An Improved Genetic Algorithm Based On Adaptive Differential Evolution
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2016, Vol 18, Issue 1
Abstract
Abstract: In order to solve the premature convergence and improve the optimization ability of simple genetic algorithm (SGA) in the complex function optimization, an improved differential evolution based-genetic algorithm (DGA) is proposed in this paper. On the basis of the selection and crossover operation of SGA, the adaptive differential evolution operator is used as the main optimization method to realize the mutation operation. Furthermore, the optimal reservation and worst elimination strategies are added in the SGA. All of these improvements can significantly improve the optimization performance of SGA. Compared with the SGA, the simulation results of function optimization show that the proposed DGA is characterized by strong global optimization ability, quick convergence speed and good stability, which shows the validity of the improvements to simple genetic algorithm.
Authors and Affiliations
Feng Jiang, Yi Shen, Chen Zhang, Mingxin Yuan , Panpan Ge, Yi Shen, Mingxin Yuan
Keywords
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