云自适应粒子群算法  

Adaptive particle swarm optimization algorithm based on cloud theory

作　　者：韦杏琼[1] 周永权[1] 黄华娟[1] 罗德相[1] 

机构地区：[1]广西民族大学数学与计算机科学学院,南宁530006

出　　处：《计算机工程与应用》2009年第1期48-50,76,共4页Computer Engineering and Applications

基　　金：国家自然科学基金;广西自然科学基金~~

摘　　要：文中提出了云自适应粒子群优化(CAPSO)算法,根据粒子适应度值把种群分为三个子群,分别采用不同的惯性权重生成策略,由X条件云发生器自适应调整普通子群粒子的惯性权重,由于云模型云滴具有随机性和稳定倾向性特点,使惯性权重既具有传统的趋势性,满足快速寻优能力,又具有随机性,在提高收敛速度和保持种群多样性之间做了一个很好的权衡。通过典型函数优化实验表明,与标准粒子群算法相比,CAPSO具有较高的计算精度和较快的收敛速度。In this paper,an adaptive particle swarm optimization algorithm based on cloud theory is proposed,the particles are divided into three group based on the fitness of the particle in order to adopt different inertia weight generating strategy.The inertia weight in general group is adaptively varied depending on X-conditional cloud generator.The inertia weight has the stable tendency and randomness property because of the cloud model,this not only improves the convergence speed,but also maintains the diversity of the population.In all cases studied,CAPSO is greatly superior than PSO in the terms of efficiency.

关 键 词：粒子群优化 惯性权重 自适应参数调整 云理论 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]