论文标题
Pysamoo：python中的替代辅助多目标优化
pysamoo: Surrogate-Assisted Multi-Objective Optimization in Python
论文作者
论文摘要
在过去的二十年中，已经付出了巨大的努力来解决计算上昂贵的优化问题，并提出了将替代物纳入优化的各种优化方法。但是，大多数优化工具箱都不包含用于计算昂贵问题的现成运行算法，尤其是与其他关键要求结合使用，例如处理多个相互冲突的目标或约束。因此，缺乏适当的软件包已成为解决现实世界应用程序的瓶颈。拟议的框架Pysamoo解决了现有优化框架的这些缺点，并提供了多种优化方法来处理涉及耗时评估功能的问题。该框架扩展了Pymoo的功能，Pymoo是一种流行而全面的多目标优化工具箱，并结合了替代物来支持昂贵的功能评估。该框架可根据GNU Affero通用公共许可证（AGPL）获得，主要是为了研究目的而设计的。有关Pysamoo的更多信息，鼓励读者访问：AnyOptimization.com/projects/pysamoo。
Significant effort has been made to solve computationally expensive optimization problems in the past two decades, and various optimization methods incorporating surrogates into optimization have been proposed. However, most optimization toolboxes do not consist of ready-to-run algorithms for computationally expensive problems, especially in combination with other key requirements, such as handling multiple conflicting objectives or constraints. Thus, the lack of appropriate software packages has become a bottleneck for solving real-world applications. The proposed framework, pysamoo, addresses these shortcomings of existing optimization frameworks and provides multiple optimization methods for handling problems involving time-consuming evaluation functions. The framework extends the functionalities of pymoo, a popular and comprehensive toolbox for multi-objective optimization, and incorporates surrogates to support expensive function evaluations. The framework is available under the GNU Affero General Public License (AGPL) and is primarily designed for research purposes. For more information about pysamoo, readers are encouraged to visit: anyoptimization.com/projects/pysamoo.
