In this work we explore the properties which make many real-life global optimization problems extremely difficult to handle, and some of the common techniques used in literature to address them. We then introduce a general optimization management tool called GloMPO (Globally Managed Parallel Optimization) to help address some of the challenges faced by practitioners. GloMPO manages and shares information between traditional optimization algorithms run in parallel. We hope that GloMPO will be a flexible framework which allows for customization and hybridization of various optimization ideas, while also providing a substitute for human interventions and decisions which are a common feature of optimization processes of hard problems. GloMPO is shown to produce lower minima than traditional optimization approaches on global optimization test functions, the Lennard-Jones cluster problem, and ReaxFF reparameterizations. The novel feature of forced optimizer termination was shown to find better minima than normal optimization. GloMPO is also shown to provide qualitative benefits such a identifying degenerate minima, and providing a standardized interface and workflow manager.

中文翻译：

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GloMPO（全局管理并行优化）：一种用于昂贵的黑盒优化的工具，应用于 ReaxFF 重新参数化


在这项工作中，我们探讨了使许多现实生活中的全局优化问题极难处理的属性，以及文献中用于解决这些问题的一些常用技术。然后，我们介绍一种称为 GloMPO（全局管理并行优化）的通用优化管理工具，以帮助解决从业者面临的一些挑战。 GloMPO 管理并共享并行运行的传统优化算法之间的信息。我们希望 GloMPO 将成为一个灵活的框架，允许定制和混合各种优化思想，同时也提供人类干预和决策的替代品，这是难题优化过程的共同特征。 GloMPO 在全局优化测试函数、Lennard-Jones 集群问题和 ReaxFF 重新参数化方面显示出比传统优化方法更低的最小值。强制优化器终止的新颖功能被证明可以比正常优化找到更好的最小值。 GloMPO 还被证明可以提供定性优势，例如识别退化最小值，并提供标准化界面和工作流程管理器。