RI: Small: Model-Directed Hybridization: Principled Design of Hybrids of Model Building, Metaheuristics and More Traditional Optimization Techniques

本项目将模型导向优化技术与传统优化技术相结合，重点研究面向模型的混合动力系统的原理设计、增强、分析和应用。关键思想是使用由模型导向优化技术构建的问题景观模型来促进对特定局部搜索过程的性质和可能的有效性以及这些过程的适当邻域结构的决策。所开发的技术的重要性将在广泛的应用中得到证明，包括计算物理学、生物学、化学和运筹学中的问题。该项目将对计算优化产生变革性影响，主要是因为它将自动设计先进的邻域结构和适用于各种优化问题的问题特定算子，包括噪声、动态景观、复杂结构和多个冲突目标的问题。除了推进计算优化之外，该项目还将对科学、工程和商业的其他学科产生重大影响，因为它将为这些学科的从业者提供工具，使当前技术难以解决的问题能够得到实际解决。此外，该项目将为开发景观分析的先进技术以及元启发式和传统优化技术的先进混合理论研究提供方法。为配合研究目标，该计划强调更广泛的影响，重点是为学生研究人员提供建议，促进研究生对本科生的指导，参与多学科和合作项目，确保研究成果的有效传播，组织学生比赛，促进当地研究基础设施，以及支持科学和工程领域代表性不足的团体。

This project focuses on the principled design, enhancement, analysis and application of model-directed hybrids, which combine model-directed optimization and traditional optimization techniques. The key idea is to use models of the problem landscape constructed by model-directed optimization techniques to facilitate decisions about the nature and likely effectiveness of particular local search procedures and appropriate neighborhood structures for those procedures. The importance of the developed techniques will be demonstrated in a broad spectrum of applications, including problems in computational physics, biology, chemistry and operations research.The project will have transformative effects on computational optimization mainly because it will automate the design of advanced neighborhood structures and problem-specific operators applicable to broad classes of optimization problems, including problems with noise, dynamic landscape, complex structure and multiple conflicting objectives. Besides advancing computational optimization, the project will have a strong impact on a variety of other disciplines in science, engineering and commerce because it will provide practitioners in these disciplines with tools that allow practical solutions of problems intractable with current techniques. Furthermore, the project will provide methodology for development of advanced techniques for landscape analysis as well as theoretical study of advanced hybrids of metaheuristics and traditional optimization techniques.To complement the research goals, the project puts a strong emphasis on broader impacts with the focus on advising student researchers, facilitating graduate mentoring of undergraduates, participating in multidisciplinary and collaborative projects, ensuring effective dissemination of research outcomes, organizing student competitions, boosting the local research infrastructure, and supporting groups underrepresented in science and engineering.