Adaptive Particle Swarm Optimization Algorithm with Dynamically Changing Inertia Weight

In order to get a better balance between global search ability and local search capabilities in the particle swarm algorithm, analyzed the relationship between inertia weight and the particle fitness, the population size and dimensions of the searching space, and constructed a function between them. After each iteration, updated the inertia weight of each particle as to achieved a self-adaptive adjustment of global search ability and local search capabilities. A new improved particle swarm optimization is brought up combined with population dynamic management strategy. The searching result of some standard testing functions proved that the new algorithm have a stronger global optimization capability and a higher search efficiency.