Particle Swarm Optimization Based on Self-adaptive Acceleration Factors

摘要: The particle swarm optimization (PSO), which goes right after Ant Colony Algorithm, is another new intelligence algorithm. PSO has the same drawbacks as other algorithms in spite of its predominance some fields. That easily falling into local solution and low convergence velocity final stage. An improved algorithm called acceleration factors self-adaptive (ASAPSO) was proposed for drawbacks. constant coefficients standard were changed progress evolution. By controlling factors, particles have stronger global search capability early stage are less likely to be impacted by current optimum position fly more rapidly stage, thus achieved enhanced velocity. From numerous experimental results on 4 widely used benchmark functions, we can show that ASAPSO outperforms three PSO.