A discrete artificial bee colony algorithm for quadratic assignment problem

Issue title: New Techniques for Intelligent Networks with Machine Learning

Guest editors: Nan Jiang, Khaled Riad and Weiwei Lin

Article type: Research Article

Authors: Peng, Zhong-yuan^a | Huang, Yun-ji^a | Zhong, Yu-bin^{b; *}

Affiliations: [a] Department of Social Sciences, Maoming Polytechnic, Maoming, Guangdong, P. R. China | [b] School of Mathematics and Information Sciences, Guangzhou University Guangzhou, Guangdong, P. R. China

Correspondence: [*] Corresponding author. E-mail: zhong [email protected].

Abstract: The quadratic assignment problem (QAP) is a well-known challenging combinational optimization problem that has received many researchers’ attention with varied real-world and industrial applications areas. Using the framework of basic artificial bee colony algorithm, frequently used crossover and mutation operators, and combined with an effective local search method, this paper proposes a simple but effective discrete artificial bee colony (DABC) algorithm for solving quadratic assignment problems (QAPs). Typical QAP benchmark instances are selected from QAPLIB in order to conduct the simulation experiment where common performance metrics are used to evaluate the algorithm. The paper also investigates the influence factors of the algorithm’s performance. The results show that the proposed algorithm is a quite effective and practical new approach for handling QAP problems.

Keywords: Discrete artificial bee colony algorithm, combinatorial optimization, Quadratic Assignment Problem

DOI: 10.3233/JHS-220684

Journal: Journal of High Speed Networks, vol. 28, no. 2, pp. 131-141, 2022