MAiNGO – McCormick-based Algorithm for mixed-integer Nonlinear Global Optimization

MAiNGO – 基于 McCormick 的混合整数非线性全局优化算法

• 批准号: 442664501
• 负责人: Professor Alexander Mitsos, Ph.D.
• 金额: --
• 依托单位: Lehrstuhl Systemverfahrenstechnik (SVT)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2021
• 资助国家: 德国
• 起止时间: 2020-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/442664501?language=en
• 关键词: MAiNGO; McCormick; based; Algorithm; mixed

The objective of this project is to develop and provide a reliable and novel open-source software for deterministic global optimization of mixed-integer nonlinear optimization problems. A first version of MAiNGO is already being developed at the chair for Process Systems Engineering (AVT.SVT) RWTH Aachen University with the goal of an open-source publication. In contrast to other state-of-the-art deterministic global optimization solvers, MAiNGO does not increase the dimensionality of the user-defined optimization problem. This is achieved through the reduced-space formulation and the application of the so-called McCormick relaxations. The McCormick relaxations are constructed via the open-source package MC++. MAiNGO implements a specialized heuristic for the improvement of the resulting McCormick relaxations and also specialized relaxations for specific intrinsic functions (including functions commonly found in the field of chemical engineering). We have already shown, that MAiNGO exhibits computational advantages for problems formulated in a reduced-space manner. Problems profiting from a reduced-space formulation can be found in flowsheet optimization or data-driven optimization with artificial neural networks.MAiNGO can be used, adapted and extended by the research community. Currently, MAiNGO is a prototype software and misses an adequate documentation and unit testing. Moreover, the software has to be tested on published benchmarks from the global optimization community. The implementation of interfaces to common programming and modelling languages also represents one of goals of this project.

该项目的目标是开发和提供一个可靠的和新颖的开源软件，用于混合整数非线性优化问题的确定性全局优化。MAiNGO的第一个版本已经在亚琛大学过程系统工程（AVT.SVT）的椅子上开发，目标是开源出版物。与其他最先进的确定性全局优化求解器相比，MAiNGO不会增加用户定义的优化问题的维度。这是通过减少空间制定和应用所谓的麦考密克松弛。McCormick弛豫是通过开源软件包MC++构建的。MAiNGO实现了一种专门的启发式，用于改进所产生的McCormick弛豫，以及针对特定内在功能（包括化学工程领域中常见的功能）的专门弛豫。我们已经表明，MAiNGO表现出计算优势的问题，制定了减少空间的方式。从缩减空间公式中获益的问题可以在流程优化或人工神经网络的数据驱动优化中找到。MAiNGO可以被研究界使用，调整和扩展。目前，MAiNGO是一个原型软件，缺少足够的文档和单元测试。此外，该软件必须在全球优化社区发布的基准上进行测试。实现通用编程和建模语言的接口也是该项目的目标之一。