自动化学报2025，Vol.51Issue(5)：1080-1091,12.DOI:10.16383/j.aas.c240257
基于改进粒子群优化和Stackelberg博弈的武器部署
Weapon Deployment Based on Improved Particle Swarm Optimization and Stackelberg Game
摘要
Abstract
In order to cope with the impact of incoming target's maneuvering adjustment on the defensive capabil-ity of the defense zone,a new deployment optimization model and a solution algorithm are designed.Firstly,at the tactical level,a new weapon deployment model is proposed that takes into account changes in offensive and defens-ive information,allowing for dynamic adjustment of the deployment strategy to improve the overall effectiveness of the defense system;Secondly,an initialization scheme for the algorithm,based on chaotic mapping mechanism and K-means clustering and center-of-gravity method,is designed to cope with both limited and ample resources,redu-cing the risk of the algorithm falling into local optima;Then,an individual optimal renewal method based on the Metropolis criterion and a global optimal renewal method based on the Stackelberg game model are designed to guide the evolutionary directions of the population;Finally,the effectiveness of the new model and the proposed al-gorithm is verified through multi-scale scenario simulation experiments.The results of the comparative experiments show that the new model reflects deployment scheme differences more accurately and the proposed algorithm signi-ficantly improves the solution quality and convergence.关键词
武器部署/Stackelberg博弈/粒子群优化/K均值聚类/重心法Key words
Weapon deployment/Stackelberg game/particle swarm optimization/K-means clustering/center-of-gravity method引用本文复制引用
刘富樯,刘中阳,周伦,皮阳军,蒲华燕,罗均..基于改进粒子群优化和Stackelberg博弈的武器部署[J].自动化学报,2025,51(5):1080-1091,12.基金项目
国家自然科学基金(62033001),重庆市技术创新与应用发展重点项目(CSTB2023TIAD-KPX0057)资助Supported by National Natural Science Foundation of China(62033001)and Key Project of Technological Innovation and Ap-plication Development of Chongqing(CSTB2023TIAD-KPX0057) （62033001）
