AccScience Publishing / IJOCTA / Volume 10 / Issue 2 / DOI: 10.11121/ijocta.01.2020.00775
RESEARCH ARTICLE

A modified crow search algorithm for the weapon-target assignment problem

Emrullah Sonuç1*
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1 Department of Computer Engineering, Karabuk University, Turkey
IJOCTA 2020, 10(2), 188–197; https://doi.org/10.11121/ijocta.01.2020.00775
Submitted: 26 January 2019 | Accepted: 31 January 2020 | Published: 4 June 2020
© 2020 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Abstract

The Weapon-Target Assignment (WTA) problem is one of the most important optimization problems in military operation research. In the WTA problem, assets of defense aim the best assignment of each weapon to target for decreasing expected damage directed by the offense. In this paper, Modified Crow Search Algorithm (MCSA) is proposed to solve the WTA problem. In MCSA, a trial mechanism is used to improve the quality of solutions using parameter LIMIT. If the solution is not improved after a predetermined number of iterations, then MCSA starts with a new position in the search space. Experimental results on the different sizes of the WTA problem instances show that MCSA outperforms CSA in all problem instances. Also, MCSA achieved better results for 11 out of 12 problem instances compared with four state-of-the-art algorithms. The source codes of MCSA for the WTA are publicly available at http://www.3mrullah.com/MCSA.html.

Keywords
Combinatorial optimization
Crow search algorithm
Nature inspired meta-heuristic algorithms
Weapon-Target Assignment Problem
Conflict of interest
The authors declare they have no competing interests.
References

[1] Kline, A., Ahner, D., & Hill, R. (2018). The Weapon-Target Assignment Problem. Computers & Operations Research. https://doi.org/10.1016/j.cor.2018.10.015

[2] Ahuja, R. K., Kumar, A., Jha, K. C., & Orlin, J. B.(2007). Exact and Heuristic Algorithms for the Weapon-Target Assignment Problem. Operations Research, 55(6), 1136–1146. https://doi.org/10.1287/opre.1070.0440

[3] Sikanen, T. (2008). Solving weapon target assignment problem with dynamic programming.Independent Research Projects in Applied Mathematics, 32.

[4] Ma, F., Ni, M., Gao, B., & Yu, Z. (2015). An efficient algorithm for the weapon target assignment problem. In 2015 IEEE International Conference on Information and Automation (pp. 2093–2097). https://doi.org/10.1109/ICInfA.2015.7279633

[5] Lloyd, S. P., & Witsenhausen, H. S. (1986). Weapons allocation is NP-complete. In 1986 Summer Computer Simulation Conference (pp. 1054–1058).

[6] Talbi, E.-G. (2009). Metaheuristics: From Design to Implementation. John Wiley & Sons.

[7] Sotoudeh-Anvari, A., & Hafezalkotob, A. (2018). A bibliography of metaheuristics-review from 2009 to 2015. International Journal of Knowledge Based Intelligent Engineering Systems, 22(1), 83–95. https://doi.org/10.3233/KES-180376

[8] Askarzadeh, A. (2016). A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm. Computers & Structures, 169, 1–12. https://doi.org/10.1016/j.compstruc.2016.03.001

[9] Şahin, M. A., & Leblebi̇ ci̇ oğlu, K. (2011). A Hierarchical Fuzzy Decision Maker for the Weapon Target Assignment. IFAC Proceedings Volumes, 44(1), 8993–8998. https://doi.org/10.3182/20110828-6-IT-1002.00986

[10] Wang, J., Luo, P., Hu, X., & Zhang, X. (2018). A Hybrid Discrete Grey Wolf Optimizer to Solve Weapon Target Assignment Problems. Discrete Dynamics in Nature and Society. https://doi.org/10.1155/2018/4674920

[11] Li, Y., Kou, Y., Li, Z., Xu, A., & Chang, Y. (2017). A Modified Pareto Ant Colony Optimization Approach to Solve Biobjective Weapon-Target Assignment Problem. International Journal of Aerospace Engineering. https://doi.org/10.1155/2017/1746124

[12] Sonuc, E., Sen, B., & Bayir, S. (2017). A Parallel Simulated Annealing Algorithm for Weapon-Target Assignment Problem. International Journal of Advanced Computer Science and Applications, 8(4). https://doi.org/10.14569/IJACSA.2017.080412

[13] Zhang, J., Wang, X., Xu, C., & Yuan, D. (2012).ACGA Algorithm of Solving Weapon—Target Assignment Problem. Open Journal of Applied Sciences, 02(04), 74–77. https://doi.org/10.4236/ojapps.2012.24B018

[14] Durgut, R., Kutucu, H., & Akleylek, S. (2017). An Artificial Bee Colony Algorithm for Solving the Weapon Target Assignment Problem. In Proceedings of the 7th International Conference on Information Communication and Management (pp. 28–31). New York, NY, USA: ACM. https://doi.org/10.1145/3134383.3134390

[15] Kutucu, H., & Durgut,R. (2018). Silah Hedef Atama Problemi için Tavlama Benzetimli Bir Hibrit Yapay Ari Kolonisi Algoritmasi. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 22(Özel), 263. https://doi.org/10.19113/sdufbed.39561

[16] Lee, Z.-J., Lee, C.-Y., & Su, S.-F. (2002). An immunity-based ant colony optimization algorithm for solving weapon–target assignment problem. Applied Soft Computing, 2(1), 39–47. https://doi.org/10.1016/S1568-4946(02)00027-3

[17] Hu, X., Luo, P., Zhang, X., & Wang, J. (2018). Improved Ant Colony Optimization for Weapon- Target Assignment. Mathematical Problems in Engineering. https://doi.org/10.1155/2018/6481635

[18] Tokgöz, A., & Bulkan, S. (2013). Weapon Target Assignment with Combinatorial Optimization Techniques. International Journal of Advanced Research in Artificial Intelligence, 2(7). https://doi.org/10.14569/IJARAI.2013.020707

[19] Li, X., Zhou, D., Pan, Q., Tang, Y., & Huang, J.(2018). Weapon-Target Assignment Problem by Multiobjective Evolutionary Algorithm Based on Decomposition. Complexity. https://doi.org/10.1155/2018/8623051

[20] Kline, A. G., Ahner, D. K., & Lunday, B. J. (2018).Real-time heuristic algorithms for the static weapon target assignment problem. Journal of Heuristics. https://doi.org/10.1007/s10732-018-9401-1

[21] Hocaoğlu, M. F. (2019). Weapon target assignment optimization for land based multi-air defense systems: A goal programming approach. Computers & Industrial Engineering, 128, 681–689. https://doi.org/10.1016/j.cie.2019.01.015

[22] Karaboga, D., & Basturk, B. (2007). Apowerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459–471. https://doi.org/10.1007/s10898-007-9149- x

[23] Akay, B. B., & Karaboga, D. (2017). Artificial bee colony algorithm variants on constrained optimization. An International Journal of Optimization and Control: Theories & Applications (IJOCTA), 7(1), 98–111. https://doi.org/10.11121/ijocta.01.2017.00342

[24] Sonuç, E. (2018). Artificial Bee Colony Algorithm for The Linear Ordering Problem. In Proceeding Book of the International Conference on Advanced Technologies, Computer Engineering and Science (ICATCES 2018) (pp. 818–820). Safranbolu, Turkey.

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An International Journal of Optimization and Control: Theories & Applications, Electronic ISSN: 2146-5703 Print ISSN: 2146-0957, Published by AccScience Publishing