一种“自我”感知的高维混沌群体智能算法  

A Self-Perception High-Dimensional Chaotic Particle Swarm Algorithm

作　　者：陶乾[1,2] 黄哲学[2] 顾春琴[3] 

机构地区：[1]广东第二师范学院计算机科学系,广州510310 [2]中国科学院深圳先进技术研究院,深圳518055 [3]仲恺农业工程学院网络工程系,广州510225

出　　处：《集成技术》2014年第3期15-21,共7页Journal of Integration Technology

基　　金：中国博士后基金(2013M542219);广东省重大科技攻关项目(2012A080104022);广东省自然科学基金重点项目(S2012020011067);广东第二师范学院博士专项(2012ARF05)

摘　　要：为避免早熟收敛和提升粒子在高维空间的搜索能力,文章提出了一种"自我"感知的高维混沌群体智能算法。首先,采用pBest和gBest混沌双扰动来增强粒子的搜索能力;其次,提出一种"自我"感知策略来帮助种群避免早熟收敛;最后,将三种不同微粒群优化(Particle Swarm Optimization,PSO)算法在旅行推销员问题(Traveling Salesman Problem,TSP)上进行了对比实验。实验结果显示"自我"感知的高维混沌群体智能算法简单、有效可行,值得推荐。To avoid the premature convergence and enhance the search capability of the high-dimensional space, a novel self-perception high-dimensional chaotic particle swarm algorithm was presented. Firstly, a double perturbation of pBest and gBest was used to enhance the searching capability of particles. Secondly, self-perception approach was proposed to help the particle swarm to avoid the premature convergence. Lastly, three discrete PSO variants were tested on the traveling salesman problem （TSP）. Experimental results show that the self-perception high-dimensional chaotic particle swarm algorithm is simple, effective and promoting in a high-dimensional space.

关 键 词：微粒群优化 混沌 扰动 自我感知 收敛 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]