Abstract: Particle swarm optimization (PSO) is an intelligent algorithm which simulates the social behavior of bird swarm or fish group and has been applied widely in all kinds of fields on optimization computation．The inertia weight of PSO has employed the policy of decreasing progressively with iteration,but the variable value of inertia weight is decided in term of the experiment and rarely analyzed with theory．This paper analyzed the inertia weight of PSO with the theoretical modal．And then a kind of PSO modal with inertia weight increasing progressively with iteration was provided．The benchmark functions were used to conduct the experiment．The test results show that PSO with weight increasing is superior to the traditional PSO with weight decreasing and can match with standard PSO．

Key words: Particle swarm optimization,Progressive weight increase,Swarm intelligence,Evolutionary computation