Please wait a minute...
浙江大学学报(工学版)
计算机科学技术     
一种基于单纯形法的改进中心引力优化算法
刘杰1,2, 王宇平3
1. 西安电子科技大学 数学与统计学院,陕西 西安 710071;2. 西安科技大学 理学院,陕西 西安 710054;3. 西安电子科技大学 计算机学院,陕西 西安 710071
An improved central force optimization based on simplex method
LIU Jie1,2, WANG Yu-ping3
1. School of Mathematics and Statistics, Xi’dian University, Xi’an 710071, China; 2. College of Science, Xi’an University of Science and Technology, Xi’an 710054, China; 3. School of Computer Science and Technology, Xi’dian University, Xi’an 710071,China
 全文: PDF(2233 KB)   HTML
摘要:

针对中心引力算法无法在演化速度和求解质量之间做到有效均衡,提出一种基于单纯形法的改进中心引力算法.该算法通过周期性地把单纯形算子得到的最优个体迁移到中心引力算法的探测器种群中,达到中心引力算法和单纯形法(SM)的协同搜索:单纯形法借助中心引力算法跳出局部最优点,中心引力算法依靠单纯形法提高局部搜索能力.为了强化两种算法的作用,将改进的单纯形法应用到算法设计中,对算法的参数进行灵敏度分析,为中心引力算法的参数设置提供建议.通过6个典型的2~40维测试函数对算法进行测试,数值试验结果表明:新算法有效地克服了停滞现象,增强了全局搜索能力,与对比算法相比性能更佳.

Abstract:

Considering that the existing central force optimization (CFO) cannot achieve an effective balance between the evolution speed and the quality of solutions, an improved central force optimization based on the simplex method (SM-CFO) was introduced. By periodical migration of the best individual obtained by the SM operator into the detector population of the CFO, the proposed algorithm can achieve cooperative search of the CFO and SM: with the help of CFO, SM can get away from local minima; and with SM, CFO can improve its local exploiting capability. Furthermore, in order to enhance the ability of CFO and SM, an improved Nelder-Mead SM was proposed. Through a detailed sensitivity analysis on the parameters of the proposed algorithm, some suggestions for the parameter setting were put forward. Numerical experiments and comparisons on six 2-40 dimensional benchmark functions indicate that the proposed algorithm avoids the stagnation and enhances the global search ability, and is superior to other existing algorithms.

出版日期: 2014-12-01
:  TP 301  
基金资助:

国家自然科学基金资助项目(61272119, 11301414, 11226173)

通讯作者: 王宇平,男,教授,博导     E-mail: ywang@xidian.edu.cn
作者简介: 刘杰(1977—), 男,博士生, 讲师, 从事全局优化算法研究
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  

引用本文:

刘杰, 王宇平. 一种基于单纯形法的改进中心引力优化算法[J]. 浙江大学学报(工学版), 10.3785/j.issn.1008-973X.2014.12.003.

LIU Jie, WANG Yu-ping. An improved central force optimization based on simplex method. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 10.3785/j.issn.1008-973X.2014.12.003.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2014.12.003        http://www.zjujournals.com/eng/CN/Y2014/V48/I12/2115

[1] FORMATO R A. Central force optimization: A new metaheuristic with applications in applied electromagnetics [J]. Progress in Electromagnetics Research-PIER, 2007, 77(1): 425-449.
[2] ROBERT C G, WANG L F, ALAM M. Training neural networks using central force optimization and particle swarm optimization: insights and comparisons [J]. Expert Systems with Applications, 2012, 39(1): 555-563.
[3] MAHMOUD K R. Central force optimization: Nelder-Mead hybrid algorithm for rectangular micro strip antenna design [J]. Electromagnetics, 2011, 31(8): 8866-8872.
[4] ALI H, HELENA M R. Detection of leakage freshwater and friction factor calibration in drinking networks using central force optimization [J]. Water Resource Manage, 2012, 26(8): 2347-2363.
[5] 吴晓军,杨战中,赵明.均匀搜索粒子群算法[J].电子学报,2011, 39(6): 695-702.
WU Xiao-jun, YANG Zhan-zhong, ZHAO Ming. A uniform searching particle swarm optimization algorithm [J].Acta Electronica Sinica, 2011, 39(6): 695-702.
[6] JING C, DAVID W P. On fast and accurate block-based motion estimation algorithms using particle swarm optimization [J]. Information Sciences, 2012, 197(15): 53-64.
[7] TAVARES R F N, GODINHO M F. An ant colony optimization approach to a permutational flow shop scheduling problem with outsourcing allowed [J]. Computers & Operations Research, 2011, 38 (9):1286-1293.
[8] 仇晨晔,王春露,左兴权,等.基于K-Means全局引导策略的多目标微粒群算法[J].北京邮电大学学报, 2012, 35(5): 4953.
QIU Chen-ye, WANG Chun-lu, ZUO Xing-quan, et al. Multi-objective particle swarm optimization based on a K-means guide selection strategy [J]. Journal of Beijing University of Posts and Telecommunications, 2012, 35(5): 49-53.
[9] 雷秀娟, 黄旭, 吴爽,等. 基于连接强度的PPI 网络蚁群优化聚类算法[J].电子学报,2012, 40(4): 695-702.
LEI Xiu-juan, HUANG Xu, WU Shuang, et al. Joint strength based ant colony optimization clustering algorithm for PPI networks[J]. Acta Electronica Sinica, 2012, 40(4): 695-702.
[10] 孟超,刘三民,孙知信.中心引力算法收敛分析及在神经网络中的应用[J].软件学报,2013, 24(10): 2354-2365.
MENG Chao, LIU San-min, SUN Zhi-xin. Convergence proof for central force optimization algorithm and application in neural networks [J]. Journal of Software, 2013, 24(10): 2354-2365.
[11] 孟超, 孙知信.中心引力优化CFO 算法研究[J].电子学报, 2013,41(4): 698-703.
MENG Chao, SUN Zhi-xin. Research on central force optimization algorithm [J]. Acta Electronica Sinica, 2013, 41(4): 698-703.
[12] NELDER J A, MEAD R. A simplex method for function minimization [J]. The Computer Journal, 1965, 7(4): 308-313.
[13] ESMAT R, HOSSEIN N, SAEID S. GSA: A gravitational search algorithm [J]. Information Sciences, 2009, 179(3): 2232-2248.

[1] 毛宜钰, 刘建勋, 胡蓉, 唐明董. 基于Logistic函数和用户聚类的协同过滤算法[J]. 浙江大学学报(工学版), 2017, 51(6): 1252-1258.
[2] 张丽娜, 余阳. 海量O2O服务组合的优化[J]. 浙江大学学报(工学版), 2017, 51(6): 1259-1268.
[3] 董立岩, 朱琪, 李永丽. 基于最大共识的模型组合算法[J]. 浙江大学学报(工学版), 2017, 51(2): 416-421.
[4] 张小骏, 刘志镜, 李杰. 基于图像处理思想的激波捕捉自适应网格方法[J]. 浙江大学学报(工学版), 2017, 51(1): 89-94.
[5] 易树平, 刘觅, 温沛涵. 面向智能车间的工艺规划辅助决策方法[J]. 浙江大学学报(工学版), 2016, 50(10): 1911-1921.
[6] 过晓芳,王宇平,代才. 新的混合分解高维多目标进化算法[J]. 浙江大学学报(工学版), 2016, 50(7): 1313-1321.
[7] 张震, 潘再平, 潘晓弘. 骨干粒子群算法两种不同实现的优化特性[J]. 浙江大学学报(工学版), 2015, 49(7): 1350-1357.
[8] 苗峰,谢安桓,王富安,喻峰,周华. 多阶段可替换分组并行机调度问题的求解[J]. 浙江大学学报(工学版), 2015, 49(5): 866-872.
[9] 刘杰, 王宇平. 一种基于单纯形法的改进中心引力优化算法[J]. 浙江大学学报(工学版), 2014, 48(7): 2-.
[10] 斯袁杰,桂林,杨小虎. 模型验证中的公平性问题[J]. 浙江大学学报(工学版), 2014, 48(7): 1217-1225.
[11] 倪广翼, 章孝灿, 苏程, 俞伟斌. 基于多染色体演化的自适应类别数聚类方法[J]. 浙江大学学报(工学版), 2014, 48(6): 980-986.
[12] 孔勇奇, 潘志庚. 基于抽风矢量场的深度凹陷图像分割算法[J]. 浙江大学学报(工学版), 2014, 48(6): 1024-1033.
[13] 刘加海,杨茂林,雷航,廖勇. 共享资源约束下多核实时任务分配算法[J]. J4, 2014, 48(1): 113-117.
[14] 赵诗奎, 方水良, 顾新建. 柔性车间调度的新型初始机制遗传算法[J]. J4, 2013, 47(6): 1022-1030.
[15] 宋杰, 侯泓颖, 王智, 朱志良. 云计算环境下改进的能效度量模型[J]. J4, 2013, 47(1): 44-52.