Self-adaptive Particle Swarm OPtimization Algorithm Based on Tentative Adjusting Step Factor
基于试探的变步长自适应粒子群算法

Aiming at premature defect and poor result of Particle Swarm Optimization algorithm, a new Self-adaptive inertia factor was designed according to diversity in the population and generation number based on analysing inertia factor's effect of algorithm. And through ploughing around adjusting step factors,the Particle's ability in local searching was enhanced. Three typical function tests were given. Comparing with APSO, the result indicates the effectiveness of this improvement.