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Author(s): |
Li Li, schoool of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan Anhui, China
Kun Chen, schoool of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan Anhui, China
Haibo Hu, schoool of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan Anhui, China |
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Abstract: |
Aiming at the disadvantage of classical PSO algorithm for the optimization of complex multi- modal function. Such as relapsing into the local extremum and the low searching efficiency, a GA-PSO is proposed to overcome these demerits. The Genetic Algorithm and Particle Swarm Optimization were combined in hybrid PSO, which used the chemotactic, reproduction and elimination-dispersal of the GA and combined the merit of the few of parameter and easy to optimize, Strengthened the global search ability, enhanced the convergence effect. The simulation results demonstrate the feasibility and validity of the algorithm. Especially, this algorithm could find the global optimization effectively fast for the multi-modal function.
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