粒子置换的双种群综合学习PSO算法

doi:10.3778/j.issn.1673-9418.2005016

摘要/Abstract

摘要：

针对粒子群算法（PSO）种群多样性低和易于陷入局部最优等问题，提出一种粒子置换的双种群综合学习PSO算法（PP-CLPSO）。根据PSO算法的收敛特性和Logistic映射的混沌思想，设计并行进化的PSO种群和混沌化种群，结合粒子编号机制，形成双种群系统中粒子的同号结构和同位结构，其中粒子的惯性权重根据适应度值自适应调节；当搜索过程陷入局部最优时，PSO种群同位结构下适应度值较差的粒子，根据与混沌化种群间的同号结构执行粒子置换操作，实现了双种群系统资源的合理调度，增加了种群的多样性；进而综合双向搜索的同位粒子学习策略和线性递减搜索步长的局部学习策略，进行全局探勘和局部搜索，提高了算法的求解精度。实验选取9个基准测试函数，同时与4个改进的粒子群算法和4个群智能算法进行对比验证，实验结果表明，PP-CLPSO算法在求解精度和收敛速度等方面具备较好的综合性能。

关键词: 粒子群算法（PSO）, 双种群系统, 粒子编号, 粒子置换, 综合学习

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.

Key words: particle swarm optimization (PSO), two-population system, particle numbering, particle permutation, comprehensive learning

纪伟, 李英梅, 季伟东, 张珑. 粒子置换的双种群综合学习PSO算法[J]. 计算机科学与探索, 2021, 15(4): 766-776.

JI Wei, LI Yingmei, JI Weidong, ZHANG Long. Two-Population Comprehensive Learning PSO Algorithm Based on Particle Per-mutation[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(4): 766-776.

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