Combining concepts of inertia weights and constriction factors in particle swarm optimization

摘要: A particle swarm optimization algorithm with global star topology designed by combining the concepts of inertia weight and constriction factor is proposed in this paper. We enhance search ability at beginning, while slowing down local when particles are near minimum linearly decreasing weight. apply a value 0.729 scales velocity step sizes, such that can move without large overshoots beginning smoothly approach goals series small steps area optimal solution prior to end iterations. For quick convergence, chosen algorithm. The simulations performed on 2 well-known benchmark functions for over 50 runs indicate population size only 20 achieve quickly accurately.