Research on Particle Swarm Optimization Based on Dynamic Acceleration Coefficients

TENG Zhi-jun; LV Jin-ling; GUO Li-wen; WANG Zhi-xin; XU Heng; YUAN Li-hong

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Research on Particle Swarm Optimization Based on Dynamic Acceleration Coefficients

更新时间：2026-09-04

- Research on Particle Swarm Optimization Based on Dynamic Acceleration Coefficients
- Vol. 34, Issue 12, Pages: 125-129(2017)
- 作者机构：
  
  1. 东北电力大学机构信息工程学院,吉林,吉林,132012
  2. 国网吉林供电公司信息通信分公司,吉林,吉林,132011
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2017
- 稿件说明：
移动端阅览
滕志军, 吕金玲, 郭力文, 王志新, 许恒, 袁丽红. 基于动态加速因子的粒子群优化算法研究[J]. 微电子学与计算机, 2017, 34(12): 125-129.

TENG Zhi-jun, LV Jin-ling, GUO Li-wen, et al. Research on Particle Swarm Optimization Based on Dynamic Acceleration Coefficients[J]. 2017, 34(12): 125-129.
滕志军, 吕金玲, 郭力文, 王志新, 许恒, 袁丽红. 基于动态加速因子的粒子群优化算法研究[J]. 微电子学与计算机, 2017, 34(12): 125-129. DOI：

TENG Zhi-jun, LV Jin-ling, GUO Li-wen, et al. Research on Particle Swarm Optimization Based on Dynamic Acceleration Coefficients[J]. 2017, 34(12): 125-129. DOI：

摘要

针对固定加速因子导致粒子群算法中函数优化精度差、易于陷入局部最优、后期时收敛速率较缓慢等问题

提出一种基于动态加速因子的改进粒子群优化算法(PSO-DAC).采用递减的惯性权重系数

提高权衡局部搜索和全局搜索的能力

引入动态的加速因子

有利于全局搜索以改善粒子群算法的收敛速度及精度.借助四个常用的测试函数与标准粒子群算法进行仿真测验对比

结果显示

改进之后的算法的最优解精度明显提高于利用标准粒子群算法所得出的最优解精度

同时比标准粒子群算法迭代次数降低51.28%以上

能够更快搜索到最优解

特别是在多峰函数中表现更加明显.

Abstract

For fixed acceleration factors in the particle swarm optimization cause the function optimization accuracy poorly

easy to fall into local optimal solution and slow late convergence

this paper presents an improved particle swarm optimization base on dynamic acceleration coefficients(PSO-DAC).Adopting decreasing inertia weight coefficients improve the ability of weigh local search and global search capability.At the same time

introducing dynamic acceleration coefficients raise the convergence speed and accuracy of the particle swarm algorithm.By four commonly used benchmark functions

the improved particle swarm algorithm contrasts with the standard particle swarm optimization through simulation experiments.The experimental results show

the improved algorithm comparing with the standard particle swarm optimization has higher accuracy and reduce the number of iterations over 51.28%.The optimal solution can be found more quickly

especially in multimodal function.

关键词

Keywords

references

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