A novel hybrid genetic differential evolution algorithm for constrained optimization problems

Most of the real-life applications have many constraints and they are considered as constrained optimization problems (COPs). In this paper, we present a new hybrid genetic differential evolution algorithm to solve constrained optimization problems. The proposed algorithm is called hybrid genetic differential evolution algorithm for solving constrained optimization problems (HGDESCOP). The main purpose of the proposed algorithm is to improve the global search ability of the DE algorithm by combining the genetic linear crossover with a DE algorithm to explore more solutions in the search space and to avoid trapping in local minima. In order to verify the general performance of the HGDESCOP algorithm, it has been compared with 4 evolutionary based algorithms on 13 benchmark functions. The experimental results show that the HGDESCOP algorithm is a promising algorithm and it outperforms other algorithms. Keywords—Constrained optimization problems, Genetic algorithms, Differential evolution algorithm, Linear crossover.