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

本信息技术研究项目旨在开发一种用于设计优化的基于熵的多目标遗传算法。该算法基于气体统计理论的类比，以获得解的最完整的表示。在提出的算法中，将根据理想气体在封闭空间中膨胀的分子的统计行为，模拟种群中设计样本的统计行为，随着它们的演化。这个类比的目的是获得具有最大熵的解，即沿着帕累托边界的最大可能的一致性和覆盖率。这项调查将涉及开发多目标设计优化中处理约束的新方法，以及评估一组同等最优(帕累托)解的“优度”的新质量指标。质量指标也将被用来为算法制定新的收敛标准。如果成功，研究结果将在计算工具水平上推进最先进的技术，使多目标工程设计优化能够应用于广泛类别的问题。这项调查的结果将广泛传播给工程设计自动化界，转移并整合到马里兰大学研究生和本科生水平的几门课程中，并转移到行业中。

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.