A cost-benefit local search coordination in multimeme differential evolution for constrained numerical optimization problems

This paper introduces an adaptive local search coordination for a multimeme Differential Evolution to constrained numerical optimization problems. The proposed approach associates a pool of direct local search operators within the standard Differential Evolution. The coordination mechanism consists of a probabilistic method based on a cost-benefit scheme, and it is aimed to regulate the activation probability of every local search operator during the evolutionary cycle of the global search. Also, the method adopts the ɛ-constrained method as a constraint-handling technique. The proposed approach is tested on thirty-six well-known benchmark problems. Numerical results show that the proposed method is suitable to coordinate a set of local search operators adequately within a memetic scheme for constrained search spaces.