基于种群划分与变异策略的粒子群优化算法

粒子群算法因其形式比较简洁,参数设置灵活,操作简便易行,并且能够快速收敛,从而引起广泛关注。但是传统的粒子群算法也有缺陷:收敛速度慢以及容易陷入局部最优等。针对这些问题,本文借鉴小生境的方法,在进化初始阶段,对种群进行划分,将初始种群分为子种群,对不同的子种群进行不同的变异策略;在进化过程中,针对不同的子种群,设置不同的惯性权重因子ω,用来增强全局搜索能力与局部搜索能力。实验结果表明,本文提出的算法较传统的粒子群算法具有较快的收敛性以及找寻的全局最优解更接近真实解集,收敛精度比较高。

Particle Swarm Optimization Algorithm Based on Population Division and Variation Strategy

Particle swarm optimization algorithm is of simple in form, flexible in parameter setting, easy to operate, and capable of fast convergence， so it has attracted many attentions. However, the traditional particle swarm algorithm also has its drawbacks: the slow convergence rate and the vulnerability to local optimization. In order to solve this problem, this paper uses the niche method to initialize the population, in the initial stages of evolution, dividing the initial population into subpopulations, and using different mutation strategies for different subpopulations; in the process of evolution, different inertia weighting factors are set for different subpopulations, in order to enhance the global search ability and the local search ability. The results of test functions show that the algorithm has faster convergence than the traditional particle swarm algorithm, the global optimal solution is closer to the real solution set, and the convergence accuracy is also higher.