Constrained Optimization Based on Hybrid Evolutionary Algorithm and Adaptive Constraint-Handling Technique

Yong Wang, Zixing Cai, Yuren Zhou, Zhun Fan

Research output: Contribution to journalJournal articleResearchpeer-review


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.
Original languageEnglish
JournalStructural and Multidisciplinary Optimization
Issue number4
Pages (from-to)395-413
Publication statusPublished - 2009


  • Hybrid evolutionary algorithm
  • Constrained optimization
  • Constraint-handling technique

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