Estimating force mix lower bounds using a multi-objective evolutionary algorithm

Fred Ma, S. Wesolkowski
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引用次数: 1

Abstract

Nations will always experience conflicting pressures to reduce both (i) the funding of militaries and (ii) the probability that they will not be able to respond to scenarios that may arise. We develop a multiobjective evolutionary algorithm (MOEA) to generate force mix options that trade-off between lower bounds for objective (i) versus objective (ii). A set of military assets or force mix is evaluated against multiple instances of the future, each composed of a mix of stochastically generated realistic scenarios based on historically derived parameters. Scenario success is evaluated by matching each occurrence with a course of action (CoA) whose force element (FE) demands can be met. The lower bound on (i) comes from the assumption that a nation has complete flexibility to engage in scenarios at times that minimize simultaneous demand on FEs. The results are compared with the results from Tyche, a discrete event Simulator, which provides an more realistic, though pessimistic, point estimate of objective (ii). Results confirm the expected relative behavior of both models.
用多目标进化算法估计力混合下界
各国总是会面临相互冲突的压力,既要减少(1)军事资金,又要减少(2)它们无法应对可能出现的情况的可能性。我们开发了一种多目标进化算法(MOEA)来生成力量混合选项,在目标(i)与目标(ii)的下限之间进行权衡。针对未来的多个实例评估一组军事资产或力量混合,每个实例由基于历史衍生参数的随机生成的现实场景的混合组成。通过将每个事件与能够满足力元素(FE)需求的行动过程(CoA)相匹配来评估场景的成功。(i)的下限来自这样一个假设,即一个国家有完全的灵活性,可以在某些情况下将对FEs的同时需求最小化。结果与离散事件模拟器Tyche的结果进行了比较,后者提供了一个更现实的,尽管悲观的,目标(ii)的点估计。结果证实了两种模型的预期相对行为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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