发表机构
Leiden University; Royal Nethe（莱顿大学; 皇家尼德）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对多学科设计优化中缺乏全面软件框架的问题，提出了开源Python框架DistributedDesignOptimizer，该框架支持直观问题定义、分布式协调算法、模块化扩展、交互式后处理及分布式计算，填补了现有框架的空白。
AI 中文摘要
多学科设计优化（MDO）使得优化算法能够应用于复杂的工程系统，涵盖航空航天、汽车、机器人技术以及微电子等领域。此类多组件系统通常由耦合的子系统组成，每个子系统具有各自的设计变量、约束和目标。若缺乏对这些耦合的适当协调，子系统可能在孤立状态下达到最优，而整个系统仍处于次优甚至不可行状态。分布式设计优化协调耦合子系统的优化问题，同时允许它们保留对其局部设计变量的控制。尽管存在有前景的协调方法，但其应用受到缺乏全面软件框架的阻碍，该框架需支持直观的问题定义、提供合适的分布式协调算法、具备模块化和可扩展性、支持交互式（后）处理，并支持子系统执行的分布式计算。本工作为此类软件推导了需求，评估了现有框架是否符合这些需求，并介绍了分布式设计优化器（DistributedDesignOptimizer），一个填补这一空白的开源Python框架。
英文摘要
Multidisciplinary Design Optimization (MDO) enables the application of optimization algorithms to complex engineered systems, ranging from aerospace and automotive to robotics and microelectronics. Such multi-component systems typically consist of coupled subsystems, each characterized by its own design variables, constraints and objectives. Without adequate coordination of these couplings, subsystems may be optimal in isolation while the overall system remains suboptimal or even infeasible. Distributed design optimization coordinates coupled subsystem optimization problems while allowing them to retain control over their local design variables. Although promising coordination methods exist, their application is hindered by the lack of a comprehensive software framework which supports intuitive problem definition, provides suitable distributed coordination algorithms, is modular and extensible, enables interactive (post-)processing, and supports the distributed computation of subsystem executions. This work derives requirements for such software, assesses existing frameworks against them, and introduces DistributedDesignOptimizer, an open-source Python framework to fill this gap.
Commentsto be published in the conference proceedings of ECCOMAS 2026
