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This paper analyzes the influence of the depth of direct local search methods in constrained numerical optimization problems in order to use as a local search operator (LSO) within a memetic algorithm. To perform this study, five direct local search methods (Random Walk, Simulated Annealing, Nelder-Mead, Hooke-Jeeves, and Hill Climber) are implemented separately to analyze their behavior within constrained...
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...
This paper presents a memetic approach combining the Modified Artificial Bee Colony algorithm (MABC) and the Hooke-Jeeves method to improve its performance to solve constrained numerical optimization problems. The operator used by the employed bees was modified in such a way that more diverse solutions are generated. For constraint handling, the set of feasibility rules used in the original MABC was...
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...
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