Drift analysis of mutation operations for biogeography-based optimization

被引:14
|
作者
Guo, Weian [1 ,4 ,5 ]
Wang, Lei [2 ,3 ]
Ge, Shuzhi Sam [4 ]
Ren, Hongliang [5 ]
Mao, Yanfen [1 ]
机构
[1] Tongji Univ, Sino German Coll Appl Sci, Shanghai 201804, Peoples R China
[2] Shanghai Univ Finance & Econ, Shanghai Key Lab Financial Informat Technol, Shanghai 200433, Peoples R China
[3] Tongji Univ, Sch Elect & Informat, Shanghai 201804, Peoples R China
[4] Natl Univ Singapore, Interact Digital Media Inst, Social Robot Lab, Elect & Comp Engn, Singapore 119077, Singapore
[5] Natl Univ Singapore, Dept Biomed Engn, Singapore 117575, Singapore
基金
中国国家自然科学基金;
关键词
Evolutionary algorithm; Mutation operator; Migration operator; Biogeography-based optimization; Drift analysis; Expected first hitting time; EVOLUTIONARY ALGORITHM; ABLATION; MODELS; RATES; TIME;
D O I
10.1007/s00500-014-1370-1
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
As an essential factor of evolutionary algorithms (EAs), mutation operator plays an important role in exploring the search space, maintaining the diversity of individuals and breaking away local optimums. In most standard evolutionary algorithms, the mutation operator is independent from the recombination operator. Nevertheless, in biogeography-based optimization (BBO), the mutation operator is affected not only by predefined constants but also by recombination models, namely the migration operator. However to date, the relationship between the mutation and migration has never been investigated. To reveal the relationship and evaluate the mutation models, we utilize drift analysis to investigate the expected first hitting time of BBO with different migration models. The analysis compares three different kinds of mutation models in a mathematical way and the conclusion is helpful for designing migration models of BBO. The simulation results are also in agreement with our analysis.
引用
收藏
页码:1881 / 1892
页数:12
相关论文
共 50 条
  • [1] Drift analysis of mutation operations for biogeography-based optimization
    Weian Guo
    Lei Wang
    Shuzhi Sam Ge
    Hongliang Ren
    Yanfen Mao
    Soft Computing, 2015, 19 : 1881 - 1892
  • [2] A real-coded biogeography-based optimization with mutation
    Gong, Wenyin
    Cai, Zhihua
    Ling, Charles X.
    Li, Hui
    APPLIED MATHEMATICS AND COMPUTATION, 2010, 216 (09) : 2749 - 2758
  • [3] Intelligent Mutation Operator of Biogeography-Based Optimization Algorithm
    Zhu, Chao
    Yang, Bo
    He, He
    Wu, Anqi
    Zheng, Qi
    2016 2ND INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS - COMPUTING TECHNOLOGY, INTELLIGENT TECHNOLOGY, INDUSTRIAL INFORMATION INTEGRATION (ICIICII), 2016, : 311 - 314
  • [4] Biogeography-Based Optimization
    Simon, Dan
    IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2008, 12 (06) : 702 - 713
  • [5] Modified Blended Migration and Polynomial Mutation in Biogeography-Based Optimization
    Bansal, Jagdish Chand
    HARMONY SEARCH ALGORITHM, 2016, 382 : 217 - 225
  • [6] An analysis of the migration rates for biogeography-based optimization
    Guo, Weian
    Wang, Lei
    Wu, Qidi
    INFORMATION SCIENCES, 2014, 254 : 111 - 140
  • [7] Variations of biogeography-based optimization and Markov analysis
    Ma, Haiping
    Simon, Dan
    Fei, Minrui
    Xie, Zhikun
    INFORMATION SCIENCES, 2013, 220 : 492 - 506
  • [8] Metropolis biogeography-based optimization
    Al-Roomi, Ali R.
    El-Hawary, Mohamed E.
    INFORMATION SCIENCES, 2016, 360 : 73 - 95
  • [9] Localized biogeography-based optimization
    Zheng, Yu-Jun
    Ling, Hai-Feng
    Wu, Xiao-Bei
    Xue, Jin-Yun
    SOFT COMPUTING, 2014, 18 (11) : 2323 - 2334
  • [10] Localized biogeography-based optimization
    Yu-Jun Zheng
    Hai-Feng Ling
    Xiao-Bei Wu
    Jin-Yun Xue
    Soft Computing, 2014, 18 : 2323 - 2334