ITR: Entropy Based Multi-Objective Genetic Algorithm With Constraint Handling and Set Quality Metrics

ITR：具有约束处理和集合质量指标的基于熵的多目标遗传算法

• 批准号: 0112767
• 负责人: Shapour Azarm
• 金额: $ 37.04万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-09-01 至 2004-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0112767&HistoricalAwards=false
• 关键词: ITR; Entropy; Based; Multi; Objective

This Information Technology Research project aims at developing an entropy based multi-objective genetic algorithm for design optimization. The algorithm is based on an analogy from statistical theory of gases in order to obtain a fullest possible representation of solutions. In the proposed algorithm, statistical behaviors of a sample of designs in a population will be simulated, as they evolve, according to those of the molecules of an ideal gas undergoing expansion in an enclosure. The goal of this analogy is to obtain solutions with maximum entropy, that is, maximum possible uniformity and coverage along the Pareto frontier. The investigation will involve the development of new methods for handling constraints in multi-objective design optimization as well as new quality metrics for assessing the "goodness" of a set of equally optimum (Pareto) solutions. The quality metrics will also be used to develop new convergence criteria for the algorithm. If successful, the outcome of the research will advance the state of the art at the level of computational tools that enable multi-objective engineering design optimization with applications to a wide class of problems. The results of this investigation will be broadly disseminated for the engineering design automation community, transferred and integrated into several courses at the graduate and undergraduate levels at the University of Maryland, and transferred to industry.

本资讯科技研究计划旨在发展一种以熵为基础的多目标遗传演算法，以进行设计最佳化。该算法是基于气体统计理论的类比，以获得尽可能完整的解决方案。在所提出的算法中，人口中的设计样本的统计行为将被模拟，因为它们的演变，根据那些在封闭空间中经历膨胀的理想气体的分子。 这种类比的目的是获得具有最大熵的解，即沿着帕累托边界沿着最大可能的均匀性和覆盖率。调查将涉及开发新的方法来处理多目标设计优化中的约束条件，以及新的质量指标来评估一组同样最优（帕累托）解决方案的“善良”。 质量指标也将用于开发新的收敛标准的算法。如果成功的话，研究的结果将在计算工具的水平上推进最先进的技术，使多目标工程设计优化与应用到广泛的一类问题。这项调查的结果将广泛传播的工程设计自动化社区，转移和整合到几个课程在研究生和本科生水平在马里兰州大学，并转移到行业。