基于粒子健康度的快速收敛粒子群优化算法  

PSO algorithm with high speed convergence based on particle health

作　　者：靳其兵[1] 赵振兴[1] 苏晓静[2] 曹丽婷[1] 

机构地区：[1]北京化工大学信息科学与技术学院,北京100029 [2]北京交通大学机械与电子控制工程学院,北京100044

出　　处：《化工学报》2011年第8期2328-2333,共6页CIESC Journal

基　　金：国家高技术研究发展计划项目(2008AA042131);国家重点基础研究发展计划项目(2007CB714300)~~

摘　　要：针对现有粒子群优化算法在工程应用中,特别是在粒子维数较高的情况下,很容易发生早熟收敛等缺点,提出了一种基于粒子健康度的快速收敛粒子群优化算法(HPSO)。给出了粒子健康度的概念及计算方法。该算法通过动态监控粒子的健康度指标,对健康度较低的粒子单独进行变异操作。从而可以在保护健康粒子继续搜索最优值的同时,有效'治疗'非健康的早熟粒子,提高了整个粒子群的寻优能力及跳出局部最优值的能力。然后通过大量的标准测试函数对其进行测试,并将其与标准粒子群优化算法(SPSO)、权重递减的粒子群优化算法(WPSO)进行对比。测试结果表明,在粒子维数较高的应用中HPSO算法的收敛速度更快,效率更高。Particle swarm optimization（PSO）which has the general purpose optimization method received much attention in past years.In many studies,PSO has been successful in a variety of optimization problems.But the speed of convergence of standard PSO algorithm on high dimensional search space is unacceptable in practice.The concept of particle health was proposed,and gives an algorithm for particle health calculation in this paper.A new variation of PSO model proposed based on particle health（HPSO） can effectively reduce the probability of local optimum and enhance convergence speed especially for high dimensional search spaces.The proposed were tested by a variety of high-dimensional benchmark functions,and compared with standard PSO algorithm and decreasing inertia weight variation（WPSO）.It was found that the application of these modifications resulted in significant gain in speed and efficiency.

关 键 词：粒子健康度 粒子群优化 快速收敛 早熟 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]