随机差分变异粒子群混合优化算法

针对传统粒子群优化算法与差分进化算法都易出现早熟等问题,提出了一种随机差分变异粒子群混合优化算法。算法结合粒子群与差分算法的各自特点,首先采用差分变异方法产生试探性候选个体,再将其代入到粒子群速度更新公式,引导粒子飞行方向,从而扩大搜索空间,增强算法的全局勘探能力。为避免粒子陷入局部最优解,采用随机差分变异方式对当前最优粒子进行扰动,使算法在有效提高局部开采能力的同时,有效避免停滞现象的发生。算法分别在单峰及多峰等8个测试函数上与3个相关算法进行对比实验,实验结果表明,新的混合算法优于其他对比算法,有效提高了算法的性能。

Hybrid algorithm based on particle swarm optimization with stochastic differential mutation

To solve the problem of premature convergence in traditional particle swarm optimization (PSO) and differential evolution (DE),a hybrid algorithm based on particle swarm optimization with stochastic differential mutation is proposed in this paper.Combining with the characteristics between PSO and DE,the new algorithm firstly generates a candidate individual using differentia] mutation,and then put the individual into velocity update formula leading flight direction of particle,which can expand the search space and enhance the global explorative ability of algorithm.Meanwhile,a stochastic differential mutation method is presented to disturb the current optimal particle in order to avoid the best particle being trapped into local optima,since which may cause search stagnation.The new algorithm compared with three related algorithms on 8 benchmark functions including unimodal and multimodal test functions.The experimental results show that the new hybrid algorithm outperforms other comparative algorithms and greatly improves performance of algorithm.