首页 | 本学科首页   官方微博 | 高级检索  
     

一种新的差分与粒子群算法的混合算法
引用本文:王志,胡小兵,何雪海. 一种新的差分与粒子群算法的混合算法[J]. 计算机工程与应用, 2012, 48(6): 46-48
作者姓名:王志  胡小兵  何雪海
作者单位:重庆大学 数理学院,重庆 400030
摘    要:利用粒子群算法的快速收敛性和差分进化算法的搜索精度较高等特点,提出了一种新的混合优化算法。该算法在粒子群算法的中后期,在已经寻找到的最优位置周围,随机生成一定数量的粒子进行差分进化算法,可以减少一定的运算量和在较优的区域进行寻找最优解。通过几个Benchmark函数的测试证明,新的混合算法具有搜索精度更高和更快收敛的优点。

关 键 词:差分进化算法  粒子群优化算法  混合算法  

New hybrid optimization based on differential evolution and particle swarm optimization.
WANG Zhi , HU Xiaobing , HE Xuehai. New hybrid optimization based on differential evolution and particle swarm optimization.[J]. Computer Engineering and Applications, 2012, 48(6): 46-48
Authors:WANG Zhi    HU Xiaobing    HE Xuehai
Affiliation:School of Mathematics & Physics, Chongqing University, Chongqing 400030, China
Abstract:To take advantage of different algorithms, a hybrid optimization algorithm is proposed based on the combination of Differential Evolution (DE) and Particle Swarm Optimization (PSO). At the last period of the hybrid optimization, a new population will be produced around the best position found by the PSO, and DE is carried out with this population. The hybrid optimization can deduce the computa- tional work to some degree and has more chance to find the best solution in a better region. Numerical tests on some benchmark functions are conducted for the algorithm evaluation. The results show the higher precision and more probability to find the best solution.
Keywords:Differential Evolution ( DE )  Particle Swarm Optimization (PSO)  hybrid algorithm
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
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