A Small-World Particle Swarm Optimization with a Rule of Degree

JIN Yan-xia; YIN Li; ZHANG Xiao-wen; ZHANG Xin

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A Small-World Particle Swarm Optimization with a Rule of Degree

更新时间：2026-09-04

- A Small-World Particle Swarm Optimization with a Rule of Degree
- Vol. 33, Issue 10, Pages: 115-119(2016)
- 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：
- DOI：
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- Published：2016
- 稿件说明：
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靳雁霞, 银莉, 张晓闻, 张鑫. 以度为规则的小世界粒子群算法[J]. 微电子学与计算机, 2016, 33(10): 115-119.

JIN Yan-xia, YIN Li, ZHANG Xiao-wen, et al. A Small-World Particle Swarm Optimization with a Rule of Degree[J]. 2016, 33(10): 115-119.
靳雁霞, 银莉, 张晓闻, 张鑫. 以度为规则的小世界粒子群算法[J]. 微电子学与计算机, 2016, 33(10): 115-119. DOI：

JIN Yan-xia, YIN Li, ZHANG Xiao-wen, et al. A Small-World Particle Swarm Optimization with a Rule of Degree[J]. 2016, 33(10): 115-119. DOI：

摘要

针对粒子群算法快速收敛

容易陷入局部最优的现象

提出了一种以度为规则的小世界粒子群算法.该算法将粒子群的分布引入小世界模型

给小世界的随机连接赋予一个规则

即将度最大的粒子和度最小的粒子进行连接

使粒子之间充分交流

避免了粒子之间信息交流不充分而出现的早熟收敛现象.通过测试函数对其进行仿真实验

实验结果表明

以度为规则的小世界粒子群算法不易陷入局部最优

能够更好地寻找最优解

其性能优于传统的粒子群算法.

Abstract

For fast convergence and prone to premature phenomenon of particle swarm optimization

proposed a small-world particle swarm optimization with a Rule of degree.The particle swarm distributions use the model of small world network. Give a rule for small-worlds random connection

the biggest degree connect to the smallest

making communication more effective between particles

avoiding rapid convergence and has a balance of diversity of particle swarm. Using the standard function to test it

the experimental results show that the particle swarm not easy to fall into local optimum and better finding optimal solution. A small-world particle swarm optimization with a rule of degree is better than the conventional particle swarm algorithm.

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references

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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, 49(8): 1-5. DOI: 1

Guangquan Xu, Chao Xu. PSO-TPS: An optimal trust path selection algorithm based on particle swarm optimization in small world network[C]// Proceedings - 2nd International Conference on Cloud and Green Computing and 2nd International Conference on Social Computing and Its Applications. Xiangtan: IEEE, 2012: 594-600.

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龚月娇, 稽智源. 自适应小世界粒子群优化算法[J]. 计算机工程与设计, 2015, 36(6): 1598-1606. DOI: 1598

Ming Li, Wenqiang Du, Fuzhong Nian. An adaptive particle swarm optimization algorithm based on directed weighted complex network[J]. Mathematical Problems in Engineering, 2014, 2014(4): 1-7. DOI: 1

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