Abstract
Quadratic Assignment Problem (QAP) is a problem of facility locations of individual resources. QAP is a proven NP-hard challenging optimization problem and has a large number of real-life applications in diverse fields such as hospital layout problems, machine scheduling, keyboard design, and backboard wiring problem. Artificial electric field optimization (AEFA) is a new metaheuristic optimization algorithm and has achieved great success in continuous optimization problems. This paper presents a discrete artificial electric field algorithm for QAP. Due to the combinatorial nature of QAP, the general operations of AEFA such as particle representations, velocity and position update rules, and subtraction operations are modified. The proposed algorithm is applied to solve the QAP instances taken from the QAP library. The results show the promising performance of the proposed algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
T.C. Koopmans, M. Beckmann, Assignment problems and the location of economic activities. Econ. J. Econ. Soc. 53–76 (1957)
P.M. Pardalos, F. Rendl, H. Wolkowitz, The quadratic assignment problem: a survey and recent developments. Quadratic assignment and related problem. DIMACS Ser Discrete Math Theor Comput Sci. 16, 1–42 (1994)
R.K. Ahuja, J.B. Orlin, A. Tiwari, A greedy genetic algorithm for the quadratic assignment problem. Comput. Oper. Res. 27(10), 917–934 (2000)
L.M. Gambardella, É.D. Taillard, M. Dorigo, Ant colonies for the quadratic assignment problem. J. Oper. Res. Soc. 50(2), 167–176 (1999)
V. Nissen, Solving the quadratic assignment problem with clues from nature. IEEE Trans. Neural Netw. 5(1), 66–72 (1994)
T. Dokeroglu, E. Sevinc, A. Cosar, Artificial bee colony optimization for the quadratic assignment problem. Appl. Soft Comput. 76, 595–606 (2019)
A. Misevicius, An implementation of the iterated tabu search algorithm for the quadratic assignment problem. Oper. Res. Spect. 34(3), 665–690 (2012)
T. James, C. Rego, F. Glover, Multistart tabu search and diversification strategies for the quadratic assignment problem. IEEE Trans. Syst. Man Cybern. Part A: Syst. Humans 39(3), 579–596 (2009)
F. Hafiz, A. Abdennour, Particle Swarm algorithm variants for the quadratic assignment problems-A probabilistic learning approach. Expert Syst. Appl. 44, 413–431 (2016)
A. Misevicius, A modified simulated annealing algorithm for the quadratic assignment problem. Informatica 14(4), 497–514 (2003)
T. Dokeroglu, Hybrid teaching–learning-based optimization algorithms for the quadratic assignment problem. Comput. Ind. Eng. 85, 86–101 (2015)
U. Benlic, J.K. Hao, Memetic search for the quadratic assignment problem. Expert Syst. Appl. 42(1), 584–595 (2015)
Z. Drezner, Extensive experiments with hybrid genetic algorithms for the solution of the quadratic assignment problem. Comput. Oper. Res. 35(3), 717–736 (2008)
W.L. Lim, A. Wibowo, M.I. Desa, H. Haron, A biogeography-based optimization algorithm hybridized with tabu search for the quadratic assignment problem. Comput. Intell. Neurosci. (2016)
M. Abdel-Basset, G. Manogaran, D. El-Shahat, S. Mirjalili, Integrating the whale algorithm with tabu search for quadratic assignment problem: a new approach for locating hospital departments. Appl. Soft Comput. 73, 530–546 (2018)
A. Yadav, AEFA: artificial electric field algorithm for global optimization. Swarm Evol. Comput. 48, 93–108 (2019)
A. Yadav, N. Kumar, Artificial electric field algorithm for engineering optimization problems. Expert Syst. Appl. 149, 113308 (2020)
A. Yadav, Discrete artificial electric field algorithm for high-order graph matching. Appl. Soft Comput. 92, 106260 (2020)
R.E. Burkard, S.E. Karisch, F. Rendl, QAPLIB—a quadratic assignment problem library. J. Glob. Optim. 10(4), 391–403 (1997)
L.Y. Tseng, S.C. Liang, A hybrid metaheuristic for the quadratic assignment problem. Comput. Optim. Appl. 34(1), 85–113 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Anita, Yadav, A., Kumar, N., Kim, J.H. (2021). Development of Discrete Artificial Electric Field Algorithm for Quadratic Assignment Problems. In: Nigdeli, S.M., Kim, J.H., Bekdaş, G., Yadav, A. (eds) Proceedings of 6th International Conference on Harmony Search, Soft Computing and Applications. ICHSA 2020. Advances in Intelligent Systems and Computing, vol 1275. Springer, Singapore. https://doi.org/10.1007/978-981-15-8603-3_36
Download citation
DOI: https://doi.org/10.1007/978-981-15-8603-3_36
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-8602-6
Online ISBN: 978-981-15-8603-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)