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

RI：小型：模型导向的混合：模型构建、元启发式和更传统的优化技术混合的原理设计

• 批准号: 1115352
• 负责人: Uday Chakraborty
• 金额: $ 44.6万
• 依托单位: University of Missouri-Saint Louis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-15 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1115352&HistoricalAwards=false
• 关键词: RI; Small; Model; Directed; Hybridization

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.

本计画将联合收割机与传统的最佳化技术相结合，着重于模型导向的混合优化的原理设计、强化、分析与应用。其关键思想是使用模型导向优化技术构建的问题景观模型，以促进决策的性质和可能的有效性，特定的本地搜索程序和适当的邻域结构，这些程序。所开发技术的重要性将在广泛的应用中得到证明，包括计算物理、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高级邻域结构和适用于广泛类别的问题的特定于问题的运算符，包括噪声、动态景观、复杂的结构和多重冲突的目标。除了推进计算优化之外，该项目还将对科学、工程和商业领域的各种其他学科产生强烈影响，因为它将为这些学科的从业者提供工具，使他们能够实际解决当前技术难以解决的问题。此外，该项目将提供先进的景观分析技术的开发方法，以及先进的元生态学和传统优化技术的理论研究。为了补充研究目标，该项目强调更广泛的影响，重点是为学生研究人员提供咨询，促进本科生的研究生指导，参与多学科和合作项目，确保研究成果的有效传播，组织学生竞赛，促进当地的研究基础设施，并支持在科学和工程领域代表性不足的群体。