首页 | 本学科首页   官方微博 | 高级检索  
     

混沌协同人工鱼粒子群混合算法及其应用
引用本文:张创业,莫愿斌,何登旭,王万民.混沌协同人工鱼粒子群混合算法及其应用[J].计算机工程与应用,2010,46(32):48-51.
作者姓名:张创业  莫愿斌  何登旭  王万民
作者单位:1.广西民族大学 数学与计算机科学学院,南宁 530006 2.南昌大学 机电工程学院,南昌 330031
基金项目:广西民族大学人才引进科研启动项目
摘    要:针对基本人工鱼群算法(AFSA)收敛速度较慢、精度较低和粒子群易陷于局部的缺点,提出了混沌协同人工鱼粒子群混合算法(CCAFSAPSO)。该算法采取AFSA、PSO的全局并行搜索与模拟退火算法(SA)的局部串行搜索机制相结合的搜索方式,并用混沌映射的遍历性和模拟退火算法的突跳功能,克服了AFSA、PSO的收敛速度、求解精度和易陷于局部最优的不足。典型函数测试进一步表明CCAFSAPSO算法和同类算法相比,收敛速度更快、求解精度较高。最后将算法应用于化工数据处理,获得满意效果。

关 键 词:优化算法  人工鱼算法  粒子群算法  模拟退火  混沌  协同  
收稿时间:2009-4-14
修稿时间:2009-6-5  

AFSA-PSO hybrid algorithm based on chaos-collaborative evolution and its application
ZHANG Chuang-ye,MO Yuan-bin,HE Deng-xu,WANG Wan-min.AFSA-PSO hybrid algorithm based on chaos-collaborative evolution and its application[J].Computer Engineering and Applications,2010,46(32):48-51.
Authors:ZHANG Chuang-ye  MO Yuan-bin  HE Deng-xu  WANG Wan-min
Affiliation:1.College of Mathematics and Computer Science,Guangxi University for Nationalities,Nanning 530006,China 2.School of Mechatronics Engineering,Nanchang University,Nanchang 330031,China
Abstract:Aiming at the drawbacks of Artificial Fish-Swarm Algorithm(AFSA),such as being poor in performance of precision and being low in rate of convergence,Chaos Cooperative Artificial Fish-Swarm Algorithm Particle Swarm Optimization(CCAFSA) is presented.By combining AFSA,PSO global parallel search with Simulation Annealing algorithm(SA) search mechanism,and taking the snap leaping function of Simulated Annealing algorithm and the ergodicity of chaos,CCAFSAPSO overcomes the deficiencies of AFSA and PSO in convergence speed,accuracy and the easy trapping into local optimum.The results of the typical function tests further show that CCAFSAPSO algorithm has faster convergence,higher accuracy than similar algorithms.At last the algorithm is applied to process chemical engineering data and gets satisfying results.
Keywords:optimization algorithm  Artificial Fish-Swarm Algorithm(AFSA)  Particle Swarm Optimization(PSO)  Simulated Annealing(SA)  chaos  collaborative  
本文献已被 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
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号