Adaptive Inertia Weight Particle Swarm Optimization

摘要: Adaptive inertia weight is proposed to rationally balance the global exploration and local exploitation abilities for particle swarm optimization. The resulting algorithm called adaptive optimization (AIW-PSO) where a simple effective measure, individual search ability (ISA), defined indicate whether each lacks or in dimension. A transform function employed dynamically calculate values of according ISA. In iteration during run, every can choose appropriate along dimension space its own situation. By this fine strategy adjusting weight, performance PSO could be improved. order demonstrate effectiveness AIW-PSO, comprehensive experiments were conducted on three well-known benchmark functions with 10, 20, 30 dimensions. AIW-PSO was compared linearly decreasing PSO, fuzzy random number PSO. Experimental results show that achieves good outperforms other algorithms.