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

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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.