Performance comparison of local search operators in differential evolution for constrained numerical optimization problems

This paper analyzes the relationship between the performance of the local search operator within a Memetic Algorithm and its final results in constrained numerical optimization problems by adapting an improvement index measure, which indicates the rate of fitness improvement made by the local search operator. To perform this analysis, adaptations of Nealder-Mead, Hooke-Jeeves and Hill Climber algorithms are used as local search operators, separately, in a Memetic DE-based structure, where the best solution in the population is used to exploit promising areas in the search space by the aforementioned local search operators. The "-constrained method is adopted as a constraint-handling technique. The approaches are tested on thirty six benchmark problems used in the special session on "Single Objective Constrained Real-Parameter Optimization" in CEC\´2010. The results suggest that the algorithm coordination proposed is suitable to solve constrained problems and those results also show that a poor value of the improvement index measure does not necessarily reflect on poor final results obtained by the MA in a constrained search space.