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EN
The Differential Evolution algorithm, like other evolutionary techniques, presents as main disadvantage the high number of objective function evaluations as compared with classical methods. To overcome this disadvantage, this work proposes a new strategy for the dynamic updating of the population size to reduce the number of objective function evaluations. This strategy is based on the definition of convergence rate to evaluate the homogeneity of the population in the evolutionary process. The methodology is applied to the solution of singular optimal control problems in chemical and mechanical engineering. The results demonstrated that the methodology proposed represents a promising alternative as compared with other competing strategies.
EN
In this paper a new optimization algorithm based on Differential Evolution, non-dominated sorting strategy and neighborhood exploration strategy for guaranteeing convergence and diversity through the generation of neighborhoods of different sizes to potential candidates in the population is presented. The performance of the algorithm proposed is validated by using standard test functions and metrics commonly adopted in the specialized literature. The sensitivity analysis of some relevant parameters of the algorithm is performed and compared with the classical DE algorithm without the strategy of neighborhood exploration and with other state-of-the-art evolutionary algorithms.
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