融入最佳叶节点的改进粒子群算法

靳雁霞; 张晓闻; 银莉; 张鑫

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融入最佳叶节点的改进粒子群算法

更新时间：2026-09-04

- 融入最佳叶节点的改进粒子群算法
- An Improved Particle Swarm Optimization Algorithm for Optimal Leaf Nodes
- 微电子学与计算机 2016年33卷第9期页码：64-69
- 作者机构：
  
  中北大学机构计算机与控制工程学院,山西,太原,030051
- 作者简介：
- 基金信息：
  
  国家自然科学基金项目 61379459
- DOI：
  中图分类号：
- 纸质出版：2016
- 稿件说明：
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靳雁霞, 张晓闻, 银莉, 张鑫. 融入最佳叶节点的改进粒子群算法[J]. 微电子学与计算机, 2016, 33(9): 64-69.

JIN Yan-xia, ZHANG Xiao-wen, YIN Li, et al. An Improved Particle Swarm Optimization Algorithm for Optimal Leaf Nodes[J]. 2016, 33(9): 64-69.
靳雁霞, 张晓闻, 银莉, 张鑫. 融入最佳叶节点的改进粒子群算法[J]. 微电子学与计算机, 2016, 33(9): 64-69. DOI：

JIN Yan-xia, ZHANG Xiao-wen, YIN Li, et al. An Improved Particle Swarm Optimization Algorithm for Optimal Leaf Nodes[J]. 2016, 33(9): 64-69. DOI：

摘要

针对目前传统粒子群算法早熟收敛、多维空间搜索精度不高的问题

提出了一种逐级寻优的算法

在该算法中

基于树状的拓扑结构

同时在速度更新公式中加入最优的叶子节点来平衡粒子全面搜索空间的能力.这样有效地优化了空间搜索精度的问题

又能使群中的粒子向局部最优值、全局最优值和叶子节点的最优值学习

避免早熟收敛.通过几个经典的基准测试函数仿真实验

比较了几种不同算法的效率

测试结果表明提出的新算法取得了很好的效果.将改进的算法应用于图像匹配中

提升了匹配的速度和准确率.

Abstract

The topology of the particle swarm algorithm(PSO)can directly affect the performance of the algorithm

the neighborhood structure dynamic changes of particles to reach for the purpose of changing particles topology. In order to overcome the problem of the premature and low precision of the standard PSO

we present a tree topological structure using optimal leaf node to improve velocity update formula

while these particles explore the search space more broadly. A PSO algorithm using the tree topology able to solve the problem of space searching accuracy effectively

and can make particles study to local optima

the global optima and the optima of the leaf node used to avoid premature. Through several classic benchmark test function simulation

compares the efficiency of several different algorithms

the experimental results suggest that PSO algorithms using the tree topology are able to provide superior and more consistent performance over some existing PSO algorithms. The improved algorithm is applied to image matching

and the matching speed and accuracy are improved.

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references

HUX, EBERHARTR C. Multi-objective optimization using dynamic neighborhood particle swarm optimization[C]// Proceedings of IEEE Congress on Evolution Computation. Washington, DC: IEEE, 2002: 1677-1681.

J Kennedy, R C Eberhart. Particle swarm optimization[C]//Proc IEEE Int Conf Neural Networks. Perth, Australia: IEEE, 1995: 1942-1948.

Yannis Marinakis. An improved particle swarm optimization algorithm for the capacitated location routing problem and for the location routing problem with stochastic demands[J]. Applied Soft Computing, Volume 37, December 2015, 37(12): 680-701.

Wei Hong Lim, Nor Ashidi Mat Isa. Particle swarm optimization with increasing topology connectivity[J]. Engineering Applications of Artificial Intelligence, 2014, 27(1): 80-102. DOI: 80

XIA T, WANG X, LIANG S, et al. ANN trained by PSO with adaptive genetic operator and its application[J]. Journal of PLA University of Science and Technology: Natural Science Edition, 2011, 12 (1) : 70-74. DOI: 70

朱蓉, 靳雁霞, 范卫华. 融合优质粒子分布的粒子群优化算法[J]. 小型微型计算机系统, 2015, 35(3): 576-580. DOI: 576

Mohammad Reza Bonyadi, Xiang Li, Zbigniew Michalewicz. A hybrid particle swarm with a time-adaptive topology for constrained optimization[J]. Swarm and Evolutionary Computation, 2014, 18(10): 22-37. DOI: 22

鹿艳晶, 马苗. 基于灰色粒子群优化的快速图像匹配算法[J]. 计算机工程与应用, 2009, 45(10): 157-159. DOI: 157

刘衍民, 赵庆祯, 牛奔. 基于自适应动态邻居和广义学习的改进粒子群算法[J]. 计算机应用, 2010, 30(10): 2578-2581. DOI: 2578

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