A self-adaptive combined strategies algorithm for constrained optimization using differential evolution

There are a huge number of differential evolution variants that have been proposed in the literature for solving constrained problems. However, none of them was considered as being a well-accepted approach for solving a broad range of problems with different mathematical properties. Therefore, in this paper, for a better coverage of the problem characteristics, a self-adaptive differential evolution algorithm is introduced. To do that, it uses multiple search operators in conjunction with multiple constraint handling techniques. The need for such an approach is justified by experimental analysis on a well-known set of problems. The results show that the proposed algorithm is superior to other state-of-the-art algorithms.