Hostname: page-component-78c5997874-4rdpn Total loading time: 0 Render date: 2024-11-05T15:25:57.304Z Has data issue: false hasContentIssue false
  • English
  • Français

Multitarget allocation strategy based on adaptive SA-PSO algorithm

Published online by Cambridge University Press:  28 January 2022

S. Liu Open the ORCID record for S. Liu [Opens in a new window] ,
W. Liu ,
F. Huang ,
Y. Yin ,
B. Yan  and
T. Zhang
S. Liu
Affiliation:
Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an, Shannxi China
W. Liu
Affiliation:
Shanghai Aerospace Equipment Manufacturer, Shanghai, China
F. Huang
Affiliation:
School of Astronautics, Northwestern Polytechnical University, Xi’an, Shannxi, China
Y. Yin
Affiliation:
School of Astronautics, Northwestern Polytechnical University, Xi’an, Shannxi, China
B. Yan*
Affiliation:
School of Astronautics, Northwestern Polytechnical University, Xi’an, Shannxi, China
T. Zhang
Affiliation:
Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an, Shannxi China
*
*Corresponding author. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Weapon target allocation (WTA) is an effective method to solve the battlefield fire optimisation problem, which plays an important role in intelligent automated decision-making. We researched the multitarget allocation problem to maximise the attack effectiveness when multiple interceptors cooperatively attack multiple ground targets. Firstly, an effective and reasonable fitness function is established, based on the situation between the interceptors and targets, by comprehensively considering the relative range, relative angle, speed, capture probability and radiation source matching performance and thoroughly evaluating them based on the advantage of the attack effectiveness. Secondly, the optimisation performance of the particle swarm optimisation (PSO) algorithm is adaptively improved. We propose an adaptive simulated annealing-particle swarm optimisation (SA-PSO) algorithm by introducing the simulated annealing algorithm into the adaptive PSO algorithm. The proposed algorithm can enhance the convergence speed and overcome the disadvantage of the PSO algorithm easily falling into a local extreme point. Finally, a simulation example is performed in a scenario where ten interceptors cooperate to attack eight ground targets; comparative experiments are conducted between the adaptive SA-PSO algorithm and PSO algorithm. The simulation results indicate that the proposed adaptive SA-PSO algorithm demonstrates great performance in convergence speed and global optimisation capabilities, and a maximised attack effectiveness can be guaranteed.

Keywords

Multitarget allocationAdaptiveSimulated annealing algorithmParticle swarm optimisationAttack effectiveness
Type
Research Article
Information
The Aeronautical Journal , Volume 126 , Issue 1300 , June 2022 , pp. 1069 - 1081
Check for updates
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of Royal Aeronautical Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Shiyu, Z. and Rui, Z. Cooperative guidance for multimissile salvo attack, Chin. J. Aeronaut., 2008, 21, (6), pp 533–539.CrossRefGoogle Scholar
Wang, X., Zhang, Y., Liu, D. and He, M. Three-dimensional cooperative guidance and control law for multiple reentry missiles with time-varying velocities, Aerospace Sci. Technol., 2018, 80, pp 127–143.CrossRefGoogle Scholar
You, H. and Zhao, F.J. Distributed synergetic guidance law for multiple missiles with angle-of-attack constraint, Aeronaut. J., 2020, 124, (1274), pp 533–548.CrossRefGoogle Scholar
An, K., Guo, Z.Y., Huang, W. and Xu, X.P. A cooperative guidance approach based on the finite-time control theory for hypersonic vehicles, Int. J. Aeronaut. Space Sci., 2021, pp 1–11. doi: 10.1007/s42405-021-00416-5.Google Scholar
Lee, M.Z. Constrained weapon–target assignment: Enhanced very large scale neighborhood search algorithm, IEEE Trans. Syst. Man Cybern. Part A Syst. Hum., 2009, 40, (1), pp 198–204.CrossRefGoogle Scholar
Bogdanowicz, Z.R. Advanced input generating algorithm for effect-based weapon–target pairing optimization, IEEE Trans. Syst. Man Cybern. Part A Syst. Hum., 2011, 42, (1), pp 276–280.CrossRefGoogle Scholar
Bogdanowicz, Z.R., Tolano, A., Patel, K. and Coleman, N.P. Optimization of weapon–target pairings based on kill probabilities, IEEE Trans. Cybern., 2012, 43, (6), pp 1835–1844.CrossRefGoogle Scholar
Bogdanowicz, Z.R. and Patel, K. Quick collateral damage estimation based on weapons assigned to targets, IEEE Trans. Syst. Man Cybern. Syst., 2014, 45, (5), pp 762–769.Google Scholar
Chen, J., Xin, B., Peng, Z., Dou, L. and Zhang, J. Evolutionary decision-makings for the dynamic weapon-target assignment problem, Sci. China Ser. F Inf. Sci., 2009, 52, (11), p 2006.CrossRefGoogle Scholar
Sonuc, E., Sen, B. and Bayir, S. A parallel simulated annealing algorithm for weapon-target assignment problem, Int. J. Adv. Comput. Sci. Appl., 2017, 8, (4), pp 87–92.Google Scholar
Kline, A., Ahner, D. and Hill, R. The weapon-target assignment problem. Comput. Oper. Res., 2019, 105, pp 226–236.CrossRefGoogle Scholar
Kong, L., Wang, J. and Zhao, P. Solving the dynamic weapon target assignment problem by an improved multiobjective particle Swarm optimization algorithm, Appl. Sci., 2021, 11, (19), 9254.CrossRefGoogle Scholar
Kalmár-Nagy, T., Giardini, G. and Bak, B.D. The multiagent planning problem, Complexity, 2017, 2017, p 3813912.CrossRefGoogle Scholar
Hocaoğlu, M.F. Weapon target assignment optimization for land based multi-air defense systems: A goal programming approach, Comput. Ind. Eng., 2019, 128, pp 681–689.CrossRefGoogle Scholar
Kumarappan, N. and Suresh, K. Combined SA PSO method for transmission constrained maintenance scheduling using levelized risk method, Int. J. Electr. Power Energy Syst., 2015, 73, pp 1025–1034.CrossRefGoogle Scholar
Yuanming, D., Chengyang, L.I.U., Qian, L.U. and Min, Z.H.U. Effectiveness evaluation of UUV cooperative combat based on GAPSO-BP neural network, 2019 Chinese Control And Decision Conference (CCDC), IEEE, 2019, pp 4620–4625.Google Scholar
Cheng, Z., Fan, L. and Zhang, Y. Multi-agent decision support system for missile defense based on improved PSO algorithm, J. Syst. Eng. Electr., 2017, 28, (3), pp 514–525.Google Scholar
Zheng, X., Gao, Y., Jing, W. and Wang, Y. Multidisciplinary integrated design of long-range ballistic missile using PSO algorithm, J. Syst. Eng. Electr., 2020, 31, (2), pp 335–349.CrossRefGoogle Scholar
Fu, G., Wang, C., Zhang, D., Zhao, J. and Wang, H. A multiobjective particle swarm optimization algorithm based on multipopulation coevolution for weapon-target assignment, Math. Probl. Eng., 2019, 2019, 1424590 (11 pages). doi: 10.1155/2019/1424590.CrossRefGoogle Scholar
Liu, Z., Shi, Z., Wu, L. and Xiao, Y. Solving cooperative anti-missile weapon-target assignment problems using hybrid algorithms based on particle swarm and tabu search, DEStech Trans. Comput. Sci. Eng., 2017, 190, pp 898–906.Google Scholar
Kennedy, J. and Eberhart, R. Particle swarm optimization, Proceedings of ICNN’95-International Conference on Neural Networks, Vol. 4, IEEE, 1995, pp 1942–1948.Google Scholar
Eberhart, R. and Kennedy, J. A new optimizer using particle swarm theory, MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, IEEE, 1995, pp 39–43.Google Scholar
Wu, Z., Wu, Z. and Zhang, J. An improved FCM algorithm with adaptive weights based on SA-PSO, Neural Comput. Appl., 2017, 28, (10), pp 3113–3118.CrossRefGoogle Scholar
Lu, C., Yang, Z., Sun, X., Ding, Q. and Zhao, Q. A decoupling control of composite cage rotor bearingless induction motor based on SA-PSO support vector machine inverse, Int. Trans. Electr. Energy Syst., 2021, 31, (8), p e12988.CrossRefGoogle Scholar