基于汉明距离与免疫思想的粒子群算法
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摘要
针对传统粒子群算法收敛速度慢、无法描述离散问题以及后期容易陷入局部最优解的缺陷等问题,提出一种基于汉明距离与免疫思想的改进粒子群算法(IHPSO)。首先,引入汉明距离表示位置与速度更新,使传统粒子群算法能够求解离散问题;然后,融入免疫接种、免疫选择等免疫思想,定义新的种群更新方式,解决了传统粒子群算法收敛速度慢、易陷入局部最优解的弊端;最后,通过TSP问题的模拟实验证明了改进的粒子群算法在求解速度与精度等方面均有明显提高。
An improved particle swarm optimization( IHPSO) algorithm based on hamming distance and immunity is proposed to solve the problems such as slow convergence speed of traditional particle swarm algorithm,inability to describe the properties of discrete problems and the shortcoming of local optimal solution. Firstly, the hamming distance representation position and velocity update is introduced to enable the traditional particle swarm optimization algorithm to solve discrete problems.Then,the traditional particle swarm optimization algorithm is easy to fall into the local optimal solution due to slow convergence speed. Finally,the simulation results of TSP show that the improved particle swarm optimization( pso) algorithm improves the solving speed and accuracy.
An improved particle swarm optimization( IHPSO) algorithm based on hamming distance and immunity is proposed to solve the problems such as slow convergence speed of traditional particle swarm algorithm,inability to describe the properties of discrete problems and the shortcoming of local optimal solution. Firstly, the hamming distance representation position and velocity update is introduced to enable the traditional particle swarm optimization algorithm to solve discrete problems.Then,the traditional particle swarm optimization algorithm is easy to fall into the local optimal solution due to slow convergence speed. Finally,the simulation results of TSP show that the improved particle swarm optimization( pso) algorithm improves the solving speed and accuracy.
引文
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[8]孙锋利,何明一,高全华.引入欧椋鸟群飞行机制的改进粒子群算法[J].计算机应用研究,2012,29(5):1666-1669.
[9]谢红侠,马晓伟,陈晓晓,等.基于多种群的改进粒子群算法多模态优化[J].计算机应用,2016,36(9):2516-2520.
[10]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.
[11]HIMANSHU R,ANAMIKA Y.Iris recognition using combined support vector machine and Hamming distance approac[J].Expert Systemswith Applications,2014,41(2):588-593.
[12]OSABA E,YANG X S,DIAZ F,et al.An improved discrete bat algorithm for symmetric and asymmetric traveling salesman problems[J].Engineering Applications of Artificial Intelligence,2016,48(C):59-71.
[13]杨辉,康立山,陈毓屏.一种基于构建基因库求解TSP问题的遗传算法[J].计算机学报,2003,26(12):1753-1758.
[14]邹鹏,周智,陈国良,等.求解TSP问题的多级归约算法[J].软件学报,2003,14(1):35-42.
[15]张长胜,孙吉贵,欧阳丹彤.一种自适应离散粒子群算法及其应用研究[J].电子学报,2009,37(2):299-304.
[2]YEH W.New parameter-free simplified swarm optimization for artificial neural network training and its application in the prediction of time series[J].IEEE Trans on Neural Network and Learning Systems,2013,24(4):661-665.
[3]JORDEHI A R,JASNI J,WAHAB N A,et al.Enhanced leader PSO(ELPSO):a new algorithm for allocating distributed TCSC’s in power systems[J].International Journal of Electrical Power and Energy Systems,2015,64:771-784.
[4]SCOOTT-HAYWARD S,GARCIA-PALACIOS E.Channel time allocation PSO for gigabit multimedia wireless networks[J].IEEE Trans on Multimedia,2014,16(3):828-836.
[5]GONG Y J,ZHANG J,CHUNG H S,et al.An efficient resource allocation scheme using particle swarm optimization[J].IEEE Trans on Evolutionary Computation,2012,16(6);801-816.
[6]宋书强,叶春明.一种新的自适应小生境粒子群优化算法[J].计算机仿真,2010,27(10):175-178.
[7]史哲文,白雪石,郭禾.基于改进小生境粒子群算法的多模函数优化[J].计算机应用研究,2012,29(2):465-468.
[8]孙锋利,何明一,高全华.引入欧椋鸟群飞行机制的改进粒子群算法[J].计算机应用研究,2012,29(5):1666-1669.
[9]谢红侠,马晓伟,陈晓晓,等.基于多种群的改进粒子群算法多模态优化[J].计算机应用,2016,36(9):2516-2520.
[10]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.
[11]HIMANSHU R,ANAMIKA Y.Iris recognition using combined support vector machine and Hamming distance approac[J].Expert Systemswith Applications,2014,41(2):588-593.
[12]OSABA E,YANG X S,DIAZ F,et al.An improved discrete bat algorithm for symmetric and asymmetric traveling salesman problems[J].Engineering Applications of Artificial Intelligence,2016,48(C):59-71.
[13]杨辉,康立山,陈毓屏.一种基于构建基因库求解TSP问题的遗传算法[J].计算机学报,2003,26(12):1753-1758.
[14]邹鹏,周智,陈国良,等.求解TSP问题的多级归约算法[J].软件学报,2003,14(1):35-42.
[15]张长胜,孙吉贵,欧阳丹彤.一种自适应离散粒子群算法及其应用研究[J].电子学报,2009,37(2):299-304.