引入人工蜂群搜索算子的粒子群算法  

Particle swarm optimization with search operator of artificial bee colony algorithm

作　　者：高卫峰[1] 刘三阳[1] 焦合华[1,2] 秦传东[1] 

机构地区：[1]西安电子科技大学数学科学系,西安710071 [2]长江师范学院数学与计算机学院,重庆408100

出　　处：《控制与决策》2012年第6期833-838,共6页Control and Decision

基　　金：国家自然科学基金项目(60974082);中央高校基本科研业务费专项基金项目(50510700004)

摘　　要：针对标准粒子群算法易出现早熟现象和收敛速度慢等问题,提出一种引入人工蜂群搜索算子的粒子群算法.首先利用人工蜂群搜索算子很强的探索能力,对粒子搜索到的历史最优位置进行搜索以帮助算法快速跳出局部最优点;然后,为了提高算法的全局收敛速度,提出一种基于混沌和反学习的初始化方法.通过12个标准测试函数的仿真实验并与其他算法相比较,所得结果表明所提出的算法具有较快的收敛速度和很强的跳出局部最优的能力.To the problems of premature convergence frequently appeared in standard particle swarm optimization（PSO） algorithm and its poor convergence,a particle swarm optimization algorithm with the search operator of artificial bee colony is proposed.Firstly,the method makes full use of the exploration of artificial bee colony algorithm search operator to help the algorithm to jump out of the likely local optima.Then to enhance the global convergence,when producing the initial population,both chaotic maps and opposition-based learning based method is proposed.Moreover,simulation experiment on a suite of 12 benchmark functions is given,and the comparisons with other algorithms are provided.The results show that the proposed approach has better convergence rate and great capability of preventing premature convergence.

关 键 词：粒子群优化 种群初始化 搜索算子 人工蜂群算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]