A real-coded biogeography-based optimization with mutation

Biogeography-based optimization (BBO) is a new biogeography inspired algorithm for global optimization. There are some open research questions that need to be addressed for BBO. In this paper, we extend the original BBO and present a real-coded BBO approach, referred to as RCBBO, for the global opti...

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Veröffentlicht in:Applied mathematics and computation Jg. 216; H. 9; S. 2749 - 2758
Hauptverfasser: Gong, Wenyin, Cai, Zhihua, Ling, Charles X., Li, Hui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier Inc 01.07.2010
Elsevier
Schlagworte:
Algorithms
Benchmarking
Biogeography-based optimization
Calculus of variations and optimal control
Evolutionary programming
Exact sciences and technology
Exploration
Exploration ability
Global optimization
Mathematical analysis
Mathematical models
Mathematics
Mutation
Mutations
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in mathematical programming, optimization and calculus of variations
Numerical methods in optimization and calculus of variations
Operators
Optimization
Sciences and techniques of general use
Global optimization
Mutation
Evolutionary programming
Biogeography-based optimization
Exploration ability
Experimental result
Numerical analysis
Applied mathematics
Optimization method
Variational calculus
Algorithm
Mathematical programming
ISSN:0096-3003, 1873-5649
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:Biogeography-based optimization (BBO) is a new biogeography inspired algorithm for global optimization. There are some open research questions that need to be addressed for BBO. In this paper, we extend the original BBO and present a real-coded BBO approach, referred to as RCBBO, for the global optimization problems in the continuous domain. Furthermore, in order to improve the diversity of the population and enhance the exploration ability of RCBBO, the mutation operator is integrated into RCBBO. Experiments have been conducted on 23 benchmark problems of a wide range of dimensions and diverse complexities. The results indicate the good performance of the proposed RCBBO method. Moreover, experimental results also show that the mutation operator can improve the performance of RCBBO effectively.
Bibliographie:ObjectType-Article-2
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ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2010.03.123