Migration Ratio Model Analysis of Biogeography-Based Optimization Algorithm and Performance Comparison

被引:8
作者
Wang, Jie-sheng [1 ]
Song, Jiang-di [2 ]
机构
[1] Univ Sci & Technol Liaoning, Natl Financial Secur & Syst Equipment Engn Res Ct, Anshan, Liaoning Provin, Peoples R China
[2] Univ Sci & Technol Liaoning, Sch Elect & Informat Engn, Anshan 114044, Liaoning Provin, Peoples R China
基金
国家科技攻关计划; 中国国家自然科学基金;
关键词
Biogeography-based optimization algorithm; migration ratio model; function optimization; performance comparison; BEE COLONY ALGORITHM; DIFFERENTIAL EVOLUTION; EFFICIENT ALGORITHM;
D O I
10.1080/18756891.2016.1175817
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Biogeography-based optimization (BBO) algorithm is based on species migration between habitats to complete information circulation and sharing, which achieves the global optimization by improving the adaptability of habitats. In this paper, the basic migration balance model of biogeography theory is elaborated. Based on the population adaptive migration mechanism of BBO algorithm, the algorithm procedure is set up. Seven linear or nonlinear migration ratio models (including three new migration ratio models) are described. Simulation experiments are carried out on eight testing functions to verify the proposed migration ratio models. Simulation results show that different migration ratio model has different influence on the optimization performance of BBO algorithm, in which the sine migration ratio model has the best optimization performance. This also represents that the nonlinear migration ratio model close to the natural laws outperforms other simple linear migration ratio models.
引用
收藏
页码:544 / 558
页数:15
相关论文
共 21 条
[1]   Hybrid Differential Evolution With Biogeography-Based Optimization for Solution of Economic Load Dispatch [J].
Bhattacharya, Aniruddha ;
Chattopadhyay, Pranab Kumar .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2010, 25 (04) :1955-1964
[2]   Hybridizing Biogeography-Based Optimization With Differential Evolution for Optimal Power Allocation in Wireless Sensor Networks [J].
Boussaid, Ilhem ;
Chatterjee, Amitava ;
Siarry, Patrick ;
Ahmed-Nacer, Mohamed .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2011, 60 (05) :2347-2353
[3]  
Ergezer M, 2011, IEEE C EVOL COMPUTAT, P1496
[4]   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
[5]   A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm [J].
Karaboga, Dervis ;
Basturk, Bahriye .
JOURNAL OF GLOBAL OPTIMIZATION, 2007, 39 (03) :459-471
[6]  
Kennedy James., 2010, Particle Swarm Optimization, P760
[7]   Development and investigation of efficient artificial bee colony algorithm for numerical function optimization [J].
Li, Guoqiang ;
Niu, Peifeng ;
Xiao, Xingjun .
APPLIED SOFT COMPUTING, 2012, 12 (01) :320-332
[8]   A hybrid niching PSO enhanced with recombination-replacement crowding strategy for multimodal function optimization [J].
Li, Minqiang ;
Lin, Dan ;
Kou, Jisong .
APPLIED SOFT COMPUTING, 2012, 12 (03) :975-987
[9]   Genetic Algorithm with adaptive elitist-population strategies for multimodal function optimization [J].
Liang, Yong ;
Leung, Kwong-Sak .
APPLIED SOFT COMPUTING, 2011, 11 (02) :2017-2034
[10]   An analysis of the equilibrium of migration models for biogeography-based optimization [J].
Ma, Haiping .
INFORMATION SCIENCES, 2010, 180 (18) :3444-3464