A new approach to weapon-target assignment in cooperative air combat. (English) Zbl 1427.91070
Summary: A new approach to solving weapon-target assignment (WTA) problem is proposed in this paper. Firstly, relative superiority that lays the foundation for assignment is calculated based on the combat power energy of the fighters. Based on the relative superiority, WTA problem is formulated. Afterwards, a hybrid algorithm consisting of improved artificial fish swarm algorithm (AFSA) and improved harmony search (HS) is introduced and furthermore applied to solve the assignment formulation. Finally, the proposed approach is validated by eight representative benchmark functions and two concrete cooperative air combat examples. The results show that the approach proposed in this paper achieves good performances in solving WTA problem in cooperative air combat.
References:
| [1] | Karasakal, O., Air defense missile-target allocation models for a naval task group, Computers and Operations Research, 35, 2, 1759-1770, (2008) · Zbl 1139.90398 · doi:10.1016/j.cor.2006.09.011 |
| [2] | Bogdanowicz, Z. R., A new efficient algorithm for optimal assignment of smart weapons to targets, Computers and Mathematics with Applications, 58, 4, 1965-1969, (2009) · Zbl 1189.90084 · doi:10.1016/j.camwa.2009.07.082 |
| [3] | Mehmet, A., Approximating the optimal mapping for weapon target assignment by fuzzy reasoning, Information Sciences, 25, 5, 30-44, (2014) |
| [4] | Bogdanowicz, Z. R., Advanced input generating algorithm for effect-based weapon–target pairing optimization, IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, 42, 1, 276-280, (2012) · doi:10.1109/TSMCA.2011.2159591 |
| [5] | Toroslu, I. H.; Arslanoglu, Y., Genetic algorithm for the personnel assignment problem with multiple objectives, Information Sciences. An International Journal, 177, 3, 787-803, (2007) · doi:10.1016/j.ins.2006.07.032 |
| [6] | Lee, Z.-J.; Su, S.-F.; Lee, C.-Y., Efficiently solving general weapon-target assignment problem by genetic algorithms with greedy eugenics, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 33, 1, 113-121, (2003) · doi:10.1109/TSMCB.2003.808174 |
| [7] | Bai, F.; Chang, T. Q.; Li, Y., An improved niche-based adaptive genetic algorithm for WTA problem solving, Proceedings of the 2010 1st International Conference on Computational Problem-Solving, ICCP 2010 |
| [8] | McKendall Jr., A. R.; Shang, J.; Kuppusamy, S., Simulated annealing heuristics for the dynamic facility layout problem, Computers & Operations Research, 33, 8, 2431-2444, (2006) · Zbl 1086.90037 · doi:10.1016/j.cor.2005.02.021 |
| [9] | Zeng, X.; Zhu, Y.; Nan, L.; Hu, K.; Niu, B.; He, X., Solving weapon-target assignment problem using discrete particle swarm optimization, Proceedings of the 6th World Congress on Intelligent Control and Automation, WCICA 2006, IEEE · doi:10.1109/WCICA.2006.1713032 |
| [10] | Thangaraj, R.; Pant, M.; Nagar, A. K., Maximization of expected target damage value using quantum particle swarm optimization, Proceedings of the International Conference on Developments in eSystems Engineering, DeSE 2009, IEEE · doi:10.1109/DeSE.2009.48 |
| [11] | Su, M.-C.; Lai, S.-C.; Lin, S.-C.; You, L.-F., A new approach to multi-aircraft air combat assignments, Swarm and Evolutionary Computation, 6, 39-46, (2012) · doi:10.1016/j.swevo.2012.03.003 |
| [12] | Li, Y.; Dong, Y. N., Weapon-target assignment based on simulated annealing and discrete particle swarm optimization in cooperative air combat, Acta Aeronautica et Astronautica Sinica, 31, 3, 626-631, (2010) |
| [13] | Xin, B.; Chen, J.; Zhang, J.; Dou, L.; Peng, Z., Efficient decision makings for dynamic weapon-target assignment by virtual permutation and tabu search heuristics, IEEE Transactions on Evolutionary Computation, 40, 6, 649-662, (2010) · doi:10.1109/TSMCC.2010.2049261 |
| [14] | Song, W. D.; Zhao, C. W.; Huo, J. X., Improved differential evolution algorithm for solving WTA problem, Energy Procedia, 11, 1348-1353, (2011) · doi:10.1016/j.egypro.2011.10.401 |
| [15] | Zhang, J.; Wang, Z.-X.; Chen, L.; Wu, Z.-B.; Lu, J.-F., Modeling and optimization on antiaircraft weapon-target assignment at multiple interception opportunity, Acta Armamentarii, 35, 10, 1644-1650, (2014) · doi:10.3969/j.issn.1000-1093.2014.10.019 |
| [16] | Chen, L.; Wang, Z. X.; Wu, Z. B., A kind of antiaircraft weapon-target optimal assignment under earlier damage principle, Acta Aeronautica et Astronautica Sinica, 35, 9, 2574-2582, (2014) · doi:10.7527/S1000-6893.2014.0048 |
| [17] | Zhou, T.; Ren, B.; Yu, L., Modeling of air combat analysis based on combat power field, Journal of System Simulation, 20, 3, 738-745, (2008) |
| [18] | Li, X. L.; Shao, Z. J.; Qian, J. X., An optimizing method based on autonomous animals: fish swarm algorithm, Systems Engineering Theory and Practice, 22, 11, 32-38, (2002) |
| [19] | Yazdani, D.; Nabizadeh, H.; Mohamadzadeh Kosari, E.; Nadjaran Toosi, A., Color quantization using modified artificial fish swarm algorithm, Lecture Notes in Computer Science, 7106, 1, 382-391, (2011) |
| [20] | Xue, P., Adaptive hybrid artificial fish school algorithm for solving the real roots of polynomials, Proceedings of the International Conference on Intelligent Computing (ICIC 2014), Intelligent Computing Theory, Springer · doi:10.1007/978-3-319-09333-8_6 |
| [21] | El-Said, S. A., Image quantization using improved artificial fish swarm algorithm, Soft Computing-A Fusion of Foundations,Methodologies and Applications, 19, 9, 2667-2679, (2015) · doi:10.1007/s00500-014-1436-0 |
| [22] | Wang, C.-R.; Zhou, C.-L.; Ma, J.-W., An improved artificial fish-swarm algorithm and its application in feed-forward neural networks, Proceedings of the 4th International Conference on Machine Learning and Cybernetics |
| [23] | Jiang, M.; Wang, Y.; Rubio, F.; Yuan, D., Spread spectrum code estimation by artificial fish swarm algorithm, Proceedings of the IEEE International Symposium on Intelligent Signal Processing (WISP ’07) · doi:10.1109/WISP.2007.4447587 |
| [24] | Azad, M. A. K.; Rocha, A. M. A. C.; Fernandes, E. M. G. P., Improved binary artificial fish swarm algorithm for the 0-1 multidimensional knapsack problems, Swarm and Evolutionary Computation, 14, 66-75, (2014) · doi:10.1016/j.swevo.2013.09.002 |
| [25] | Geem, Z. W.; Kim, J. H.; Loganathan, G. V., A new heuristic optimization algorithm: harmony search, Simulation, 76, 2, 60-68, (2001) · doi:10.1177/003754970107600201 |
| [26] | Chang, Y. Z.; Li, Z. W.; Kou, Y. X., Method for formation selection in air combat under uncertain information condition, Systems Engineering and Electronics, 11, 38, 2552-2560, (2016) |
| [27] | Liu, B.; Wang, L.; Jin, Y.-H.; Tang, F.; Huang, D.-X., Improved particle swarm optimization combined with chaos, Chaos, Solitons and Fractals, 25, 5, 1261-1271, (2005) · Zbl 1074.90564 · doi:10.1016/j.chaos.2004.11.095 |
| [28] | Rahnamayan, S.; Tizhoosh, H. R.; Salama, M. M. A., Opposition-based differential evolution, IEEE Transactions on Evolutionary Computation, 12, 1, 64-79, (2008) · doi:10.1109/TEVC.2007.894200 |
| [29] | Chun, J.-S.; Kim, M.-K.; Jung, H.-K.; Hong, S.-K., Shape optimization of electromagnetic devices using immune algorithm, IEEE Transactions on Magnetics, 33, 2, 1876-1879, (1997) · doi:10.1109/20.582650 |
| [30] | Wang, L.; Yang, R.; Xu, Y.; Niu, Q.; Pardalos, P. M.; Fei, M., An improved adaptive binary harmony search algorithm, Information Sciences. An International Journal, 232, 58-87, (2013) · doi:10.1016/j.ins.2012.12.043 |
| [31] | Li, R.-P.; Ouyang, H.-B.; Gao, L.-Q.; Zou, D.-X., Learned harmony search algorithm and its application to 0-1 knapsack problems, Control and Decision, 28, 2, 205-210, (2013) · Zbl 1289.68142 |
| [32] | Khalili, M.; Kharrat, R.; Salahshoor, K.; Sefat, M. H., Global dynamic harmony search algorithm: GDHS, Applied Mathematics and Computation, 228, 195-219, (2014) · Zbl 1364.90373 · doi:10.1016/j.amc.2013.11.058 |
| [33] | Zhu, B. L.; Zhu, R. C.; Xiong, X. F., Battle Plane Effectiveness Evaluation, (1993), Beijing, China: Aviation Industry Press, Beijing, China |
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