Oppostition-based evolutionary algorithms: toward solving high-dimensional optimization problems efficiently

基于对立的进化算法：高效解决高维优化问题

• 批准号: 371992-2010
• 负责人: Rahnamayan, Shahryar
• 金额: $ 1.82万
• 依托单位: University of Ontario Institute of Technology
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=528448
• 关键词: Oppostition; based; evolutionary; algorithms; toward

Modeling of a scientific or engineering problem often leads to an optimization problem. Optimization provides a formal basis for decision making in a wide variety of applications, ranging from engineering design to service oriented applications such as healthcare, finance, and transportation. In this proposal, Evolutionary Algorithms (EAs) will be investigated to solve those optimization problems, which are otherwise difficult or impossible to solve by classical methods. Solving problems with mixed-type variables, dynamic environments, multi-objective constrained functions, and non-analytical functions are examples that highlight the outstanding capabilities of EAs. But currently, EAs are computationally expensive because of their evolutionary nature. Furthermore, EAs suffer from the problem of dimensionality. This means that their performance deteriorates quickly as the dimensionality of the search space increases. Rapid solutions of the problems are highly desirable during research and design processes. As a consequence, acceleration of EAs is a significant issue of importance for the scientific community to solve large-scale problems in a smaller time interval. This research develops new acceleration schemes for EAs, and also smart sampling methods, which are needed particularly for high-dimensional problems. Combining these proposed schemes and sampling methods would result in more efficient and robust approaches to accelerate well-known evolutionary algorithms and effectively solve high-dimensional problems. Reduction of function evaluations for time-consuming optimization problems is highly demanding. Solving high-dimensional expensive optimization problems is a challenging research area. This proposal aims to make significant contributions to this field. It would provide a very good training environment for highly qualified personnel, who can become Canada's future leaders and pioneering researchers in optimization techniques for applications in science and engineering. These applications depend on successfully finding optimum values for a hundred parameters during design of a product or solving a scientific problem.

科学或工程问题的建模通常会导致优化问题。优化为各种应用中的决策提供了正式的基础，从工程设计到面向服务的应用（例如医疗保健、金融和运输）。在该提案中，将研究进化算法（EA）来解决那些经典方法很难或不可能解决的优化问题。解决混合类型变量、动态环境、多目标约束函数和非分析函数的问题是突出 EA 卓越能力的例子。但目前，由于 EA 的进化性质，其计算成本很高。此外，EA 还存在维数问题。这意味着随着搜索空间维度的增加，它们的性能会迅速恶化。在研究和设计过程中，非常需要快速解决问题。因此，EA 的加速对于科学界在更短的时间间隔内解决大规模问题来说是一个重要的问题。这项研究开发了新的 EA 加速方案以及智能采样方法，这对于高维问题尤其需要。结合这些提出的方案和采样方法将产生更有效和鲁棒的方法来加速众所周知的进化算法并有效地解决高维问题。减少耗时的优化问题的函数评估要求很高。解决高维昂贵的优化问题是一个具有挑战性的研究领域。该提案旨在为该领域做出重大贡献。它将为高素质人才提供非常良好的培训环境，使他们能够成为加拿大未来的领导者和科学和工程应用优化技术的先驱研究人员。这些应用取决于在产品设计或解决科学问题期间成功找到一百个参数的最佳值。