Adaptive particle swarm optimization algorithm with dynamically changing inertia weight
Huifang Wang · Jisuanji gongcheng yu sheji · 2010
In order to get a better balance between global search ability and local search capabilities in the particle swarm algorithm,the relationships between inertia weight and the particle fitness,the population size and dimensions of the searching space are analyzed,and a function is constructed between them.After each iteration,the inertia weight of each particle is updated as to achieved a self-adaptive adjustment of global search ability and local search capabilities.A new improved particle swarm optimization is brought forward combined with population dynamic management strategy.The searching result of some standard testing functions proves that the new algorithm have a stronger global optimization capability and a higher search efficiency.