Full Text:   <1567>

CLC number: TP301.6

On-line Access: 2021-08-17

Received: 2020-05-19

Revision Accepted: 2020-08-27

Crosschecked: 2021-08-04

Cited: 0

Clicked: 3084

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Rafet Durgut

https://orcid.org/0000-0002-6891-5851

-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2021 Vol.22 No.8 P.1080-1091

http://doi.org/10.1631/FITEE.2000239


Improved binary artificial bee colony algorithm


Author(s):  Rafet Durgut

Affiliation(s):  Computer Engineering Department, Engineering Faculty, Karabuk University, Karabuk 78050, Turkey

Corresponding email(s):   rafetdurgut@karabuk.edu.tr

Key Words:  Artificial bee colony, Binary optimization, Uncapacitated facility location problem (UFLP)


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

@article{title="Improved binary artificial bee colony algorithm",
author="Rafet Durgut",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="22",
number="8",
pages="1080-1091",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000239"
}

%0 Journal Article
%T Improved binary artificial bee colony algorithm
%A Rafet Durgut
%J Frontiers of Information Technology & Electronic Engineering
%V 22
%N 8
%P 1080-1091
%@ 2095-9184
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000239

TY - JOUR
T1 - Improved binary artificial bee colony algorithm
A1 - Rafet Durgut
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 22
IS - 8
SP - 1080
EP - 1091
%@ 2095-9184
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000239


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.

改进的二进制人工蜂群算法

Rafet DURGUT
卡拉比克大学工程学院计算机工程系,土耳其卡拉比克,78050
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。

关键词:人工蜂群;二进制优化;无容量限制的设施选址位置问题(UFLP)

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Akbari R, Hedayatzadeh R, Ziarati K, et al., 2012. A multi-objective artificial bee colony algorithm. Swarm Evol Comput, 2:39-52.

[2]Askarzadeh A, 2016. A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct, 169:1-12.

[3]Beasley JE, 1990. OR-Library: distributing test problems by electronic mail. J Oper Res Soc, 41(11):1069-1072.

[4]Chuang LY, Chang HW, Tu CJ, et al., 2008. Improved binary PSO for feature selection using gene expression data. Comput Biol Chem, 32(1):29-38.

[5]Crawford B, Soto R, Astorga G, et al., 2017. Putting continuous metaheuristics to work in binary search spaces. Complexity, 2017:8404231.

[6]Gogna A, Tayal A, 2013. Metaheuristics: review and application. J Exp Theor Artif Intell, 25(4):503-526.

[7]Hakli H, Kiran MS, 2020. An improved artificial bee colony algorithm for balancing local and global search behaviors in continuous optimization. Int J Mach Learn Cybern, 11(9):2051-2076.

[8]He YC, Xie HR, Wong TL, et al., 2018. A novel binary artificial bee colony algorithm for the set-union knapsack problem. Fut Gener Comput Syst, 78:77-86.

[9]Holland JH, 1992. Genetic algorithms. Sci Amer, 267(1):66-73.

[10]Hussain K, Salleh MNM, Cheng S, et al., 2019. Metaheuristic research: a comprehensive survey. Artif Intell Rev, 52(4):2191-2233.

[11]Jia DL, Duan XT, Khan MK, 2014. Binary artificial bee colony optimization using bitwise operation. Comput Ind Eng, 76:360-365.

[12]Karaboga D, Basturk B, 2007. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim, 39(3):459-471.

[13]Karaboga D, Gorkemli B, 2011. A combinatorial artificial bee colony algorithm for traveling salesman problem. Int Symp on Innovations in Intelligent Systems and Applications, p.50-53.

[14]Karaboga D, Gorkemli B, Ozturk C, et al., 2014. A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev, 42(1):21-57.

[15]Kashan MH, Nahavandi N, Kashan AH, 2012. DisABC: a new artificial bee colony algorithm for binary optimization. Appl Soft Comput, 12(1):342-352.

[16]Kennedy J, Eberhart R, 1995. Particle swarm optimization. Proc Int Conf on Neural Networks, p.1942-1948.

[17]Kiran MS, 2015. The continuous artificial bee colony algorithm for binary optimization. Appl Soft Comput, 33:15-23.

[18]Kiran MS, Gündüz M, 2013. XOR-based artificial bee colony algorithm for binary optimization. Turk J Electr Eng Comput Sci, 21:2307-2328.

[19]Korkmaz S, Kiran MS, 2018. An artificial algae algorithm with stigmergic behavior for binary optimization. Appl Soft Comput, 64:627-640.

[20]Lorena AC, de Carvalho ACPLF, Gama JMP, 2008. A review on the combination of binary classifiers in multiclass problems. Artif Intell Rev, 30(1-4):19.

[21]Mallipeddi R, Suganthan PN, Pan QK, et al., 2011. Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl Soft Comput, 11(2):1679-1696.

[22]Mirjalili S, Lewis A, 2016. The whale optimization algorithm. Adv Eng Softw, 95:51-67.

[23]Mirjalili S, Mirjalili SM, Lewis A, 2014. Grey wolf optimizer. Adv Eng Softw, 69:46-61.

[24]Rajasekhar A, Lynn N, Das S, et al., 2017. Computing with the collective intelligence of honey bees—a survey. Swarm Evol Comput, 32:25-48.

[25]Rechenberg I, 1978. Evolutionsstrategien. In: Schneider B, Ranft U (Eds.), Simulationsmethoden in der Medizin und Biologie. Medizinische Informatik und Statistik, Vol 8. Springer, Berlin, Heidelberg, p.83-114.

[26]Santana CJJr, Macedo M, Siqueira H, et al., 2019. A novel binary artificial bee colony algorithm. Fut Gener Comput Syst, 98:180-196.

[27]Storn R, Price K, 1997. Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim, 11(4):341-359.

[28]Talbi EG, 2009. Metaheuristics: from Design to Implementation. John Wiley & Sons, Hoboken, New Jersey, USA.

[29]Wu GH, Mallipeddi R, Suganthan PN, 2019. Ensemble strategies for population-based optimization algorithms—a survey. Swarm Evol Comput, 44:695-711.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2022 Journal of Zhejiang University-SCIENCE