of solution accuracy and convergence speed.

Keywords

• particle swarm optimization (pso)
• two-population system
• particle numbering
• particle permutation
• comprehensive learning