POWRE: Exploration of Applying Statistical Process Control Strategies to Simulated Annealing Optimization Techniques

9805972施密特这项研究将把统计过程控制(SPC)技术应用于模拟退火法优化算法的控制，该技术通常用于监控制造系统中的质量。模拟退火法是一种流行的复杂问题优化技术，因为它只需要计算每个潜在解的值。其他技术计算评估函数导数或进行矩阵运算。模拟退火法的缺点是在优化过程中必须对数千或数百万个解进行评估。初步研究支持使用统计过程控制方法来检测生产性搜索。生产性搜索被定义为导致最近一组建议解决方案的平均值发生重大变化的搜索。如果可检测到，则可以指示算法仅在搜索有效时运行。本文的工作是开发一个精细化的产生式搜索退火法检测原型实现算法，并为其提供基础理论支持。该算法将在NASA兰利研究中心多学科优化分部的工程问题测试套件中选定的问题上进行测试。这笔拨款使得对这一羽翼未丰的想法进行更彻底的调查，使研究人员能够将当前在设计理论和设计师辅助工具方面的工作与运筹学联系起来。

9805972 Schmidt This research will apply statistical process control (SPC) techniques, commonly used to monitor quality in manufacturing systems, to the control of simulated annealing optimization algorithms. Simulated annealing is a popular technique for complex problem optimization because it only requires calculations of the value of each potential solution. Other techniques calculate evaluation function derivatives or do matrix manipulations. Simulated annealing's drawback is that thousands or millions of solutions must be evaluated during the optimization process. Preliminary research supports the use of SPC methods to detect productive search. Productive search is defined as search resulting in a period of significant change in the average value of the most recent set of proposed solutions. If detectable, algorithms can be directed to run during only while the search is productive. This work is to develop a refined implementation of a prototype detection of productive search annealing algorithm and supporting foundational theory. The algorithm will be tested on selected problems from the Multidisciplinary Optimization Branch of NASA's Langley Research Center's test suite of engineering problems. This grant enables a more thorough investigation of this fledgling idea, allowing the researcher to bridge current work in design theory and designer assistance tools into operations research.