Memetic differential evolution for constrained numerical optimization problems

This paper presents a memetic algorithm for solving constrained numerical optimization problems. The proposed approach uses differential evolution as a global search algorithm, which was improved with a mathematical programming method called Powell´s conjugate direction as a local search operator. To the best of the authors´ knowledge, this is the first attempt to use such mathematical programming method within differential evolution for constrained optimization. The proposed algorithm was tested on 36 test problems used in the special session on “Single Objective Constrained Real-Parameter Optimization” in CEC´2010. The proposed algorithm is able to find competitive results with respect to the winner algorithm in that session.