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DistributedDesignOptimizer: A modular Python framework for Setup, Execution and Processing of Distributed Design Optimization
Sebastian Ellmaier, Albert J. de Wit, Akilesh Raveendran, Marc-Eric Vogt, Thomas Bäck, Anna V. Kononova
TL;DR
Distributed design optimization coordinates coupled subsystems while preserving local design decisions, but its engineering use lacks comprehensive software support. The paper derives requirements and introduces DistributedDesignOptimizer, a modular Python framework. Existing frameworks do not sufficiently cover all identified features, and the new framework is still under development.
Problem
Promising distributed primal-dual and sensitivity-based methods are not widely used in engineering organizations, and comprehensive software for defining, executing, and analyzing distributed optimization is lacking.
Source: §3 · p. 5Method
The paper derives software requirements and introduces a modular Python framework that separates subsystem optimization, coordination, and information exchange while keeping problem definition independent of the coordination method.
Source: §1 · p. 2, §4 · p. 7, §5 · p. 12Results
Takeaways & Limitations
The framework records optimization histories and provides interactive post-processing for distributed design optimization.
Source: §5 · p. 12The framework remains under development, with planned improvements to its features, benchmark suite, cluster deployment, and GUI.
Source: §5 · p. 12
Abstract
from arXiv · showhide
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.
1 INTRODUCTION
Coupled engineering subsystems need coordinated optimization that preserves their control over local design decisions. The paper addresses the lack of general-purpose software for defining, executing, and analyzing distributed design optimization by introducing DistributedDesignOptimizer.
1 INTRODUCTION
- Coupled disciplines such as structures, aerodynamics, and propulsion are designed by specialized teams, motivating coordination that preserves subsystem autonomy over constraints, objectives, and design decisions.The paper frames this as a coordination need for engineered systems with coupled components.Source: §1 · p. 2
- Distributed design optimization coordinates coupled subsystem decisions toward system feasibility and optimality while retaining subsystem control over local design variables.By contrast, IDF and MDF distribute analysis but centralize design-variable decisions in a single optimizer.Source: §1 · p. 2
- A general-purpose framework combining algorithm-independent problem definition, interchangeable coordination methods, extensibility, benchmarking, and post-processing was missing.The authors identify this gap as an obstacle to applying promising coordination approaches.Source: §1 · p. 2
- DistributedDesignOptimizer separates problem definition from coordination methods within a modular structure for defining, executing, extending, and analyzing distributed optimization problems and algorithms.The work also derives software requirements and assesses existing frameworks.Source: §1 · p. 2
2 DISTRIBUTED DESIGN OPTIMIZATION
Distributed design optimization coordinates local subsystem optimization problems while addressing their couplings. The section describes formulations and coordination methods, then presents a shared algorithmic structure that accommodates distributed primal-dual and sensitivity-based methods.
2 DISTRIBUTED DESIGN OPTIMIZATION
- A decomposition reformulation treats shared design variables as additional subsystem variables and introduces pairwise coupling constraints between them.The resulting AIO formulation’s optimal solutions coincide with those of Equation (2) when the set jH is selected appropriately.Source: §2 · p. 3
- Distributed primal-dual methods and Sensitivity-based Distributed Programming are identified as the most suitable classes for the considered setting, which includes subsystem autonomy and bidirectional couplings.BLISS and CSSO impair subsystem autonomy, while CO and ATC are limited to unidirectional couplings.Source: §2 · p. 4
- Subsystems repeatedly solve local optimization problems augmented by coordination objectives or constraints, then exchange and update coupling quantities until convergence.The coordination parameters depend on information from neighboring subsystems and, where applicable, a controller.Source: §2 · p. 4
- The unified algorithmic structure includes distributed primal-dual and sensitivity-based methods as specializations of repeated subsystem solves and coordination updates.The iteration structure has inner and outer loops and can include a controller.Source: §2 · p. 4
3 LACK OF SUITABLE COMPREHENSIVE SOFTWARE
Distributed primal-dual and sensitivity-based methods are considered promising but lack comprehensive software support for engineering use. The section sets out six framework features and finds that existing frameworks do not cover them all.
3 LACK OF SUITABLE COMPREHENSIVE SOFTWARE
- A suitable framework should provide algorithm-independent problem definition, algorithm choice, modular extensibility, standardized benchmarks, processing and analysis, and local or cluster deployment.The authors connect these software needs to the limited use of promising distributed methods in engineering organizations.Source: §3 · pp. 5–6
- Prior software has varied limitations, including problem-specific research code, domain restrictions, limited algorithm support, and gaps in parallel execution or analysis.The reviewed frameworks include ADMM implementations, domain-focused tools [13, 17, 18, 19], and MDO or distributed-optimization frameworks [20, 21, 22, 23, 24, 25, 26].Source: §3 · pp. 6–7
- Existing frameworks provide partial building blocks, but none sufficiently covers all six identified features.The assessment compares frameworks against features using fully, partially, and not satisfied categories.Source: §3 · p. 7, Table 1 · p. 7
4 DistributedDesignOptimizer FRAMEWORK
DistributedDesignOptimizer is a modular Python framework for setting up, executing, and processing distributed design optimization. This section presents its architecture, an SSBJ problem setup, execution analysis, and ways to extend it with new algorithms.
4.1 Architecture Overview
- The architecture separates coordination, shared middle-level communication, and subsystem optimization, with interchangeable convergence, iteration, and coupling-update components.Middle-level storage mediates neighbor coupling exchange with multiprocessing locks; subsystems can use different numerical solvers.Source: §4.1 · pp. 7–8
4.2 Problem Definition
- Problem files separate optimization setup from coordination choices and configure ALC, Consensus ALC, ALADIN, or SBDP with interchangeable execution strategies.Source: §4.2 · pp. 9–10
4.3 Processing
- Execution histories capture per-iteration runtime, evaluations, variables, coupling data, and convergence flags; the Viewer loads and refreshes logs for customized cross-run analysis.Source: §4.3 · p. 11
- 3.36 · 10^4 is the best-known optimum approached as ALC coordination drives SSBJ coupling consistency.Source: Figure 4 · p. 11, §4.3 · p. 11
4.4 Extending with a Novel Algorithm
- Novel algorithms reuse base classes and extend only needed convergence, iteration, update, or method-specific coupling and coordination classes; gray classes are controller-only.Source: Figure 5 · p. 12, §4.4 · p. 12
5 CONCLUSION
DistributedDesignOptimizer provides modular support for distributed coordination methods, independent problem definition, distributed execution, and interactive post-processing. The framework remains under development, with an ambition to become an open-source standard for practical problems and method comparison.
5 CONCLUSION
- DistributedDesignOptimizer separates subsystem optimization, coordination, and information exchange; supports primal-dual and sensitivity-based methods; and records histories for interactive post-processing.Design problems can be defined independently of the coordination method.Source: §5 · p. 12
- Development priorities include dedicated variable classes, clearer separation of coordination, recording, and processing, cluster deployment, expanded benchmarks, and a holistic DDO Viewer.The benchmark suite should improve algorithm validation and comparison.Source: §5 · p. 12
- The authors envision DistributedDesignOptimizer as an open-source standard for practical problems and for implementing and comparing coordination methods.They welcome community contributions to benchmarks, methodological capabilities, and usability.Source: §5 · p. 12