COLLABORATIVE OPTIMIZATION WITH DIMENSION REDUCTION

2010 ◽  
Vol 01 (02) ◽  
pp. 179-198 ◽  
Author(s):  
ZHENXIAO GAO ◽  
TIANYUAN XIAO ◽  
WENHUI FAN
Keyword(s):  
Dimension Reduction ◽  
Multidisciplinary Design Optimization ◽  
Optimization Problem ◽  
Convergence Property ◽  
Extreme Points ◽  
Optimization Methods ◽  
Collaborative Optimization ◽  
System Level ◽  
Design Variables ◽  
Feasible Domain

Collaborative optimization (CO) method is widely used in solving multidisciplinary design optimization (MDO) problems, yet its computation requirement has been an obstacle to the applications, leading to doubts about CO's convergence property. The feasible domain of CO problem is first examined and it is proven that feasible domain remains the same during the CO formulation. So is the same with extreme points. Then based on contemporary research conclusion that the system-level optimization problem suffers from inherent computational difficulties, it is further pointed out that the employment of meta-heuristic optimization methods in CO could eliminate these difficulties. To make CO more computational feasible, a new method collaborative optimization with dimension reduction (CODR) is proposed. It focused on optimization dimension reduction and lets local copy of common shared design variables equal system shared design variables directly. Thus, the number of dimensions that CODR could reduce equal the number of common shared design variables. Numerical experiment suggests that CODR reduces computations greatly without losing of optimization accuracy.

Collaborative Optimization of Systems Involving Discrete Design at the Discipline Level

1998 ◽  
Vol 120 (1) ◽  
pp. 32-39 ◽  
Author(s):  
R. J. Balling ◽  
D. L Gale
Keyword(s):  
Multidisciplinary Design Optimization ◽  
Optimization Technique ◽  
Collaborative Optimization ◽  
System Level ◽  
Design Teams ◽  
Structural Members ◽  
Cross Sectional ◽  
Design Variables ◽  
Gradient Based ◽  
Structural Configurations

The multidisciplinary design optimization technique known as collaborative optimization is applied to two example problems to illustrate the flexibility that the technique extends to disciplinary design teams. In the first problem, disciplinary design variables are discrete-valued representing the cross-sectional dimensions of standardized shapes for structural members in a frame. In the second problem, discrete disciplinary design variables are used to represent the choice between different structural configurations of a truss tower. In both problems, disciplinary design was performed by the non-gradient-based strategy, exhaustive search. Nevertheless, system-level optimization was performed by a gradient-based strategy using simple formulas for the necessary gradients.

Application of Multidisciplinary Design Optimization to a Resource Satellite

2012 ◽  
Vol 195-196 ◽  
pp. 1066-1077
Author(s):  
Wen Rui Wu ◽  
Hai Huang ◽  
Bei Bei Wu
Keyword(s):  
Design Optimization ◽  
System Design ◽  
Multidisciplinary Design Optimization ◽  
Research Work ◽  
Thermal Control ◽  
Satellite System ◽  
Collaborative Optimization ◽  
System Level ◽  
Multidisciplinary Design ◽  
Satellite Mission

Satellite system design is a process involving various branches of knowledge, in which the designer usually needs to tradeoff many essentials and takes remarkable time. While multidisciplinary design optimization (MDO) method provides an effective approach for complicated system design, it seems especially suitable for such kind design purpose. By applying MDO in satellite system design, the efficiency of design can be expected to be improved and powerful technical supports can be obtained, which means better performance, faster design process and lower cost. According to the Resource satellite mission, width of ground cover and ground resolution are taken as the performance measurement, which combined with total mass of satellite is accounted in the optimization objective in system level. The design variables and constraints of the problem are dealt with disciplines or subsystems such as GNC, power, structure and thermal control. Corresponding analysis modules close to practical engineering are modeled. A MDO program system is developed by integrating collaborative optimization (CO) methods in iSIGHT. The result shows that the comprehensive objective can be improved, which also indicates MDO is feasible and efficient to solve the spacecraft design problem. The technology can be consulted for further research work.

scholarly journals Interaction Prediction Optimization in Multidisciplinary Design Optimization Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Debiao Meng ◽  
Xiaoling Zhang ◽  
Hong-Zhong Huang ◽  
Zhonglai Wang ◽  
Huanwei Xu
Keyword(s):  
Interaction Design ◽  
Large Scale ◽  
Optimization Problems ◽  
Collaborative Optimization ◽  
System Level ◽  
Design Parameters ◽  
Large Scale Systems ◽  
Interaction Prediction ◽  
Design Variables ◽  
Subsystem Level

The distributed strategy of Collaborative Optimization (CO) is suitable for large-scale engineering systems. However, it is hard for CO to converge when there is a high level coupled dimension. Furthermore, the discipline objectives cannot be considered in each discipline optimization problem. In this paper, one large-scale systems control strategy, the interaction prediction method (IPM), is introduced to enhance CO. IPM is utilized for controlling subsystems and coordinating the produce process in large-scale systems originally. We combine the strategy of IPM with CO and propose the Interaction Prediction Optimization (IPO) method to solve MDO problems. As a hierarchical strategy, there are a system level and a subsystem level in IPO. The interaction design variables (including shared design variables and linking design variables) are operated at the system level and assigned to the subsystem level as design parameters. Each discipline objective is considered and optimized at the subsystem level simultaneously. The values of design variables are transported between system level and subsystem level. The compatibility constraints are replaced with the enhanced compatibility constraints to reduce the dimension of design variables in compatibility constraints. Two examples are presented to show the potential application of IPO for MDO.

Hybrid reliability-based multidisciplinary design optimization with random and interval variables

2017 ◽  
Vol 232 (1) ◽  
pp. 52-64 ◽  
Author(s):  
Fan Yang ◽  
Zhufeng Yue ◽  
Lei Li ◽  
Dong Guan
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Region Of Interest ◽  
Kriging Model ◽  
Collaborative Optimization ◽  
Multidisciplinary Design ◽  
Learning Function ◽  
Design Variables ◽  
Interval Variables ◽  
Hybrid Reliability

This article presents a procedure for reliability-based multidisciplinary design optimization with both random and interval variables. The sign of performance functions is predicted by the Kriging model which is constructed by the so-called learning function in the region of interest. The Monte Carlo simulation with the Kriging model is performed to evaluate the failure probability. The sample methods for the random variables, interval variables, and design variables are discussed in detail. The multidisciplinary feasible and collaborative optimization architectures are provided with the proposed method. The method is demonstrated with three examples.

scholarly journals Optimal Reconfiguration of a Parallel Robot for Forward Singularities Avoidance in Rehabilitation Therapies. A Comparison via Different Optimization Methods

2020 ◽  
Vol 12 (14) ◽  
pp. 5803 ◽  
Author(s):  
Carlos Llopis-Albert ◽  
Francisco Valero ◽  
Vicente Mata ◽  
José L. Pulloquinga ◽  
Pau Zamora-Ortiz ◽  
...  
Keyword(s):  
Degrees Of Freedom ◽  
Local Minimum ◽  
Optimization Problem ◽  
Optimization Methods ◽  
Parallel Robot ◽  
Generalized Coordinates ◽  
Design Variables ◽  
Optimal Reconfiguration ◽  
Gradient Based ◽  
Parallel Kinematic

This paper presents an efficient algorithm for the reconfiguration of a parallel kinematic manipulator with four degrees of freedom. The reconfiguration of the parallel manipulator is posed as a nonlinear optimization problem where the design variables correspond to the anchoring points of the limbs of the robot on the fixed platform. The penalty function minimizes the forces applied by the actuators during a specific trajectory. Some constraints are imposed to avoid forward singularities and guarantee the feasibility of the active generalized coordinates for a certain trajectory. The results are compared with different optimization approaches with the aim of avoiding getting trapped into a local minimum and undergoing forward singularities. The comparison covers evolutionary algorithms, heuristics optimizers, multistrategy algorithms, and gradient-based optimizers. The proposed methodology has been successfully tested on an actual parallel robot for different trajectories.

Development of a Changeable Airfoil Optimization Model for Use in the Multidisciplinary Design of Unmanned Aerial Vehicles

2009 ◽  
Author(s):  
Scott Ferguson ◽  
Andrew H. Tilstra ◽  
Carolyn C. Seepersad ◽  
Kristin L. Wood
Keyword(s):  
Optimization Problem ◽  
Design Research ◽  
Optimization Problems ◽  
Mathematical Optimization ◽  
Operating Conditions ◽  
Multidisciplinary Optimization ◽  
System Level ◽  
Airfoil Optimization ◽  
Design Variables ◽  
Subsystem Design

Complex systems need to perform in a variety of functional states and under varying operating conditions. Therefore, it is important to manage the different values of design variables associated with the operating states for each subsystem. The research presented in this paper uses multidisciplinary optimization (MDO) and changeable systems methods together in the design of a reconfigurable Unmanned Aerial Vehicle (UAV). MDO is a useful approach for designing a system that is composed of distinct disciplinary subsystems by managing the design variable coupling between the subsystem and system level optimization problems. Changeable design research addresses how changes in the physical configuration of products and systems can better meet distinct needs of different operating states. As a step towards the development of a realistic reconfigurable UAV optimization problem, this paper focuses on the performance advantage of using a changeable airfoil subsystem. Design principles from transformational design methods are used to develop concepts that determine how the design variables are allowed to change in the mathematical optimization problem. The performance of two changeable airfoil concepts is compared to a fixed airfoil design over two different missions that are defined by a sequence of mission segments. Determining the configurations of the static and changeable airfoils is accomplished using a genetic algorithm. Results from this study show that aircraft with changeable airfoils attain increased performance, and that the manner by which the system transforms is significant. For this reason, the changeable airfoil optimization developed in this paper is ready to be integrated into a complete MDO problem for the design of a reconfigurable UAV.

Application of a Decomposition Technique for Treating a Shape Optimization Problem

1989 ◽  
Author(s):  
M. Bremicker ◽  
H. Eschenauer
Keyword(s):  
Sensitivity Analysis ◽  
Shape Optimization ◽  
Optimization Problem ◽  
Optimization Methods ◽  
Objective Functions ◽  
Shape Optimization Problem ◽  
Boundary State ◽  
Design Variables ◽  
Dimensional Shape ◽  
Suitable Approximation

Abstract The range of application of structural optimization methods can be considerably enlarged by using decomposition techniques. In this paper a novel procedure is introduced to deal with such problems more efficiently. The mechanical structure resp. system is divided into several subsystems splitting up the design variables, objective functions, and constraints accordingly. The boundary state quantities of the subsystems and the global (i.e. subsystem overlapping) functions are approximated by a sensitivity analysis of the entire system using suitable approximation concepts. It is thus possible to optimize the subsystems independently. Variables, objective functions and constraints can be chosen arbitrarily; all coupling information is obtained from the sensitivity analysis by means of global information. The application of this technique is demonstrated by a two-dimensional shape optimization problem.

Performance Measure Approach Based Multidisciplinary Design Optimization of Gear Transmission

2013 ◽  
Vol 694-697 ◽  
pp. 868-871
Author(s):  
Jun Zhang ◽  
Bing Zhang
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Performance Measure ◽  
Collaborative Optimization ◽  
Gear Transmission ◽  
System Level ◽  
Multidisciplinary Design ◽  
Engineering Systems ◽  
Performance Measure Approach ◽  
Design Cost

In order to reduce the influence of uncertainties on complicated engineering systems performance, a new method is proposed based on the performance measure approach and collaborative optimization (PMA-CO) to implement the reliability-based multidisciplinary design optimization of gear transmission. Both the mathematical model and procedures of PMA-CO are presented. With the adoption of slack factors in the system-level of collaborative optimization, both CO and PMA-CO are applied to the optimization of gear transmission. The proposed PMA-CO improves the reliability of the gear transmission and gained a tradeoff solution between design cost and reliability. Therefore, the PMA-CO is effective and practical in engineering design.

Component-Oriented Decomposition for Collaborative Optimization in Reinforcement Concrete Bridge Design

2011 ◽  
Vol 374-377 ◽  
pp. 2405-2410
Author(s):  
Lian Fa Wang ◽  
Ai Ping Tang
Keyword(s):  
Optimization Design ◽  
Target Function ◽  
Inequality Constraints ◽  
Optimization Methods ◽  
Collaborative Optimization ◽  
System Level ◽  
Optimization Strategy ◽  
Bridge Design ◽  
Direct Construction ◽  
Consistency Constraints

In order to implement the bi-level optimization strategy-collaborative optimization (CO) to bridge design, bridge optimization design process is subdivided into three subsystems in terms of component-oriented decomposition: superstructure subsystem, bearing subsystem and substructure subsystem. For system level, target function is formulated with the total direct construction cost, and inequality constraints induced relaxation factors are adopted to relax the intersubsystem consistency constraints. For subsystems, target functions are formulated with discrepancy expressions and constraints are formulated according to corresponding codes demands respectively. The feasibility and validity of the proposed approach are examined with an optimization process of reinforcement concrete box girder bridge. Optimization results from proposed approach are compared with that from mono-discipline optimization. The proposed approach shows high computing efficiency than mono-discipline optimization methods when achieving same optimization results.

Multidisciplinary design optimization of stiffened panels using collaborative optimization and artificial neural network

2017 ◽  
Vol 232 (20) ◽  
pp. 3595-3611 ◽  
Author(s):  
Hamda Chagraoui ◽  
Mohamed Soula
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Problem ◽  
Optimization Method ◽  
Collaborative Optimization ◽  
System Level ◽  
Stiffened Panels ◽  
Multi Objective ◽  
Efficiency And Effectiveness ◽  
Artificial Neural

A new method for solving the multidisciplinary design optimization problems with a minimal computational effort is presented. The proposed methodology is based on the combination of artificial neural network model and Improved Multi-Objective Collaborative Optimization. In the artificial neural network–Improved Multi-Objective Collaborative Optimization scheme, the back-propagation algorithm is used for training the artificial neural network metamodel and the Non-dominated Sorting Genetic Algorithm-II is used to search a Pareto optimality set for the objective functions of stiffened panels. The artificial neural network–Improved Multi-Objective Collaborative Optimization algorithm aims firstly to decompose the global optimization problem hierarchically into optimization design problem at system level and several sub-problems at sub-system level and secondly to replace each optimization problem at the system and subsystem levels by artificial neural network model to limit the computational cost. To highlight the efficiency and effectiveness of the proposed artificial neural network–Improved Multi-Objective Collaborative Optimization method, mathematical and engineering examples are presented. Results obtained from the application of the artificial neural network–Improved Multi-Objective Collaborative Optimization approach to an optimization problem of a stiffened panel are compared with those obtained by traditional optimization without using prediction tools. The new method (artificial neural network–Improved Multi-Objective Collaborative Optimization) was proven to be superior to traditional optimization. These results have confirmed the efficiency and effectiveness of the artificial neural network–Improved Multi-Objective Collaborative Optimization method. In addition, it converges at faster rate than traditional optimization. The traditional optimization method converges within 7918 s, while artificial neural network–Improved Multi-Objective Collaborative Optimization requires only 42 s, clearly, the artificial neural network–Improved Multi-Objective Collaborative Optimization method is much more efficient.

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