X.-j. Bi, G.-a. Liu, and J. Li (PRC)
PSO algorithm, statistical laws, dynamic learning factor, and benchmark functions
Particle swarm optimization (PSO) algorithm has the disadvantage that, once it gets into the local optimization it is very hard to jump out from the local optimization. For that reason, a novel improved Particle Swarm Optimization algorithm is presented in this paper. The algorithm can use statistical laws of particle fitting value to classify the particles, and take different evolution models for different kinds of particles. And for the particles evolved in full model, learning factor is adjusted dynamically, which can enhance the evolution efficiency and precision greatly. By the experiments and analysis, the optimization variation rule which evolved with the learning factor is achieved, and the function expressions of learning factor C1 and C2 are given in this paper. The simulation results showed that, compared with other PSO algorithms proposed before, it is improved virtually on both optimization precision and optimization efficiency by using the improved PSO algorithm to optimize four typical benchmark functions.
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