基于纠错机制的粒子群优化算法

张捷; 王华倩; 乔学工

doi:10.13873/j.1000-97872014.05.044

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基于纠错机制的粒子群优化算法

计算与测试 | 更新时间：2025-09-11

- 基于纠错机制的粒子群优化算法
- PSO algorithm based on error correction mechanism
- 传感器与微系统 2014年33卷第5期页码：118-120
- 作者机构：
  
  1. 太原理工大学信息与工程学院
  2. 太原理工大学电气与动力工程学院
- 作者简介：
- 基金信息：
  
  山西省自然科学基金资助项目（2012011013—5）
- DOI：10.13873/j.1000-97872014.05.044
  中图分类号： TP18
- 纸质出版：2014
- 稿件说明：
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[1]张捷,王华倩,乔学工.基于纠错机制的粒子群优化算法[J].传感器与微系统,2014,33(05):118-120.

ZHANG Jie, WANG Hua-qian, QIAO Xue-gong. PSO algorithm based on error correction mechanism[J]. 2014, 33(5): 118-120.
[1]张捷,王华倩,乔学工.基于纠错机制的粒子群优化算法[J].传感器与微系统,2014,33(05):118-120. DOI： 10.13873/j.1000-97872014.05.044.

ZHANG Jie, WANG Hua-qian, QIAO Xue-gong. PSO algorithm based on error correction mechanism[J]. 2014, 33(5): 118-120. DOI： 10.13873/j.1000-97872014.05.044.

摘要

针对标准粒子群算法存在的收敛性和收敛速度的问题

提出一种基于纠错机制的粒子群优化（MPSO）算法。该算法通过对粒子速度的更新过程引入一种简单的纠错机制

使得粒子在进化过程的每一步可能出现的错误得以及时修正

从根本上降低粒子在搜索过程中出错的概率。采用3个典型的函数进行测试

仿真结果表明:与标准粒子群算法相比

该算法有效地提高了其全局收敛能力和收敛速度。

Abstract

Aiming at problem of convergence and the convergent speed of the standard particle swarm algorithm

present a particle swarm optimization algorithm based on error correction mechanism called MPSO. A simple error correction mechanism is introduced into the update process of the particle

so that the possible error of the particle in every step of the evolutionary process can be corrected in time

and error probability of the particle in searching process can be reduced fundamentally. Using three typical functions to test and simulation results show that global convergence ability and convergence rate of the algorithm are increased effectively compared with the standard particle swarm algorithm.

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references

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