A Cost-Benefit Local Search Coordination in Multimeme Differential Evolution for Constrained Numerical Optimization Problems Sa´ ul Dom´ ınguez-Isidro and Efr´ en Mezura-Montes University of Veracruz, Mexico Artificial Intelligence Research Center a,1 , 1,* a Sebastian Camacho No. 5 CP 91000, Xalapa, Veracruz, MEXICO Abstract 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 prob- abilistic method based on a cost-benefit scheme, and it is aimed to regulate the activation probability of every local search operator during the evolutionary cy- cle of the global search. Also, the method adopts the ε-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. Keywords: Differential Evolution, Multimeme Algorithm, Hooke-Jeeves, Hill Climbing, Nelder-Mead, Constrained Numerical Optimization Problems 1. Introduction Differential Evolution (DE), introduced by Storn and Price in [1], is one of the most popular and efficient evolutionary algorithms (EAs). DE has been * Corresponding author Email address: sdominguezisidro@gmail.com () Preprint submitted to Journal of L A T E X Templates September 22, 2017