Particle Swarm Optimization Algorithm Based on Labor Division and Fuzzy Control
In order to overcome the shortages of the particle swarm optimization algorithm,such as low accuracy,slow convergence and falling into local optima,a particle swarm optimization al-gorithm based on labor division and fuzzy control is proposed,which improves the algorithm by using the division of labor,parameter adaptive adjustment and simulated annealing with distance factors.Particles are divided into scout and rearguard ones,the former searches randomly and the latter learns from the best individual solutions as well as the best global solution to ensure the diversity of population and to accelerate the search.A sigmoid function is used to adjust the iner-tial weight and fuzzy logic is applied to balance exploration and exploitation capability of the algo-rithm.The best global particle is updated according to simulated annealing with distance factors taken into account,which improves the ability of the algorithms to jump out of the local optima.Simulation experiments on 25 standard test functions show that the improved algorithm has bet-ter performance in terms of convergence accuracy,speed and stability.
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