Constrained Dynamic Differential Evolution using a novel hybrid constraint handling technique

In this paper, a Constrained Dynamic Differential Evolution (CDDE) algorithm is proposed to solve constrained optimization problems. In CDDE, the crossover rate CR and scale factor F are dynamically changed and selected randomly from the range [0.5,1]. This way, CDDE has degrees of exploration abilities for the landscape of the constrained optimization problems and can be able to discover the search space and reach the feasible regions. Also, a novel hybrid simple constraint handling technique is suggested, which combines two well-known techniques: feasible rules and adaptive penalty function. Near convergence, CDDE uses the Sequential quadratic programming (SQP) method to enhance its local search ability. CDDE performance has been tested on the constrained benchmark functions of the CEC 2010 competition. The results demonstrate that CDDE outperforms other state-of-the-art algorithms and consistently reaches feasible solutions.