| 云自适应粒子群算法 |
| |
| 引用本文： | 韦杏琼,周永权,黄华娟,罗德相.云自适应粒子群算法[J].计算机工程与应用,2009,45(1):48-50. |
| |
| 作者姓名： | 韦杏琼 周永权 黄华娟 罗德相 |
| |
| 作者单位： | 广西民族大学,数学与计算机科学学院,南宁,530006 |
| |
| 基金项目： | 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。
|
| 关 键 词： | 粒子群优化 惯性权重 自适应参数调整 云理论 |
| 收稿时间： | 2008-6-16 |
| 修稿时间： | 2008-9-8
|
Adaptive particle swarm optimization algorithm based on cloud theory |
| |
| WEI Xing-qiong,ZHOU Yong-quan,HUANG Hua-juan,LUO De-xiang.Adaptive particle swarm optimization algorithm based on cloud theory[J].Computer Engineering and Applications,2009,45(1):48-50. |
| |
| Authors: | WEI Xing-qiong ZHOU Yong-quan HUANG Hua-juan LUO De-xiang |
| |
| Affiliation: | WEI Xing-qiong,ZHOU Yong-quan,HUANG Hua-juan,LUO De-xiang College of Mathematics , Computer Science,Guangxi University for Nationalities,Nanning 530006,China |
| |
| Abstract: | 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... |
| |
| Keywords: | Particle Swarm Optimization（PSO） inertia weight adaptive parameter adjusting cloud theory |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
| 点击此处可从《计算机工程与应用》浏览原始摘要信息 |
|
点击此处可从《计算机工程与应用》下载全文 |
|
