首页> 外文会议>IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA 2009) >Minimizing risk on a fleet mix problem with a multiobjective evolutionary algorithm
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Minimizing risk on a fleet mix problem with a multiobjective evolutionary algorithm

机译:使用多目标进化算法将车队混合问题的风险降至最低

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We apply the non-dominated sorting genetic algorithm-II (NSGA-II) to perform a multiobjective optimization of the Stochastic Fleet Estimation (SaFE) model. SaFE is a Monte Carlo-based model which generates a vehicle fleet based on the set of requirements that the fleet is supposed to accomplish. We search for Pareto-optimal combinations of valid platform-assignments for a list of tasks, which can be applied to complete scenarios output by SaFE. Solutions are evaluated on three objectives, with the goal of minimizing fleet cost, total task duration time, and the risk that a solution will not be able to accomplish possible future scenarios.
机译:我们应用非支配的排序遗传算法-II(NSGA-II)对随机车队估计(SaFE)模型执行多目标优化。 SaFE是基于蒙特卡洛的模型,该模型根据车队应完成的一组要求生成车队。我们搜索有效平台分配的帕累托最优组合以获取任务列表,该列表可应用于SaFE完整输出的方案。对解决方案的评估基于三个目标,目标是最大程度地减少机队成本,总任务持续时间以及解决方案无法完成未来可能出现的情况的风险。

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