Solving constrained optimization problems with a self-adaptive differential evolution algorithm

Solving constrained optimization problems has been challenging for many decades. Although a number of evolutionary algorithms have been proposed recently for solving these problems, most of them require a careful tuning of several essential algorithmic parameters. This paper proposes a constrained self-adaptive differential evolution algorithm, named cwDE. This algorithm is an enhancement of a previous work that requires no parameter settings at all. The constraints-handling mechanism equipped into cwDE requires no additional parameters. The algorithm is described and its performance is evaluated using three real-world constrained engineering optimization problems that are widely tested in literature. The results show that the proposed algorithm is as highly competitive as other state-of-the-art algorithms for constrained optimization.