An Improved Glowworm Swarm Optimization Algorithm with Changing Step

LI Heng; GUO Xing; LI Wei

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An Improved Glowworm Swarm Optimization Algorithm with Changing Step

更新时间：2026-09-04

- An Improved Glowworm Swarm Optimization Algorithm with Changing Step
- Vol. 33, Issue 10, Pages: 41-45(2016)
- 作者机构：
  
  1. 安徽大学计算智能与信号处理重点实验室,安徽,合肥,230039
  2. 安徽大学计算机科学与技术学院,安徽,合肥,230601
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2016
- 稿件说明：
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李恒, 郭星, 李炜. 一种改进的变步长的萤火虫算法[J]. 微电子学与计算机, 2016, 33(10): 41-45.

LI Heng, GUO Xing, LI Wei. An Improved Glowworm Swarm Optimization Algorithm with Changing Step[J]. 2016, 33(10): 41-45.
李恒, 郭星, 李炜. 一种改进的变步长的萤火虫算法[J]. 微电子学与计算机, 2016, 33(10): 41-45. DOI：

LI Heng, GUO Xing, LI Wei. An Improved Glowworm Swarm Optimization Algorithm with Changing Step[J]. 2016, 33(10): 41-45. DOI：

摘要

为了克服人工萤火虫算法(Glowworm swarm optimization

GSO)在求解全局优化问题时

存在收敛速度慢

求解精度不高、易陷入局部最优等问题.提出了一种改进的变步长自适应的萤火虫优化算法.采用该算法可在一定程度上避免因过早的成熟陷入局部最优

并且可在后期达到更高的精确度.实验仿真证明

该算法较明显的提高了其收敛速度和精确度.

Abstract

In order to overcome the basic artificial firefly algorithm (Glowworm swarm optimization

GSO) in solving global optimization problems

slow convergence speed

precision is not high

easily falling into the master problem. So

The paper puts forward an improved variable step optimization algorithm. To some extent

adopt the algorithm can avoid premature mature into a local optimum

and can achieve a higher accuracy in the later. Through simulation experiment

proves that the algorithm on the convergence speed and precision are obviously improved.

关键词

Keywords

references

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黄凯, 周永权. 一种改进的变步长自适应GSO算法[J]. 计算机工程, 2014, 38(4): 185-187. DOI: 185

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