基于多样性指标的两群替代微粒群优化算法  

Two Sub-swarms Substituting Particle Swarm Optimization Algorithm with Diversity-based Rule

作　　者：毛恒[1] 王永初[1] 

机构地区：[1]华侨大学机电及自动化工程学院,泉州362021

出　　处：《计算机工程》2008年第4期187-189,共3页Computer Engineering

基　　金：国务院侨务办公室科研基金资助项目(03QZR13)

摘　　要：粒子群优化算法是进化计算领域中的一个新的分支。该算法简单且功能强大,但是粒子群优化也容易发生过早收敛的问题。该文提出一种两群替代微粒群优化算法,该方法将微粒分成不同的两分群进行搜索寻优。搜索一定次数后,每一次迭代首先判断微粒群的多样性是否低于一个阈值,若低于则按照黄金分割率用一分群中若干优势微粒取代另一分群中的劣势微粒。对3种常用函数的优化问题进行测试和比较,结果表明,该两群替代微粒群优化算法比基本微粒群优化算法更容易找到全局最优解,优化效率和优化性能明显提高。The particle swarm optimization algorithm is a new branch in evolution computing field. This algorithm is simple and effective, and is easy in premature convergence. In this paper, Two Sub-swarms Substituting Particle Swarm Optimization algorithm（TSSPSO） is proposed. The new algorithm assumes that particles are divided into two sub-swarms to search and find optimization. After several iteration, it can be estimated whether the diversity of the swarm is under a threshold or not. If it is true then some bad particles of one sub-swarm are replaced with some good particles of another sub-swarm. The number of the replaced swarm is gained by the golden division. Both TSSPSO and Particle Swarm Optimization algorithm （PSO） are used to resolve three well-known and widely used test functions＇ optimization problems. Results show that TSSPSO has greater efficiency, better performance and more advantages than PSO in many aspects.

关 键 词：微粒群优化算法 两群替代微粒群优化算法 多样性指标 黄金分割率 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]