A Regularized Inexact Penalty Decomposition Algorithm for Multidisciplinary Design Optimization Problem With Complementarity Constraints

2009 ◽  
Author(s):  
Shen Lu ◽  
Harrison M. Kim
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Optimization Problem ◽  
Original Problem ◽  
Equilibrium Problems ◽  
Decomposition Algorithm ◽  
Multidisciplinary Design ◽  
Stationary Points ◽  
Complementarity Constraints ◽  
Regularization Technique

Economic and physical considerations often lead to equilibrium problems in multidisciplinary design optimization (MDO), which can be captured by MDO problems with complementarity constraints (MDO-CC) — a newly emerging class of problem. Due to the ill-posedness associated with the complementarity constraints, many existing MDO methods may have numerical difficulties solving the MDO-CC. In this paper, we propose a new decomposition algorithm for MDO-CC based on the regularization technique and inexact penalty decomposition. The algorithm is presented such that existing proofs can be extended, under certain assumptions, to show that it converges to stationary points of the original problem and that it converges locally at a superlinear rate. Numerical computation with an engineering design example and several analytical example problems shows promising results with convergence to the all-in-one (AIO) solution.

A Regularized Inexact Penalty Decomposition Algorithm for Multidisciplinary Design Optimization Problems With Complementarity Constraints

2010 ◽  
Vol 132 (4) ◽  
Author(s):  
Shen Lu ◽  
Harrison M. Kim
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Original Problem ◽  
Equilibrium Problems ◽  
Optimization Problems ◽  
Decomposition Algorithm ◽  
Multidisciplinary Design ◽  
Stationary Points ◽  
Complementarity Constraints ◽  
Regularization Technique

Economic and physical considerations often lead to equilibrium problems in multidisciplinary design optimization (MDO), which can be captured by MDO problems with complementarity constraints (MDO-CC)—a newly emerging class of problem. Due to the ill-posedness associated with the complementarity constraints, many existing MDO methods may have numerical difficulties solving this class of problem. In this paper, we propose a new decomposition algorithm for the MDO-CC based on the regularization technique and inexact penalty decomposition. The algorithm is presented such that existing proofs can be extended, under certain assumptions, to show that it converges to stationary points of the original problem and that it converges locally at a superlinear rate. Numerical computation with an engineering design example and several analytical example problems shows promising results with convergence to the all-in-one solution.

Multidisciplinary design optimization of vehicle chassis product family

2021 ◽  
pp. 095440542110366
Author(s):  
Xiaokai Chen ◽  
Chenyu Wang ◽  
Guobiao Shi ◽  
Mingkai Zeng
Keyword(s):  
Design Optimization ◽  
Design Process ◽  
Multidisciplinary Design Optimization ◽  
Optimization Design ◽  
Optimization Problem ◽  
Product Family ◽  
Multidisciplinary Design ◽  
Product Family Design ◽  
Product Platforms ◽  
Product Family Optimization

In order to improve the performance of automotive product platforms and product families while keeping high development efficiency, a product family optimization design method that combines shared variable decision-making and multidisciplinary design optimization (MDO) is proposed. First, the basic concepts related to product family design optimization were clarified. Then, the mathematical description and MDO model of the product family optimization problem were established, and the improved product family design process was given. Finally, for the chassis product family optimization problem of an automotive product platform, the effectiveness of the proposed optimization method, and design process were exemplified. The results show that the collaboratively optimized product family can effectively handle the coordination between multiple products and multiple targets, compared to Non-platform development, it can maximize the generalization rate of vehicle parts and components under the premise of ensuring key performance, and give full play to the advantages of product platforms.

scholarly journals Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System

2021 ◽  
Vol 9 (5) ◽  
pp. 478
Author(s):  
Hao Chen ◽  
Weikun Li ◽  
Weicheng Cui ◽  
Ping Yang ◽  
Linke Chen
Keyword(s):  
Design Optimization ◽  
Optimization Algorithm ◽  
Multidisciplinary Design Optimization ◽  
Robotic Fish ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Cfd Method ◽  
The Individual

Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary design optimization problem. However, the research on the design optimization of robotic fish is rare. In this paper, by combining an efficient multidisciplinary design optimization approach and a novel multi-objective optimization algorithm, a multi-objective multidisciplinary design optimization (MMDO) strategy named IDF-DMOEOA is proposed for the conceptual design of a three-joint robotic fish system. In the proposed IDF-DMOEOA strategy, the individual discipline feasible (IDF) approach is adopted. A novel multi-objective optimization algorithm, disruption-based multi-objective equilibrium optimization algorithm (DMOEOA), is utilized as the optimizer. The proposed MMDO strategy is first applied to the design optimization of the robotic fish system, and the robotic fish system is decomposed into four disciplines: hydrodynamics, propulsion, weight and equilibrium, and energy. The computational fluid dynamics (CFD) method is employed to predict the robotic fish’s hydrodynamics characteristics, and the backpropagation neural network is adopted as the surrogate model to reduce the CFD method’s computational expense. The optimization results indicate that the optimized robotic fish shows better performance than the initial design, proving the proposed IDF-DMOEOA strategy’s effectiveness.

Support Vector Regression-Based Multidisciplinary Design Optimization for Ship Design

2012 ◽  
Author(s):  
Dongqin Li ◽  
Yifeng Guan ◽  
Qingfeng Wang ◽  
Zhitong Chen
Keyword(s):  
Design Optimization ◽  
Support Vector Regression ◽  
Design Process ◽  
Multidisciplinary Design Optimization ◽  
Initial Point ◽  
Ship Design ◽  
Collaborative Optimization ◽  
Multidisciplinary Design ◽  
Support Vector ◽  
Independent Design

The design of ship is related to several disciplines such as hydrostatic, resistance, propulsion and economic. The traditional design process of ship only involves independent design optimization within each discipline. With such an approach, there is no guarantee to achieve the optimum design. And at the same time improving the efficiency of ship optimization is also crucial for modem ship design. In this paper, an introduction of both the traditional ship design process and the fundamentals of Multidisciplinary Design Optimization (MDO) theory are presented and a comparison between the two methods is carried out. As one of the most frequently applied MDO methods, Collaborative Optimization (CO) promotes autonomy of disciplines while providing a coordinating mechanism guaranteeing progress toward an optimum and maintaining interdisciplinary compatibility. However there are some difficulties in applying the conventional CO method, such as difficulties in choosing an initial point and tremendous computational requirements. For the purpose of overcoming these problems, Design Of Experiment (DOE) and a new support vector regression algorithm are applied to CO to construct statistical approximation model in this paper. The support vector regression algorithm approximates the optimization model and is updated during the optimization process to improve accuracy. It is shown by examples that the computing efficiency and robustness of this CO method are higher than with the conventional CO method. Then this new Collaborative Optimization (CO) method using approximate technology is discussed in detail and applied in ship design which considers hydrostatic, propulsion, weight and volume, performance and cost. It indicates that CO method combined with approximate technology can effectively solve complex engineering design optimization problem. Finally, some suggestions on the future improvements are proposed.

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