Abstract The standard particle swarm optimization is easy to have problems of low convergence speed and precision, prematurity and poor exploring ability during later period. Aiming at these problems, an optimized particle swarm optimization based on improved pigeon search operator is proposed. Population initialization is determined by Beta opposition-based learning strategy, and the diversity of population distribution is realized. The map compass operator is improved by the linear and nonlinear mutation strategy to improve the development and the exploration ability of the pigeon-inspired algorithm. Then, the location and the speed are updated by the improved combination optimization operator to speed up the convergence, enhance the precision and avoid falling into local optimal solution in the particle swarm optimization. Simulation experimental results show that the convergence speed is improved by IPSO, and the accuracy reaches the ideal value set by the functions.
Fund:Supported by National Natural Science Foundation of China(No.51774228), National Safety Production Technology Foundation of Major Accidents Prevention Key Technology of China(No.0020-2018AQ), Social Science Foundation of Shannxi Province(No.Z20180285), Foundation of Shannxi Educational Committee(No.17JK0425)
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2018, Vol. 31 
