武器目标分配(WTA)是指挥控制和任务规划领域的关键问题之一,萤火虫算法求解武器目标分配问题具有参数设置简单、执行效率高等优点.针对传统萤火虫算法易陷入局部极值,收敛速度和精度不高的弊端,从以下三个方面进行改进:初始化萤火虫序列编码时融入PWLCM混沌优化以提高全局搜索能力;设置受迭代次数控制的服从半高斯分布的非均匀步长因子以兼顾算法的搜索能力和收敛性;设计基于排序的萤火虫更新策略同时融入交叉变异操作以提高算法的效率和准确性.为验证改进算法的先进性,先利用其求解4种典型测试函数的最优解,再求解WTA问题,并与传统萤火虫算法进行对比,仿真实验结果表明,改进后算法的评价函数值提高了2.1%,迭代次数减少了52.9%,改善了传统算法易陷入局部极值的缺点,提高了收敛速度和精度,提升了武器目标分配的效果.
Weapon target assignment(WTA)is one of the key problems in the field of command control and task planning.The firefly algorithm has the advantages of simple parameter setting and high execution efficiency when solving the problem of weapon target assignment.In response to the shortcomings of the traditional firefly algorithms,such as being prone to local optima,slow convergence speed and low accuracy,improvements are made in the following three aspects,including incorporating PWLCM(piece-wise linear chaotic map)chaotic optimization into the initialization of firefly sequence encoding to improve global search ability,setting a non-uniform step size factor that follows a semi-Gaussian distribution controlled by the number of iterations to balance the search ability and convergence of the algorithm,designing a firefly update strategy based on sorting while incorporating cross mutation operations to improve the efficiency and accuracy of the algorithm.In order to verify the progressiveness of the improved algorithm,it is used to solve the optimal solutions of four typical test functions first,and then to solve the WTA problem.Finally,it is contrasted with the traditional firefly algorithm.The results of simulation experiments show that the evaluation function value of the improved algorithm is increased by 2.1%,and the number of iterations is reduced by 52.9%.The improved algorithm improves the disadvantage of being prone to trapping in local optima,improves convergence speed and accuracy,and enhances the effect of weapon target allocation.