An Improved Particle Swarm Algorithm Based on Adaptive Strategy
Chen Che · Jisuanji fangzhen · 2015
The parameter of speed weight plays an important role in the optimization process of PSO. How to find the parameter is a key to improve the algorithm,so an improved PSO based on adaptive strategy is proposed. When the algorithm is iterating,according to the fitness value of each particle,the speed weight of each particle is adaptively changed,so that global optimization ability and the convergence ability can be improved. The simulation results show that the improved particle swarm can fast find the best position to improve global optimization ability in the single-objective function; the improved particle swarm algorithm can quickly converge to the Pareto optimal boundary of the issue to improve convergence ability in multi-objective function.