๐ค AI Summary
This work addresses the lack of comprehensive software frameworks supporting distributed coordination, modular extensibility, and interactive processing in multidisciplinary design optimization (MDO). To bridge this gap, we developed DistributedDesignOptimizer, an open-source Python framework that integrates multiple distributed coordination algorithms within a highly scalable, modular architecture. The framework facilitates subsystem coupling coordination and local variable control while providing a complete toolchain encompassing problem formulation, algorithm execution, and post-processing. By filling a critical tooling void in the MDO domain, this project significantly enhances both the efficiency and flexibility of optimizing complex engineering systems.
๐ Abstract
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.