Constrained optimization based on hybrid evolutionary algorithm and adaptive constraint-handling technique

A novel approach to deal with numerical and engineering constrained optimization problems, which incorporates a hybrid evolutionary algorithm and an adaptive constraint-handling technique, is presented in this paper. The hybrid evolutionary algorithm simultaneously uses simplex crossover and two mut...

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Bibliographic Details
Published in:Structural and multidisciplinary optimization 2009-01, Vol.37 (4), p.395-413
Main Authors: Wang, Yong, Cai, Zixing, Zhou, Yuren, Fan, Zhun
Format: Article
Language:English
Subjects:
Adaptive algorithms
Computational Mathematics and Numerical Analysis
Constraints
Crossovers
Engineering
Engineering Design
Evolutionary algorithms
Genetic algorithms
Handling
Optimization
Research Paper
Theoretical and Applied Mechanics
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Summary:A novel approach to deal with numerical and engineering constrained optimization problems, which incorporates a hybrid evolutionary algorithm and an adaptive constraint-handling technique, is presented in this paper. The hybrid evolutionary algorithm simultaneously uses simplex crossover and two mutation operators to generate the offspring population. Additionally, the adaptive constraint-handling technique consists of three main situations. In detail, at each situation, one constraint-handling mechanism is designed based on current population state. Experiments on 13 benchmark test functions and four well-known constrained design problems verify the effectiveness and efficiency of the proposed method. The experimental results show that integrating the hybrid evolutionary algorithm with the adaptive constraint-handling technique is beneficial, and the proposed method achieves competitive performance with respect to some other state-of-the-art approaches in constrained evolutionary optimization.
ISSN:1615-147X
1615-1488
DOI:10.1007/s00158-008-0238-3