{"title":"Minimizing risk on a fleet mix problem with a multiobjective evolutionary algorithm","authors":"M. Mazurek, S. Wesolkowski","doi":"10.1109/CISDA.2009.5356525","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":6407,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications","volume":"454 1","pages":"1-7"},"PeriodicalIF":0.0000,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CISDA.2009.5356525","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13
Abstract
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.
我们应用非支配排序遗传算法- ii (NSGA-II)对随机舰队估计(SaFE)模型进行多目标优化。SaFE是一个基于蒙特卡罗的模型，它根据车队应该完成的要求集生成车队。我们为一组任务搜索有效平台分配的帕累托最优组合，这些任务可以应用于由SaFE输出的完整场景。解决方案根据三个目标进行评估，目标是最小化车队成本、总任务持续时间和解决方案无法完成未来可能场景的风险。
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