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 30 条
[1]  
Back T., 1996, Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms
[2]   Closure to Discussion of "Hybrid Differential Evolution With Biogeography-Based Optimization for Solution of Economic Load Dispatch" [J].
Bhattacharya, Aniruddha ;
Chattopadhyay, Pranab Kumar .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2012, 27 (01) :575-575
[3]   Biogeography-Based Optimization for Different Economic Load Dispatch Problems [J].
Bhattacharya, Aniruddha ;
Chattopadhyay, Pranab Kumar .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2010, 25 (02) :1064-1077
[4]   A multiobjective optimization-based evolutionary algorithm for constrained optimization [J].
Cai, Zixing ;
Wang, Yong .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2006, 10 (06) :658-675
[5]   Crossover and mutation operators for grammar-guided genetic programming [J].
Couchet, Jorge ;
Manrique, Daniel ;
Rios, Juan ;
Rodriguez-Paton, Alfonso .
SOFT COMPUTING, 2007, 11 (10) :943-955
[6]   Evolutionary programming using a mixed mutation strategy [J].
Dong, Hongbin ;
He, Jun ;
Huang, Houkuan ;
Hou, Wei .
INFORMATION SCIENCES, 2007, 177 (01) :312-327
[7]   Musically meaningful fitness and mutation for autonomous evolution of rhythm accompaniment [J].
Dostal, Martin .
SOFT COMPUTING, 2012, 16 (12) :2009-2026
[8]   On the analysis of the (1+1) evolutionary algorithm [J].
Droste, S ;
Jansen, T ;
Wegener, I .
THEORETICAL COMPUTER SCIENCE, 2002, 276 (1-2) :51-81
[9]   DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization [J].
Gong, Wenyin ;
Cai, Zhihua ;
Ling, Charles X. .
SOFT COMPUTING, 2011, 15 (04) :645-665
[10]   A real-coded biogeography-based optimization with mutation [J].
Gong, Wenyin ;
Cai, Zhihua ;
Ling, Charles X. ;
Li, Hui .
APPLIED MATHEMATICS AND COMPUTATION, 2010, 216 (09) :2749-2758