摘要
为改善粒子群优化算法的搜索性能,提出一种飞行时间自适应调整的粒子群算法(FAA-PSO)。该算法在粒子群进化过程中随着进化代数增大自适应调整粒子的飞行时间,从而克服了传统粒子群算法中粒子飞行时间固定为1导致的粒子在迭代后期搜索性能下降的困难。数值结果表明,该算法有利于加速收敛,提高收敛精度。
To improve the searching performance of Particle Swarm Optimization （PSO）, a modified PSO algorithm with flying time adaptively adjusted was proposed and named FAA-PSO algorithm. The flying time of every particle in this algorithm was adaptively adjusted in pace with addition of the evolutionary generations; Thus, the algorithm overcomes the difficulty of the traditional PSO that the searching ability of particle is decreasing during the later time of iteration, which is caused by that the flying time of every particle is fixed on one. Numerical results show that this algorithm is of advantage to accelerate convergence and improve calculation accuracy.
出处
《计算机应用》
CSCD
北大核心
2006年第10期2513-2515,共3页
journal of Computer Applications
基金
陕西省自然科学研究项目(2003A09)
关键词
粒子群算法
进化算法
优化
Particle Swarm Optimization（PSO）
evolutionary computation
optimization
