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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: yanbinbin@nwpu.edu.cn
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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
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Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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