结合禁忌搜索策略的邻居结构粒子群优化算法
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摘要
提出了一种结合禁忌搜索策略的邻居结构粒子群优化算法。将粒子组成邻居结构,为了避免算法陷入局部最优,在粒子群算法中,引入了禁忌搜索策略,对当前适应值差的粒子进行替换,获得全局最优值。通过2个标准测试函数优化,与其他优化算法比较。可以看出,该算法能够明显提升粒子群算法的寻优性能。
This study proposes a particle swarm optimization( PSO) algorithm based on the neighborhood structure of Tabu search strategy. In order to avoid the algorithm falling into local optimum,Tabu search strategy is introduced in particle swarm optimization,which replaces the particles with poor fitness and obtains the global optimum. Two standard test functions are optimized and compared with other optimization algorithms. It can be seen that the algorithm can significantly improve the performance of particle swarm optimization.
This study proposes a particle swarm optimization( PSO) algorithm based on the neighborhood structure of Tabu search strategy. In order to avoid the algorithm falling into local optimum,Tabu search strategy is introduced in particle swarm optimization,which replaces the particles with poor fitness and obtains the global optimum. Two standard test functions are optimized and compared with other optimization algorithms. It can be seen that the algorithm can significantly improve the performance of particle swarm optimization.
引文
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[12]刘角,马迪,马腾波,等.基于食物链机制的动态多物种粒子群算法[J].计算机应用,2016,36(05):1341-1346.
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[14]王明慧,戴月明,田娜,等.基于冯诺依曼拓扑结构的骨干粒子群优化算法[J].计算机工程与科学,2017,39(08):1552-1561.
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[16]李勇刚,邓艳青.结合禁忌搜索的改进粒子群优化算法[J].计算机工程,2012,38(18):155-157.
[17]赵鹤群,王鹏宇,李磊.禁忌搜索算法的参数选择及其收敛特性分析[J].自动化技术与应用,2013,32(02):28-31.
[18]李佳,刘天琪,李兴源,等.改进粒子群-禁忌搜索算法在多目标无功优化中的应用[J].电力自动化设备,2014,34(08):71-77.
[2] SHIYH. EBERHART R C. A modified particle swarm optimizer[C]//Proceedings of the 1998 IEEE International Conference on Evolutionary Computation. Piscataway,NJ:IEEE,1998:67-73.
[3] CAMPOSM,KROHLINGR A Hierarchical bare bones particle swarm for solving constrained optimizationproblems[C]//Proceedings of the 2013IEEE Congress on Evolutionary Computation. Piscataway,NJ:IEEE,2013:805-812.
[4] CAMPOS M,KROHLINGRA. Bare bones particle swarm with scale mixture of Gaussians for dynamic constrained optimization[C]//Proceedings of the 2014 IEEE Congress on Evolutionary Computation. Piscataway,NJ:IEEE,2014:202-209.
[5] ZHANG Y,GONG D,HU Y,et al. Feature selection algorithm based on bare bones particle swarm optimization[J]. Neurocomputing,2015,(148):150-157.
[6]曹春红,王利民,赵大哲,等.基于小生境改进粒子群算法的几何约束求解[J].仪器仪表学报,2012,33(09):2125-2129.
[7]唐贤伦,张衡,李进,等.基于多Agent粒子群优化算法的电力系统经济负荷分配[J].电力系统保护与控制,2012,40(10):42-47.
[8] KOSHTI A,ARYA L D,CHOUBE S C. Voltage stability constrained distributed generation planning using modified bare bones particle swarm optimization[J]. Journal of the Institution of Engineers(India)Series B(Electrical,Electronics&Telecommunication and Computer Engineering),2013,94(02):123-133.
[9] Suganthan P N. Particle swarm optimizer with neiguborhood operator[C]//In:Proc. of the IEEE CEC. 1999:1958-1961.
[10]石松,陈云.层次环形拓扑结构的动态粒子群算法[J].计算机工程与应用,2013,(08):51-54.
[11]倪庆剑,张志政,邢汉承.一种基于可变多簇结构的动态概率粒子群优化算法[J].软件学报,2009,20(02):340-348.
[12]刘角,马迪,马腾波,等.基于食物链机制的动态多物种粒子群算法[J].计算机应用,2016,36(05):1341-1346.
[13]汤可宗,柳炳祥,杨静宇,等.双中心粒子群优化算法[J].计算机研究与发展,2012,49(05):1086-1094.
[14]王明慧,戴月明,田娜,等.基于冯诺依曼拓扑结构的骨干粒子群优化算法[J].计算机工程与科学,2017,39(08):1552-1561.
[15]刘林炬.引入禁忌搜索的双种群粒子群算法及其应用研究[D].江苏:江南大学,2008.
[16]李勇刚,邓艳青.结合禁忌搜索的改进粒子群优化算法[J].计算机工程,2012,38(18):155-157.
[17]赵鹤群,王鹏宇,李磊.禁忌搜索算法的参数选择及其收敛特性分析[J].自动化技术与应用,2013,32(02):28-31.
[18]李佳,刘天琪,李兴源,等.改进粒子群-禁忌搜索算法在多目标无功优化中的应用[J].电力自动化设备,2014,34(08):71-77.