摘要
针对粒子群算法易陷入局部最优的问题,结合雁群启示粒子群算法和扩展粒子群算法提出了基于雁群启示的扩展粒子群(Ge EPSO)算法。该算法在利用雁群飞行方向的多样性同时融合了所有粒子的个体极值信息,提高了种群多样性。为进一步提高改进算法的收敛速度,引入简化粒子群提出了Ge ESPSO算法。基准函数的仿真表明:改进算法Ge ESPSO较好地平衡了收敛速度和局部最优两个矛盾,总体较优。为进一步验证算法在实际应用中的有效性,又分别用两种改进算法优化BP神经网络,并用相关气象数据对PM2. 5的值进行预测。
To solve the local minimum problem of PSO, a new algorithm called GeEPSO is proposed, which hybrids the PSO based on the flight of geese and the extended PSO algorithm. The GeEPSO take the advantage of the various directions of geese and make full use of the position of each particle to assure the diversity of particle swarm. And to further improve the convergence performance, the simplified PSO is introduced to obtain another new algorithm GeESPSO. The simulation results of some benchmarks demonstrate the GeEPSO algorithm can deal well with the conflict between the convergence rate and the local minimum problem, which is better on the whole. And to further investigate the effect of the proposed algorithms in practical application, the new algorithms were used to optimize the BP neural network. Finally the prediction of PM2.5 is made with the improved BP neural network, using the meteorological data.
作者
刘浩然
崔静闯
卢泽丹
郭长江
丁攀
LIU Hao-ran;CUI Jing-chuang;LU Ze-dan;GUO Chang-jiang;DING Pan(Hebei Province Key Laboratory of Special Optical Fiber & Optical Fiber Sensing, Qinhuangdao, Hebei 066004, China;Information Science and Engineering College of Yanshan University, Qinhuangdao, Hebei 066004, China;Liren College of Yanshan University, Qinhuangdao, Hebei 066004, China)
出处
《计量学报》
CSCD
北大核心
2019年第3期498-504,共7页
Acta Metrologica Sinica
基金
国家自然科学基金(51641609)
