基于改进差分进化算法的动态防空资源分配优化

Dynamic Air Defense Resource Allocation Optimization Based on Improved Differential Evolution Algorithm

面对动态防空资源分配问题中存在的空袭目标突现和雷达、发射车等资源受干扰现象,在综合考虑雷达、发射车和导弹等武器装备性能的基础上,基于目标集、资源集建立了最小化目标总拦截价值与生存概率的混合整数决策模型。提出了一种新的改进差分进化算法进行求解,采用反向学习策略生成初始解,确保初始种群的质量,设计了一种快速修复与重构的启发式规则作用于多阶段,以提升算法的搜索能力。仿真实验验证了该算法具有求解时间和求解精度上的优越性。该研究能使武器系统在动态事件的随机影响下,保持高效的作战能力和决策效果。

Based on the integrated performance of weapon equipments such as radars,launchers and missiles,a mixed-integer decision model that minimizes the total target intercept value and the probability of survival based on Target-Set,Resource-Set is developed.A new improved differential evolutionary algorithm has been introduced to solve the problem,and the initial solutions is generated by using the reverse learning strategies to ensure the quality of the initial populations.An inspiration rule for the fast repair and reconstruction is designed to work at multi-stage to improve the search capability of the algorithm.The simulation experiment results show the algorithm's superiority in search time and search accuracy,which can maintain the efficient combat capabilities and decision-making under the random influence of dynamic events.