Abstract：To improve the global convergence ability and rate of particle swarm optimization, an improved particle swarm optimization algorithm based on dynamic learning factors and sharing method is proposed. The inertia weight factor of the algorithm decreases non-linearly, and the learning factor changes dynamically with the descending. A sharing fitness function is introduced on the basis of dynamic regulation. When the algorithm is stagnated without reaching termination, part of the particles will be selected according to the distance between particles and optimal solution. The chosen particles will be re-initialized as a new swarm and be evaluated by sharing fitness. The old and new swarms follow their own local solutions respectively until the end of the iteration. Simulation results of four typical multimodal functions show that the modified algorithm can greatly enhance the rate of the optimal solution searching and improve the global convergence performance of PSO.
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2016, Vol. 43 
