%0 Journal Article
%T 现代战争物资分配的研究——基于多目标优化的蚁群算法模型<br>Research on Material Distribution in Modern War—Ant Colony Algorithm Model Based on Multi-Objective Optimization
%A 徐慧威
%A 陈信至
%A 侯明鑫
%A 苏永杰
%A 吴少彬
%A 李军
%J Computer Science and Application
%P 428-437
%@ 2161-881X
%D 2024
%I Hans Publishing
%R 10.12677/CSA.2024.142043
%X <span style=\"font-size:14px;\">针对现代战争中红蓝双方物资分配问题，为了优化分配供应，提出一种基于蚁群算法优化现代战争物资分配的方法。引入蚁群算法构建最短路径模型，模型充分考虑约束条件，确保每个位置唯一访问；综合考虑火力打击目标，同时分析不考虑时间窗的多目标优化情况；通过信息素初始化、状态转移率标准和信息素更新，构建有效的路径规划模型；构建判断矩阵对目标因素进行一致性检验；最终通过蚁群算法获得红队和蓝队的最短路径长度并得出红蓝方运输车辆最优配置及总人数。</span><br />
<span style=\"font-size:14px;\">Aiming at the problem of material distribution between red and blue in modern war, in order to optimize the distribution and supply, a method of material distribution optimization in modern war based on an ant colony algorithm was proposed. The ant colony algorithm is introduced to construct the shortest path model, which fully considers the constraints to ensure the unique access of each location. Considering the firepower attack target comprehensively, the multi-objective optimization without considering the time window is analyzed. Through pheromone initialization, state transition rate standard and pheromone update, an effective path planning model was constructed; The judgment matrix was constructed to test the consistency of the target factors. Finally, the ant colony algorithm was used to obtain the shortest path length of the red team and the blue team, and the optimal configuration of the red and blue transport vehicles and the total number of people.</span>
%K 蚁群算法，物资分配供应，多目标优化，最短路径<br>Ant Colony Algorithm
%K Material Distribution and Supply
%K Multi-Objective Optimization
%K Shortest Path
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=82221