Abstract
Genetic algorithm is widely used to solve complex optimization problems especially for the optimization of multimodal function, due to the independence, strong robustness, strong global selection and global searching ability. In order to overcome the shortcomings that standard genetic algorithm has such as relatively weak local searching ability and premature convergence is prone to occur, adaptive genetic algorithm combined with nonlinear programming method is employed into the optimization process of nonlinear functions in this paper. Simulation performance shows that the algorithm can adaptively achieve the global optimal solution and obtain more optimal solution faster than traditional genetic algorithm.
Published Version
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