不确定条件下基于区间方法和进化策略的维修优化

C. Rocco
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引用次数: 7

摘要

本文给出了两个应用区间算法和进化策略求解不确定条件下维修优化问题的实例。在第一个例子中,他们展示了使用区间算法来解决一个单变量全局优化问题,该问题与在预定年龄更换单个组件有关。区间算法同时考虑了所有参数的不确定性,能够为最优解提供严格的边界。然而,对于复杂的问题,所需的时间确实是有限的。在第二个例子中,他们考虑了一个多部件系统,以确定在不确定因素下协调维护频率的范围。实例表明,基于进化策略的优化方法可以很好地逼近具有参数不确定性的优化模型的取值范围。该方法比其他方法更快,并且不需要对要优化的函数进行假设。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Maintenance optimization under uncertainties using interval methods & evolutionary strategies
In this paper, the authors present two examples of maintenance optimization under uncertainties using interval arithmetic methods and evolutionary strategies. In the first example, they show the use of Interval Arithmetic to solve a single variable global optimization problem related to a replacement at predetermined age for a single component. Interval arithmetic considers the uncertainty of all the parameters at the same time and is able to provide strict bounds for the optimum solution. However, for complex problem, the amount of time required does pose a limitation. In the second example, they consider a multi-component system to determine the range of coordinated maintenance frequencies under to uncertainties. The example presented confirm that the proposed approach based on evolutionary strategies can be used to determine the range of an optimization model with uncertainties on the parameters as it produces a very good approximation. The approach is faster than other approaches and requires no assumption about the function to be optimized.
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