Improved binary artificial bee colony algorithm

Rafet DURGUT

doi:10.1631/FITEE.2000239

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Improved binary artificial bee colony algorithm

Regular Papers | Updated：2022-05-19

- Improved binary artificial bee colony algorithm
- 改进的二进制人工蜂群算法
- Frontiers of Information Technology & Electronic Engineering Vol. 22, Issue 8, Pages: 1080-1091(2021)
- Affiliations：
  
  Computer Engineering Department, Engineering Faculty, Karabuk University, Karabuk 78050, Turkey
- Author bio：
  
  [ "Rafet DURGUT, E-mail: rafetdurgut@karabuk.edu.tr" ]
- Funds：
- DOI：10.1631/FITEE.2000239
  CLC： TP301.6
- Received：19 May 2020，
  
  Revised：2021-;8-;4，
  
  Published：2021-08
- Accepted：
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Rafet DURGUT. Improved binary artificial bee colony algorithm[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(8): 1080-1091.
DOI：

Rafet DURGUT. Improved binary artificial bee colony algorithm[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(8): 1080-1091. DOI： 10.1631/FITEE.2000239.

摘要

人工蜂群算法是一种基于群体智能并受蜜蜂觅食行为启发的演变优化算法。由于人工蜂群算法已被开发用于搜索连续的搜索空间来获得最优解，因此需要对其进行修改以应用于二进制优化问题。本文修改了人工蜂群算法来解决二进制优化问题，并将其命名为改进的二进制人工蜂群算法。提出的方法包括基于适应值和不同决策变量选择的更新机制。因此，我们的目标是通过增加探索能力来防止人工蜂群算法陷入局部最小值。将改进的二进制人工蜂群算法与人工蜂群算法的3种变体和其他文献中的启发式算法进行了比较，并使用了大家熟知的OR-Library数据集，其中包含为无容量限制的设施选址位置问题准备的15个问题实例。计算结果表明，该算法在收敛速度和鲁棒性方面均优于其他算法。可通过https://github.com/rafetdurgut/ibinABC获取算法源码。

Abstract

The artificial bee colony (ABC) algorithm is an evolutionary optimization algorithm based on swarm intelligence and inspired by the honey bees' food search behavior. Since the ABC algorithm has been developed to achieve optimal solutions by searching in the continuous search space

modification is required to apply it to binary optimization problems. In this study

we modify the ABC algorithm to solve binary optimization problems and name it the improved binary ABC (IbinABC). The proposed method consists of an update mechanism based on fitness values and the selection of different decision variables. Therefore

we aim to prevent the ABC algorithm from getting stuck in a local minimum by increasing its exploration ability. We compare the IbinABC algorithm with three variants of the ABC and other meta-heuristic algorithms in the literature. For comparison

we use the well-known OR-Library dataset containing 15 problem instances prepared for the uncapacitated facility location problem. Computational results show that the proposed algorithm is superior to the others in terms of convergence speed and robustness. The source code of the algorithm is available at https://github.com/rafetdurgut/ibinABC.

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references

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