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 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. Y. Wang (B) · Z. Cai School of Information Science and Engineering, Central South University, Changsha 410083, People’s Republic of China e-mail: [email protected] Y. Zhou School of Computer Science and Engineering, South China University of Technology, Guangzhou 516040, People’s Republic of China Z. Fan Department of Management Engineering, Technical University of Denmark, Lyngby DK-2800, Denmark