A New Particle Swarm Optimization Algorithm with Balancing Local and Global Search Ability

XU Cong-dong; ZHANG Ji-chun; MA Peng-fei

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A New Particle Swarm Optimization Algorithm with Balancing Local and Global Search Ability

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- A New Particle Swarm Optimization Algorithm with Balancing Local and Global Search Ability
- Vol. 33, Issue 6, Pages: 134-138(2016)
- 作者机构：
  
  解放军陆军军官学院炮兵系,安徽,合肥,230031
- 作者简介：
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- Published：2016
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徐从东, 张继春, 马鹏飞. 一种平衡全局与局部搜索能力的粒子群优化算法[J]. 微电子学与计算机, 2016, 33(6): 134-138.

XU Cong-dong, ZHANG Ji-chun, MA Peng-fei. A New Particle Swarm Optimization Algorithm with Balancing Local and Global Search Ability[J]. 2016, 33(6): 134-138.
徐从东, 张继春, 马鹏飞. 一种平衡全局与局部搜索能力的粒子群优化算法[J]. 微电子学与计算机, 2016, 33(6): 134-138. DOI：

XU Cong-dong, ZHANG Ji-chun, MA Peng-fei. A New Particle Swarm Optimization Algorithm with Balancing Local and Global Search Ability[J]. 2016, 33(6): 134-138. DOI：

摘要

针对标准粒子群优化算法存在易陷入局部最优解、收敛速度慢等缺点

从两个方面对算法进行改进.一方面

改变学习因子和惯性权重

使学习因子和惯性权重随着粒子的适应度动态自适应变化

以平衡局部和全局搜索能力; 另一方面

增加粒子的学习对象

从社会心理学出发

提出向群体中所有比自身优秀的较优个体学习

以增强社会学习能力.与标准粒子群算法进行比较

实验证明新算法具有更高的收敛效率、更快的收敛速度.

Abstract

Because of two disadvantages which are the premature convergence and slow searching of particles in the standard Particle Swarm Optimization(PSO)

two aspects of improving algorithm are proposed. On the one hand

it changes the parameters value of learning factor and Inertia weight by particles fitness to balance particles local and global search ability. On the other hand

it adds learned objects including all of particles finer than itself to improve social learning ability. Contrasted to standard PSO

the experimental result of some typical testing functions proves that the new algorithm has a higher convergence efficiency and faster search speed.

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references

Kenndey J. Particle swarm optimization[C]//Proc of IEEE International Conference on Neural Networks. Perth, Australia: IEEE Press, 1995.

刘建华. 粒子群优化算法的基本理论及其改进研究[D]. 长沙: 中南大学, 2009.

Eberhart R, Kennedy J. A new optimizer using particle swarm theory[C]//Proc of the 6th International Symposium on Micro Machine and Human Science. Nagoya, Japan: IEEE Press, 1995.

John V, Trucco E, Ivekovic S. Markerless human articulated tracking using hierarchical particle swarm optimisation[J]. Image and Vision Computing, 2010, 28(11): 1530-1547. DOI: 1530

柏连发, 韩静, 张毅, 等. 采用改进梯度互信息和红外与可见光图粒子群优化算法的像配准算法[J]. 红外与激光工程, 2012, 41(1): 248-254. DOI: 248

黄艳, 臧传治, 于海斌. 基于改进粒子群优化的无线传感器网络定位算法[J]. 控制与决策, 2012, 27(1): 156-160. DOI: 156

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