摘要
针对传统的粒子群算法容易出现前期搜寻能力低、中期容易陷入早熟、后期在最优解附近震荡等问题。本文提出自适应更新粒子的惯性权重ω;对粒子进行排序从而可以利用更多其他粒子的有用信息;每次迭代自适应更新粒子飞行时间优化算法3种不同角度进行的优化方案,而且这3种优化方案可以结合起来同时使用。实验结果表明,本文提出的这3种优化方案对粒子的搜寻精度以及速度都有了很大的提高,尤其是自适应权重与排序优化相结合的优化方案效果非常明显,自适应的飞行时间调整方案也大幅提高了粒子前期搜寻的速率。
Traditional particle swarm optimization is prone to low pre-search capability, easy to get into early, shocks late near the optimal solution, and so on. In this paper, adaptive updated particles inertia weight ω, sorts particles which can use more useful information from other particles, each iteration adaptively updates particle flying time optimization three optimization schemes from different angles, while the optimization schemes may he combined simultaneously. Experimental results show that three kinds of particles optimization search precision and speed have been greatly improved. In particular, the combination effect of adaptive weight optimization and sorting optimization is very obvious. Adaptive flight time adjustment programs have significantly increased the rate of particle preliminary search.
出处
《计算机与现代化》
2016年第12期12-15,21,共5页
Computer and Modernization
作者简介
王叙鹏（1991-），男，江西九江人，华东师范大学计算中心硕士研究生，研究方向：计算机网络，云计算；
郑凯（1968-），男，浙江宁波人，副教授，博士，研究方向：计算机网络，云计算。
