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作战方案(COA)优选是任务规划系统的重要组成部分,其性能很大程度上决定了任务规划的性能.因此针对任务规划必须符合作战要求和时效性要求,提出了扩展TOPSIS和PSO结合的COA优选方法.首先,为了提高规划的时效性,采用粒子群算法进行搜索优化;对作战要求和作战效能数据进行模糊化处理,生成标准化决策数据,计算每个COA到TOPSIS(逼近于理想解排序)正负理想解的距离;得到COA灰色关联贴进度,作为PSO算法的适应值.文章最后进行实例分析,验证该方法的可行性和有效性.
COA optimization is an important part of task planning system and its performance largely determines the performance of task planning.Therefore, in order to meet the requirements of operational requirements and timeliness, the COA is proposed to extend the cooperation between TOPSIS and PSO Optimization method.Firstly, in order to improve the timeliness of the planning, the particle swarm optimization algorithm is used to optimize the search. The operational requirements and combat effectiveness data are fuzzified to generate standardized decision data, and each COA to TOPSIS is calculated (approximate to ideal solution order) The distance between positive and negative ideal solution is obtained, and the gray relevancy degree of COA is obtained as the fitness value of PSO algorithm.At last, an example is given to verify the feasibility and effectiveness of this method.