Structure of Multi-Stage Composite Genetic Algorithm (MSC-GA) and its performance

► We propose an improved genetic algorithm named Multi-Stage Composite Genetic Algorithm (MSC-GA). ► The results indicate that the new algorithm possesses several advantages such as better convergence. ► As a result, it can be applied to many large-scale optimization problems that require higher acc...

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Bibliographic Details
Published in:Expert systems with applications Vol. 38; no. 7; pp. 8929 - 8937
Main Authors: Li, Fachao, Xu, Li Da, Jin, Chenxia, Wang, Hong
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.07.2011
Subjects:
Algorithms
Convergence
Expert systems
Genetic algorithm
Genetic algorithms
Markov chain
Markov chains
Multi-Stage Composite Genetic Algorithm (MSC-GA)
Optimization
Preserving
Strategy
Markov chain
Multi-Stage Composite Genetic Algorithm (MSC-GA)
Genetic algorithm
Optimization
Convergence
ISSN:0957-4174, 1873-6793
Online Access:Get full text
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Summary:► We propose an improved genetic algorithm named Multi-Stage Composite Genetic Algorithm (MSC-GA). ► The results indicate that the new algorithm possesses several advantages such as better convergence. ► As a result, it can be applied to many large-scale optimization problems that require higher accuracy. In view of the slowness and the locality of convergence for Simple Genetic Algorithm (SGA) in solving complex optimization problems, we propose an improved genetic algorithm named Multi-Stage Composite Genetic Algorithm (MSC-GA) through reducing the optimization-search range gradually, and the structure and implementation steps of MSC-GA is also discussed. Then, we consider its global convergence under the elitist preserving strategy using the Markov chain theory and analyze its performance through three examples from different aspects. The results indicate that the new algorithm possesses several advantages such as better convergence and less chance of being trapped into premature states. As a result, it can be widely applied to many large-scale optimization problems which require higher accuracy.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2011.01.110