基于PSO和扩展TOPSIS方法的COA优选方法

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.

Approach to COA selection based on PSO and extend TOPSIS

Course of action (COA) selection is preferably an important part of the mission planning system. Its performance largely determines the performance of mission planning. So for mission planning must meet operational requirements and timeliness requirements, the selection method is proposed combined PSO with extended TOPSIS. First of all, fuzzy process the combat effective data and the operational requirements, generate decision-making standardized data; then calculate for each COA's distance to the ideal solution of TOPSIS, then sort through the gray relational to obtain the optimal solution. In addition, in order to improve the timeliness of planning, we use the particle swarm optimization (PSO). Finally, case study is conducted to verify the feasibility and effectiveness of the algorithm.