一种改进的简化粒子群优化算法
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摘要
针对粒子群优化算法(PSO)容易陷入局部极值、进化后期收敛速度慢和精度低等缺点,提出了一种改进的简化粒子群优化算法(YSPSO)。该算法采用黄金分割法平衡惯性与经验之间的相互影响;同时,为避免错过全局最优值,增加反向随机惯性权重,使粒子在一定程度上具有反向搜索的能力。最后,对几个经典基准测试函数进行实验,结果表明,YSPSO算法在提高算法收敛速度和精度的同时,降低了陷入局部极值的可能性,提高了PSO算法的实用性。
Aiming at some demerits of particle swarm optimization algorithm(PSO),such as relapsing into local extremum easily,slow convergence velocity and low convergence precision in the late evolutionary,an improved simple particle swarm optimization algorithm(YSPSO)was proposed.It employs golden section method to balance the mutual influence between inertia and experience.Meanwhile,in order to avoid missing the global optimal value,it adds reverse random inertia weights to make the particles have the ability to search reversely in a certain extent.Finally,the experiment results of several classic benchmark functions show that YSPSO improves the practicability of PSO via improving convergence velocity and precision,and reducing the possibility of relapsing into local extremum.
Aiming at some demerits of particle swarm optimization algorithm(PSO),such as relapsing into local extremum easily,slow convergence velocity and low convergence precision in the late evolutionary,an improved simple particle swarm optimization algorithm(YSPSO)was proposed.It employs golden section method to balance the mutual influence between inertia and experience.Meanwhile,in order to avoid missing the global optimal value,it adds reverse random inertia weights to make the particles have the ability to search reversely in a certain extent.Finally,the experiment results of several classic benchmark functions show that YSPSO improves the practicability of PSO via improving convergence velocity and precision,and reducing the possibility of relapsing into local extremum.
引文
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[2]Shi Y,Eberhart R.A modified particle swarm optimizer[C]∥Proceedings of IEEE International Conference on Evolutionary Computation.IEEE,1998:69-73
[3]胡旺,李志蜀.一种更简化而高效的粒子群优化算法[J].软件学报,2007,18(4):861-868
[4]周昊天,吴志勇,田雨波.简化粒子群优化方法的改进研究[J].计算机工程与应用,2012,48(24):41-44
[5]黄太安,生佳根,徐红洋,等.一种改进的简化粒子群算法[J].计算机仿真,2013,30(2):327-330,335
[6]赵志刚,黄树运,王伟倩.基于随机惯性权重的简化粒子群优化算法[J].计算机应用研究,2014,31(2):361-363,391
[7]刘瑞芳,王希云.一种混沌惯性权重的简化粒子群算法[J].计算机工程与应用,2011,47(21):58-60
[8]李鑫滨,马阳,鹿鹭.一种基于校正因子的自适应简化粒子群优化算法[J].燕山大学学报,2013,37(5):453-459
[9]任伟建,武璇.一种动态改变学习因子的简化粒子群算法[J].自动化技术与应用,2012,31(10):9-11,37
[10]郑春颖,王晓丹,郑全弟,等.自逃逸云简化粒子群优化算法[J].小型微型计算机系统,2010,31(7):1457-1460
[11]周丹,南敬昌,高明明.改进的简化粒子群算法优化模糊神经网络建模[J].计算机应用研究,2015,32(4):1000-1003
[12]熊众望,罗可.基于改进的简化粒子群聚类算法[J].计算机应用研究,2014,31(12):3550-3552
[13]刘瑞芳.混沌w的简化粒子群算法在机械设计中的应用[J].机械工程与自动化,2010,162(5):26-27
[14]田雨波.粒子群优化算法及电磁应用[M].北京:科学出版社,2014:88-95,169-245
[2]Shi Y,Eberhart R.A modified particle swarm optimizer[C]∥Proceedings of IEEE International Conference on Evolutionary Computation.IEEE,1998:69-73
[3]胡旺,李志蜀.一种更简化而高效的粒子群优化算法[J].软件学报,2007,18(4):861-868
[4]周昊天,吴志勇,田雨波.简化粒子群优化方法的改进研究[J].计算机工程与应用,2012,48(24):41-44
[5]黄太安,生佳根,徐红洋,等.一种改进的简化粒子群算法[J].计算机仿真,2013,30(2):327-330,335
[6]赵志刚,黄树运,王伟倩.基于随机惯性权重的简化粒子群优化算法[J].计算机应用研究,2014,31(2):361-363,391
[7]刘瑞芳,王希云.一种混沌惯性权重的简化粒子群算法[J].计算机工程与应用,2011,47(21):58-60
[8]李鑫滨,马阳,鹿鹭.一种基于校正因子的自适应简化粒子群优化算法[J].燕山大学学报,2013,37(5):453-459
[9]任伟建,武璇.一种动态改变学习因子的简化粒子群算法[J].自动化技术与应用,2012,31(10):9-11,37
[10]郑春颖,王晓丹,郑全弟,等.自逃逸云简化粒子群优化算法[J].小型微型计算机系统,2010,31(7):1457-1460
[11]周丹,南敬昌,高明明.改进的简化粒子群算法优化模糊神经网络建模[J].计算机应用研究,2015,32(4):1000-1003
[12]熊众望,罗可.基于改进的简化粒子群聚类算法[J].计算机应用研究,2014,31(12):3550-3552
[13]刘瑞芳.混沌w的简化粒子群算法在机械设计中的应用[J].机械工程与自动化,2010,162(5):26-27
[14]田雨波.粒子群优化算法及电磁应用[M].北京:科学出版社,2014:88-95,169-245