基于多元信息引导的人工蜂群算法

周新宇; 刘颖; 吴艳林; 郭京蕾

doi:10.12263/DZXB.20220146

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基于多元信息引导的人工蜂群算法

学术论文 | 更新时间：2025-12-08

- 基于多元信息引导的人工蜂群算法
- Artificial Bee Colony Algorithm Based on Multiple Information Guidance
- 电子学报 2024年52卷第4期页码：1349-1363
- 作者机构：
  
  1.江西师范大学计算机信息工程学院,江西南昌 330022
  2.长沙理工大学计算机与通信工程学院,湖南长沙 410114
  3.华中师范大学计算机学院,湖北武汉 430079
- 作者简介：
  
  [ "周新宇男，1987年1月出生于江西省修水县.现为江西师范大学计算机信息工程学院副教授、博士生导师.主要研究方向为智能优化算法及应用，在国内外发表学术论文40余篇. E-mail: xyzhou@jxnu.edu.cn" ]
  [ "刘颖男，1999年7月出生于江西省吉安市.2022年6月于江西师范大学获计算机科学与技术专业学士学位，现为长沙理工大学硕士研究生.主要研究方向为智能优化算法和数据中心网络.E-mail: yingliu@stu.csust.edu.cn" ]
  [ "吴艳林男，1994年12月出生于安徽省亳州市.2022年6月于江西师范大学获软件工程专业硕士学位，现为广东工业大学博士研究生.主要研究方向为智能优化算法、组合优化. E-mail: ylwucomm@foxmail.com" ]
  [ "郭京蕾女，1977年1月出生于湖北省武汉市.现为华中师范大学计算机学院副教授、硕士生导师.主要研究方向为智能优化算法及应用. E-mail: guojinglei@mail.ccnu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金项目(61966019);江西省自然科学基金项目(20192BAB207030);中央高校基本科研业务费资助项目(CCNU20TS026)
- DOI：10.12263/DZXB.20220146
  中图分类号： TP18;
- 收稿：2022-01-30，
  
  修回：2022-09-19，
  
  纸质出版：2024-04-25
- 稿件说明：
移动端阅览
周新宇,刘颖,吴艳林,等. 基于多元信息引导的人工蜂群算法[J]. 电子学报,2024,52(04):1349-1363.

ZHOU Xin-yu, LIU Ying, WU Yan-lin, et al. Artificial Bee Colony Algorithm Based on Multiple Information Guidance[J]. Acta Electronica Sinica, 2024, 52(04): 1349-1363.
周新宇,刘颖,吴艳林,等. 基于多元信息引导的人工蜂群算法[J]. 电子学报,2024,52(04):1349-1363. DOI：10.12263/DZXB.20220146

ZHOU Xin-yu, LIU Ying, WU Yan-lin, et al. Artificial Bee Colony Algorithm Based on Multiple Information Guidance[J]. Acta Electronica Sinica, 2024, 52(04): 1349-1363. DOI：10.12263/DZXB.20220146

摘要

利用优秀个体增强解搜索方程的开采能力是改进人工蜂群算法的一种主流思路.然而，现有相关工作往往仅以适应度信息作为评价个体的唯一标准，易导致算法出现早熟收敛等问题.本文提出一种多元信息引导的人工蜂群算法，分别设计了基于适应度、位置以及相似度信息的3种解搜索方程，并在雇佣蜂阶段和观察蜂阶段采用了不同的使用方式.同时，为保存侦察蜂阶段的搜索经验，采用一种微调后的邻域搜索机制用于处理被放弃蜜源.在CEC2013测试集和一个实际优化问题上进行了大量实验验证，与6种衍生算法和5种知名的相关改进人工蜂群算法进行了对比，结果表明本文算法性能竞争优势明显，在结果精度和收敛速度上均有更好表现.

Abstract

As one of the main ideas to improve the artificial bee colony (ABC) algorithm

the superior individuals are used to enhance the exploitative capability of the solution search equation. However

in the related works

the fitness information is often considered as the sole criterion for evaluating the individuals

which may easily cause some problems

e.g.

the premature convergence. In this work

an improved ABC variant is proposed based on multiple information guidance

called ABC-MIG. In ABC-MIG

three different solution search equations are designed by using the fitness

position

and similarity information

respectively

and these new solution search equations are used in different ways for the employed bee phase and onlooker bee phase. Meanwhile

to save the search experience for the scout bee phase

a modified neighborhood search strategy is used to handle the abandoned food sources. To verify the effectiveness of ABC-MIG

extensive experiments are carried out on the CEC2013 test suite and one real-world optimization problem

and six derivative algorithms and five well-known improved ABC variants are included in the performance comparison. The results confirm that ABC-MIG has very competitive performance

in terms of the result accuracy and convergence speed.

关键词

Keywords

references

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