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针对目前智能算法初期收敛速度难以满足坦克分队武器-目标分配(Weapon-Target Assignment,WTA)要求的问题,提出了一种改进人工蜂群算法。该算法结合NEH启发式算法和随机方法对种群进行初始化,利用变邻域搜索和模拟退火方法改进了采蜜蜂算法,并简化了跟随蜂算法,提出了一种全局最优限制算法。最后,结合不同规模的WTA问题,给出了该算法参数的确定方法。仿真结果表明:改进人工蜂群算法相比于其他算法在初始种群质量和算法初期收敛速度方面具有明显优势,特别适合求解坦克分队WTA问题。
Aiming at the problem that the initial convergence speed of intelligent algorithm is hard to meet the requirement of Weapon-Target Assignment (WTA), an improved artificial bee colony algorithm is proposed. The algorithm combines NEH heuristic algorithm and stochastic method to initialize the population. The improved neighborhood mining and simulated annealing method is adopted to improve the mining of bees and simplifies the following beekeeping algorithm. A global optimization algorithm is proposed. Finally, the method of determining the parameters of the algorithm is given in combination with WTA problems of different scales. Simulation results show that compared with other algorithms, the improved artificial bee colony algorithm has obvious advantages in initial population quality and initial convergence rate of the algorithm, and is especially suitable for solving the WTA problem in tank detachment.