Computational military tactical planning system
Computational military tactical planning system
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DOI：
10.1109/tsmcc.2002.801352
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发表时间：
2002-05-01
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影响因子：
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通讯作者：
Embrechts, MJ
中科院分区：
文献类型：
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作者：
Kewley, RH;Embrechts, MJ
模糊-遗传决策优化计算系统结合了遗传优化和模糊序优先两种软计算方法和随机系统模拟这一传统的硬计算方法，解决了战术部队作战计划生成的难题。军事战术作战规划是一项复杂的、高维度的任务，经常困扰有经验的专业人员。在模糊遗传决策优化中，军事指挥官将他的战斗结果偏好输入到用户界面中以生成模糊序数偏好模型，该模型对他对任何战斗结果的偏好进行评分。一种遗传算法迭代地生成用于随机作战模拟评估的作战计划种群。模糊偏好模型将模拟结果转换为每个种群成员的适应度值，允许遗传算法生成下一个种群。进化一直持续到系统产生最终的高性能计划群体，这些计划实现了指挥官对使命的意图。实验结果分析表明，通过竞争遗传算法的友方和敌方计划的共同进化提高了规划系统的性能。如果允许进化足够长的时间，自动算法产生的计划比经验丰富的军事专家产生的计划具有更高的平均性能。
A computational system called fuzzy-genetic decision optimization combines two soft computing methods, genetic optimization and fuzzy ordinal preference, and a traditional hard computing method, stochastic system simulation, to tackle the difficult task of generating battle plans for military tactical forces. Planning for a tactical military battle is a complex, high-dimensional task which often bedevils experienced professionals. In fuzzy-genetic decision optimization, the military commander enters his battle outcome preferences into a user interface to generate a fuzzy ordinal preference model that scores his preference for any battle outcome. A genetic algorithm iteratively generates populations of battle plans for evaluation in a stochastic combat simulation. The fuzzy preference model converts the simulation results into a fitness value for each population member, allowing the genetic algorithm to generate the next population. Evolution continues until the system produces a final population of high-performance plans which achieve the commander's intent for the mission. Analysis of experimental results shows that co-evolution of friendly and enemy plans by competing genetic algorithms improves the performance of the planning system. If allowed to evolve long enough, the plans produced by automated algorithms had a significantly higher mean performance than those generated by experienced military experts.