Journal
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
Volume 37, Issue 4, Pages 395-413Publisher
SPRINGER
DOI: 10.1007/s00158-008-0238-3
Keywords
Constrained optimization; Hybrid evolutionary algorithm; Constraint-handling technique
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Funding
- National Natural Science Foundation of China [60234030, 60673062]
- National Basic Scientific Research Founds [A1420060159]
- Otto Monsted Fond
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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.
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