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.