Efficient Global Dynamic Optimization using Dynamic Cut Generation and Domain Reduction Techniques

使用动态剪切生成和域缩减技术进行高效的全局动态优化

基本信息

  • 批准号:
    1803706
  • 负责人:
  • 金额:
    $ 29.05万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2018
  • 资助国家:
    美国
  • 起止时间:
    2018-08-15 至 2019-10-31
  • 项目状态:
    已结题

项目摘要

Dynamic optimization is a computational approach used to optimally control dynamic processes. Effective dynamic optimization codes have become a key enabling technology in many industries, leading to substantial gains in profitability, efficiency, and safety. However, dynamic optimization problems commonly exhibit multiple sub-optimal local solutions. Use of these sub-optimal solutions instead of the desired globally optimal solution can lead to significant economic loss and performance degradation in many applications; this can even produce expensive or dangerous unreliable conclusions. This project aims to develop more efficient global optimization algorithms for types of problems that arise in a broad spectrum of applications in the chemical, pharmaceutical and aerospace industries.This project aims to increase the efficiency of global dynamic optimization (GDO) codes by developing cut generation and domain reduction techniques in the branch and bound (B&B) algorithm for solving nonconvex algebraic optimization problems. Cut generation refers broadly to methods that strengthen the convex relaxation of a nonconvex problem by imposing constraints that are redundant in the original model, but not in the relaxation. In contrast, domain reduction refers to methods that tighten the bounds on the decision variables in a B&B node using the problem constraints or a known feasible objective value. Research into such techniques for GDO is very well motivated by analogy to standard nonlinear programs (NLPs). Prior work on GDO has focused on relaxation methods that can be considered dynamic extensions of the most basic methods used for NLPs (specifically those based on factorable decomposition, such as McCormick relaxations). However, B&B codes based solely on these techniques are extremely inefficient in most cases. In contrast, modern B&B codes, which routinely solve problems with hundreds of decisions, utilize a rich toolbox of cut generation and domain reduction techniques. This strongly suggests that analogous techniques for dynamic problems will profoundly impact the efficiency of GDO algorithms. In addition to training graduate students, the proposed research will involve training of undergraduate researchers through Clemson's Creative Inquiry Program and rising high school seniors through Clemson's six-week Summer Program for Research Interns. The project also includes the development of a half-day long hands-on research activity for educating women and minorities about career opportunities in STEM fields, hosted by Clemson's Women in Science and Engineering (WISE) Program and the Programs for Educational Enrichment and Retention (PEER).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
动态优化是一种用于优化控制动态过程的计算方法。 有效的动态优化代码已经成为许多行业的关键技术,导致盈利能力,效率和安全性的大幅提高。然而,动态优化问题通常表现出多个次优局部解。 使用这些次优解决方案而不是期望的全局最优解决方案可能导致许多应用中的显著经济损失和性能下降;这甚至可能产生昂贵或危险的不可靠结论。本研究课题的目的是针对化学、制药、航空航天等领域的广泛应用,开发更有效的全局优化算法。本研究课题的目的是,在求解非凸代数优化问题的分支定界(B B)算法中,开发切割生成和区域缩减技术,提高全局动态优化(GDO)代码的效率。 割生成广义上是指通过施加在原始模型中冗余但在松弛中不冗余的约束来加强非凸问题的凸松弛的方法。相比之下,域缩减是指使用问题约束或已知的可行目标值来收紧B B节点中的决策变量的界限的方法。研究这些技术的GDO是非常好的动机类比标准的非线性规划(NLP)。GDO之前的工作主要集中在松弛方法上,这些方法可以被认为是用于NLP的最基本方法的动态扩展(特别是那些基于可因子分解的方法,如McCormick松弛)。然而,仅基于这些技术的B B码在大多数情况下是极其低效的。相比之下,现代B B代码通常解决数百个决策的问题,利用切割生成和域缩减技术的丰富工具箱。这有力地表明,类似的动态问题的技术将深刻影响GDO算法的效率。除了培训研究生外,拟议中的研究还将涉及通过克莱姆森的创造性探究项目培训本科研究人员,以及通过克莱姆森为期六周的研究实习生暑期项目培训高中高年级学生。该项目还包括开展为期半天的实践研究活动,教育妇女和少数民族了解STEM领域的职业机会,由克莱姆森的妇女在科学和工程(WISE)计划和教育丰富和保留(对等)计划主办该奖项反映了NSF的法定使命,并通过使用基金会的智力价值和更广泛的评估被认为值得支持。影响审查标准。

项目成果

期刊论文数量(0)
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Joseph Scott其他文献

Logic Guided Genetic Algorithms (Student Abstract)
逻辑引导遗传算法(学生摘要)
  • DOI:
    10.1609/aaai.v35i18.17873
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    D. Ashok;Joseph Scott;S. Wetzel;Maysum Panju;Vijay Ganesh
  • 通讯作者:
    Vijay Ganesh
Other things besides number: Abstraction, constraint propagation, and string variable types
  • DOI:
    10.1007/s10601-016-9263-9
  • 发表时间:
    2016-12-12
  • 期刊:
  • 影响因子:
    1.300
  • 作者:
    Joseph Scott
  • 通讯作者:
    Joseph Scott
Modifying the severity and appearance of psoriasis using deep learning to simulate anticipated improvements during treatment
利用深度学习来模拟治疗期间预期的改善,从而改变银屑病的严重程度和外观
  • DOI:
    10.1038/s41598-025-91238-y
  • 发表时间:
    2025-03-03
  • 期刊:
  • 影响因子:
    3.900
  • 作者:
    Joseph Scott;James A. Grant-Jacob;Matthew Praeger;George Coltart;Jonathan Sutton;Michalis N. Zervas;Mahesan Niranjan;Robert W. Eason;Eugene Healy;Ben Mills
  • 通讯作者:
    Ben Mills
An individualized approach to teaching adults with autism to successfully navigate job interviews via remote instruction.
一种通过远程指导教导患有自闭症的成年人成功应对工作面试的个性化方法。
  • DOI:
    10.1002/jaba.977
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    2.9
  • 作者:
    S. Kahng;Courtney Butler;Faris R. Kronfli;Christeen Zaki;Brianna Boragi;Joseph Scott
  • 通讯作者:
    Joseph Scott
Logic Guided Genetic Algorithms
逻辑引导遗传算法
  • DOI:
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    0
  • 作者:
    D. Ashok;Joseph Scott;S. Wetzel;Maysum Panju;Vijay Ganesh
  • 通讯作者:
    Vijay Ganesh

Joseph Scott的其他文献

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{{ truncateString('Joseph Scott', 18)}}的其他基金

NOVEL DECOMPOSITION ALGORITHMS FOR GUARANTEED GLOBAL OPTIMIZATION OF LARGE-SCALE NONCONVEX STOCHASTIC PROGRAMS
确保大规模非凸随机程序全局优化的新颖分解算法
  • 批准号:
    2232588
  • 财政年份:
    2023
  • 资助金额:
    $ 29.05万
  • 项目类别:
    Standard Grant
Efficient Global Dynamic Optimization using Dynamic Cut Generation and Domain Reduction Techniques
使用动态剪切生成和域缩减技术进行高效的全局动态优化
  • 批准号:
    1949747
  • 财政年份:
    2019
  • 资助金额:
    $ 29.05万
  • 项目类别:
    Standard Grant
Fault Detection and Diagnosis for Uncertain Nonlinear Systems Using Set-Based State Estimation
使用基于集合的状态估计对不确定非线性系统进行故障检测和诊断
  • 批准号:
    1949748
  • 财政年份:
    2019
  • 资助金额:
    $ 29.05万
  • 项目类别:
    Standard Grant
Fault Detection and Diagnosis for Uncertain Nonlinear Systems Using Set-Based State Estimation
使用基于集合的状态估计对不确定非线性系统进行故障检测和诊断
  • 批准号:
    1826011
  • 财政年份:
    2019
  • 资助金额:
    $ 29.05万
  • 项目类别:
    Standard Grant
RUI: Nuclear Cytology and Centrin in the Red Algae
RUI:红藻中的核细胞学和中心蛋白
  • 批准号:
    9008078
  • 财政年份:
    1990
  • 资助金额:
    $ 29.05万
  • 项目类别:
    Standard Grant
Comparative Nuclear Cytology and Ultrastructure in the Red Algae
红藻的比较核细胞学和超微结构
  • 批准号:
    8615288
  • 财政年份:
    1987
  • 资助金额:
    $ 29.05万
  • 项目类别:
    Standard Grant
PUI: Acquisition of a Transmission Electron Microscope
PUI:购买透射电子显微镜
  • 批准号:
    8411795
  • 财政年份:
    1984
  • 资助金额:
    $ 29.05万
  • 项目类别:
    Standard Grant
PUI: Comparative Nuclear Cytology in the Red Algae
PUI:红藻的比较核细胞学
  • 批准号:
    8307714
  • 财政年份:
    1983
  • 资助金额:
    $ 29.05万
  • 项目类别:
    Continuing Grant
Systematics of the Red Algae
红藻系统学
  • 批准号:
    7922205
  • 财政年份:
    1980
  • 资助金额:
    $ 29.05万
  • 项目类别:
    Continuing Grant

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磁层亚暴触发过程的全球(global)MHD-Hall数值模拟
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