Abstract To keep a balance between global searching ability and searching speed, a particle swarm optimization algorithm using dynamic neighborhood adjustment (PSODNA) is presented. According to swarm diversity variation and evolutionary state, neighborhood structure of the particle swarm is dynamically changed in PSODNA. Population entropy is introduced to estimate swarm diversity. Particle neighborhood extension factor and local effect factor are defined to describe the evolutionary state. And neighborhood expansion and constraint strategies are proposed to control the influence of good particles. The experimental results show that the proposed algorithm has great superiority both in global searching ability and searching speed.
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2010, Vol. 23 
