融合高斯变异和Powell法的花朵授粉优化算法

E-mail: fcst@ Tel: +8ell 法的花朵授粉优化算法*
肖辉辉 1,2， 万常选 1+， 段艳明 2， 喻 聪1
1. 江西财经大学 信息管理学院， 南昌 330013 2. 河池学院 计算机与信息工程学院， 广西 宜州 546300
ISSN 1673-9418 CODEN JKYTA8 Journal of Frontiers of Computer Science and Technology 1673-9418/2017/11(03)-0478-13 doi: 10.3778/j.issn.1673-9418.1601003
肖辉辉 等： 融合高斯变异和 Powell 法的花朵授粉优化算法
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mance of the FPA, firstly, this paper modifies the scaling factor of the control step size. Secondly, this paper proposes a hybrid algorithm GMPFPA (flower pollination algorithm combination with Gauss mutation and Powell search method). The Gauss mutation is utilized to perturb the global search of the GMPFPA, which enhances the diversity of population of the GMPFPA, and improves the global detection ability of the GMPFPA, and then the strong local search capability of the Powell search method is introduced to enhance the local development ability of the hybrid algorithm. Through the comparison experiment of 12 high dimensional classical test functions, the effectiveness and superiority of the improved algorithm are verified. Key words: Gauss mutation; flower pollination algorithm (FPA); Powell method; optimum value; optimization ability 摘 要： 花朵授粉算法 （flower pollination algorithm， FPA） 是最近提出的一种新型群智能优化算法， 由于其较
Flower Pollination Algorithm Combination with Gauss Mutation and Powell Search Method������
XIAO Huihui1,2, WAN Changxuan1+, DUAN Yanming2, YU Cong1
1. School of Information and Technology, Jiangxi University of Finance and Economics, Nanchang 330013,China 2. College of Computer and Information Engineering, Hechi University, Yizhou, Guangxi 546300,China + Corresponding author: E-mail: wanchangxuan@ XIAO Huihui, WANG Changxuan, DUAN Yanming, et al. Flower pollination algorithm combination with Gauss mutation and Powell search method. Journal of Frontiers of Computer Science and Technology, 2017, 11(3)： 478-490. Abstract: Flower pollinate algorithm (FPA) is a novel swarm intelligence optimization algorithm which is proposed recently, and it has been widely researched and used because of its advantages of solving the balance problem of local search and global search, and having less parameters, being implemented easily and so on. However, there are less current researches on the parameter, and the speed of convergence in the later stage is slow, what is more, it is easy to fall into local optimizations, which incline to restrict the application of the FPA. In order to improve the overall perfor-
* The National Natural Science Foundation of China under Grant No. F020204 ( 国家自然科学基金); the Natural Science Foundation of Guangxi under Grant No. 2013GXNSFBA019022 ( 广西自然科学基金); the University Science Foundation of Guangxi under Grant Nos. KY2015LX332, KY2015LX334 ( 广 西 高 校 科 学 技 术 研 究 项 目); the Graduate Innovation Project of Jiangxi Province under Grant No. YC2015-B054 ( 江西省研究生创新项目); the Project of Key Laboratory for Computer Network and New Technology of Software of Hechi University under Grant No. 2013-03 ( 河池学院计算机网络与软件新技术重点实验室资助项目); the Education Reform Project of Hechi University under Grant No. 2014EB022 ( 河池学院教改项目); the Science Foundation of Hechi University under Grant No. XJ2015QN003 (河池学院基金项目). Received 2016-01, Accepted 2016-03. CNKI 网络优先出版: 2016-03-07, /kcms/detail/11.5602.TP.20160307.1710.016.html