求解随机规划的仿真优化方法

Amany M. Akl, R. Sarker, D. Essam
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引用次数: 1

摘要

模拟优化的计算量非常大,特别是在大规模随机问题求解中,计算预算是一个重要的因素。较高的计算预算试图生成高度准确的解决方案,而较低的预算可能导致有偏差或不现实的解决方案。本文在仿真优化的背景下,研究了计算预算对求解质量的影响。研究采用多算子差分进化算法与蒙特卡罗模拟相结合的方法进行。实验方面,基于IEEE-CEC 2006约束优化竞争测试问题生成随机测试问题。实验结果提供了关于模拟优化行为的有趣见解,这将允许减少计算时间而不影响解决方案的质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Simulation optimization approach for solving stochastic programming
Simulation Optimization is computationally expensive, especially in large-scale stochastic problem solving, where the computational budget is considered as an important factor. A higher computational budget attempts to generate highly accurate solutions while a lower budget might result in biased or unrealistic solutions. In this paper, the effect of computational budget on the quality of the solution, in the context of Simulation Optimization, has been studied. The study is conducted by combining a Multi-operator Differential Evolutionary Algorithm with Monte-Carlo simulation. For experimentation, the stochastic test problems were generated based on the IEEE-CEC'2006 constrained optimization competition test problems. The experimental results provide interesting insights about the behavior of simulation optimization that would allow to reduce the computational time not compromising the quality of solutions.
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