Abstract
The Rat Swarm Optimization (RSO) algorithm is a nature-inspired optimization method that has been shown to be effective in solving continuous and discrete problems. After demonstrating its effectiveness in solving the well-known discrete traveling salesman problem, we aim to apply the RSO algorithm to another complex problem, the Quadratic Assignment Problem (QAP). The QAP is an NP-hard combinatorial problem that seeks to minimize the total cost of constructing and operating facilities, where the benefit of economic activity at any site depends on the presence of other facilities. We evaluated the proposed RSO algorithm on a set of benchmark instances from the QAPLIB library and compared its performance to other algorithms. Our results are encouraging and demonstrate the effectiveness of the proposed approach.
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References
Gao XZ, Govindasamy V, Xu H, Wang X, Zenger K (2015) Harmony search method: theory and applications. Comput Intell Neurosci 2015:1–10. https://doi.org/10.1155/2015/258491
Golabian H, Arkat J, Tavakkoli-Moghaddam R, Faroughi H (2021) A multi-verse optimizer algorithm for ambulance repositioning in emergency medical service systems. J Ambient Intell Humaniz Comput 13(1):549–570. https://doi.org/10.1007/s12652-021-02918-2
Arnold DV, Beyer H-G (2002) Noisy optimization with evolution strategies. Springer Science & Business Media
Barbarosoglu G, Ozgur D (1999) A tabu search algorithm for the vehicle routing problem. Comput Oper Res 26(3):255–270. https://doi.org/10.1016/s0305-0548(98)00047-1
Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. IEEE Xplore. https://doi.org/10.1109/CEC.2007.4425083
Goto T, Najafabadi HR, Falheiro M, Martins TC, Barari A, Tsuzuki MSG (2021) Topological optimization and simulated annealing. IFAC-PapersOnLine 54(1):205–210. https://doi.org/10.1016/j.ifacol.2021.08.078
Wang Y, Gao S, Yu Y, Cai Z, Wang Z (2021) A gravitational search algorithm with hierarchy and distributed framework. Knowl-Based Syst 218:106877. https://doi.org/10.1016/j.knosys.2021.106877
Kaveh A, Dadras A (2017) A novel meta-heuristic optimization algorithm: thermal exchange optimization. Adv Eng Softw 110:69–84. https://doi.org/10.1016/j.advengsoft.2017.03.014
Forrest S (1996) Genetic algorithms. ACM Comput Surv 28(1):77–80. https://doi.org/10.1145/234313.234350
Koza JR (2010) Human-competitive results produced by genetic programming. Genet Program Evolvable Mach 11(3–4):251–284. https://doi.org/10.1007/s10710-010-9112-3
Abualigah L, Elaziz MA, Sumari P, Khasawneh AM, Alshinwan M, Mirjalili S, … Gandomi AH (2022) Black hole algorithm: a comprehensive survey. Appl Intell.https://doi.org/10.1007/s10489-021-02980-5
Agharghor A, Riffi ME, Chebihi F (2019) Improved hunting search algorithm for the quadratic assignment problem. Indonesian J Electr Eng Comput Sci 14(1):143. https://doi.org/10.11591/ijeecs.v14.i1.pp143-154
Cui Y, Meng X, Qiao J (2022) A multi-objective particle swarm optimization algorithm based on two-archive mechanism. Appl Soft Comput 119:108532. https://doi.org/10.1016/j.asoc.2022.108532
Solving the Quadratic Assignment Problem using the Swallow Swarm Optimization Problem (2019) Int J Eng Adv Technol 8(6):3116–3120. https://doi.org/10.35940/ijeat.f9132.088619
Mzili I, Riffi ME, Benzekri F (2017) Penguins search optimization algorithm to solve quadratic assignment problem. Proceedings of the 2nd international conference on big data, cloud and applications. https://doi.org/10.1145/3090354.3090375
Dhiman G, Garg M, Nagar A, Kumar V, Dehghani M (2020) A novel algorithm for global optimization: rat swarm optimizer. J Ambient Intell Humaniz Comput 12(8):8457–8482. https://doi.org/10.1007/s12652-020-02580-0
Mzili T, Riffi ME, Mzili I, Dhiman G (2022) A novel discrete Rat swarm optimization (DRSO) algorithm for solving the traveling salesman problem. Decision making: applications in management and engineering, 5(2), 287–299. https://doi.org/10.31181/dmame0318062022m
Koopmans TC, Beckmann M (1957) Assignment problems and the location of economic activities. Econometrica 25(1):53–76. https://doi.org/10.2307/1907742
Bouzidi A, Riffi ME (2014) Discrete cat swarm optimization algorithm applied to combinatorial optimization problems. 2014 5th workshop on codes, cryptography and communication systems (WCCCS), 2014, pp 30–34,https://doi.org/10.1109/WCCCS.2014.7107914
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The authors would like to express their gratitude to editors and anonymous referees for their informative, helpful remarks and suggestions to improve this paper as well as the important guiding significance to our research.
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Mzili, T., Riffi, M.E., Mzili, I. (2023). The Adaptation of the Discrete Rat Swarm Optimization Algorithm to Solve the Quadratic Assignment Problem. In: Uddin, M.S., Bansal, J.C. (eds) Proceedings of International Joint Conference on Advances in Computational Intelligence. IJCACI 2022. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-1435-7_11
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