[1]印溪[],许斌[],亓晋[]. 一种基于禁忌策略的混合优化算法[J].计算机技术与发展,2017,27(02):46-50.
　YIN Xi[],XU Bin[],QI Jin[]. A Hybrid Optimization Algorithm Based on Tabu Strategy[J].,2017,27(02):46-50.
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 一种基于禁忌策略的混合优化算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年02期
页码:
46-50
栏目:
智能、算法、系统工程
出版日期:
2017-02-10

文章信息/Info

Title:
 A Hybrid Optimization Algorithm Based on Tabu Strategy
文章编号:
1673-629X（2017)02-0046-05
作者:
 印溪[1]; 许斌[2] ; 亓晋[2]
 1.南京邮电大学自动化学院；2.南京邮电大学物联网学院
Author(s):
 YIN Xi[1] ; XU Bin[2]; QI Jin[2]
关键词:
 ; 综合学习粒子群算法; 禁忌搜索; 高斯分布; 参数自适应; 协方差矩阵自适应进化策略
Keywords:
 ; comprehensive learning particle swarm optimization; Tabu strategy; Gaussian distribution; parameter adaption; CMA-ES
分类号:
TP301.6
文献标志码:
A
摘要:
 为了提升综合学习粒子群算法(Comprehensive Learning Particle Swarm Optimization,CLPSO)的后期收敛能力,提出一种基于禁忌策略的混合优化算法,记为CLPSO+Tabu(CMA-ES).算法以禁忌搜索算法为后续搜索操作,对综合学习粒子群算法进行改进.同时将协方差矩阵自适应进化策略(Covariance Matrix Adaptation Evolution Strategy,CMA-ES)引入禁忌搜索算法,以高斯分布为基础,以CMA-ES策略引导邻域结构的分布,构造新型自适应邻域结构,指导禁忌搜索算法中候选解的选取,从而解决综合学习粒子群算法在收敛精度低的问题,极大改善了求解效果.针对26个标准测试函数的实验结果表明,与CLPSO相比,CLPSO+Tabu(CMA-ES)算法在绝大多数函数上具有更好的收敛效果.针对其中6个优化问题,CLPSO+Tabu (CMA-ES)更是有至少一个数量级的改进.
Abstract:
 In order to enhance the convergence ability of the Comprehensive Learning Particle Swarm Optimization (CLPSO) in the later stage,a hybrid optimization algorithm based on Tabu strategy is proposed and it is denoted by CLPSO+Tabu (CMA-ES).It improves CLPSO by taking Tabu strategy as the subsequent search operation.Moreover,the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is introduced to the Tabu algorithm,which is based on Gaussian distribution.The CMA-ES guides the distribution of neighborhood structure to construct new adaptive neighborhood structure,then guides the selection of candidate solution,which can solve the problem of low convergence rate and improve the effect.The experimental result based on the 26 standard test functions shows that CLPSO+Tabu(CMA-ES) has better convergence effect than CLPSO.And it has improvement of at least one order of magnitude in six functions.

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更新日期/Last Update: 2017-05-11