Multi-objective collaborative multidisciplinary design optimization using particle swarm techniques and fuzzy decision making

2012 ◽  
Vol 226 (9) ◽  
pp. 2281-2295 ◽  
Author(s):  
Mohammad Reza Farmani ◽  
Jafar Roshanian ◽  
Meisam Babaie ◽  
Parviz M Zadeh
Keyword(s):  
Particle Swarm Optimization ◽  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Collaborative Optimization ◽  
Multidisciplinary Design ◽  
Fuzzy Decision ◽  
Swarm Optimization ◽  
Multi Objective

This article focuses on the efficient multi-objective particle swarm optimization algorithm to solve multidisciplinary design optimization problems. The objective is to extend the formulation of collaborative optimization which has been widely used to solve single-objective optimization problems. To examine the proposed structure, racecar design problem is taken as an example of application for three objective functions. In addition, a fuzzy decision maker is applied to select the best solution along the pareto front based on the defined criteria. The results are compared to the traditional optimization, and collaborative optimization formulations that do not use multi-objective particle swarm optimization. It is shown that the integration of multi-objective particle swarm optimization into collaborative optimization provides an efficient framework for design and analysis of hierarchical multidisciplinary design optimization problems.

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 for Small Vertical Wind Turbine Design

2014 ◽  
Vol 571-572 ◽  
pp. 1083-1086
Author(s):  
Qiu Yun Mo ◽  
Fei Deng ◽  
Shuai Shuai Li ◽  
Ke Yan Zhang
Keyword(s):  
Wind Turbine ◽  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Design Theory ◽  
Optimization Problems ◽  
Vertical Wind ◽  
Multidisciplinary Design ◽  
Existing Problems ◽  
Depth Study ◽  
Turbine Design

Multidisciplinary design optimization (MDO) represents the development direction of complex products design theory and method, it shows a huge advantage in solving complex optimization problems in engineering applications, for example product design. This paper briefly analyzes some existing problems of small vertical wind turbine, and puts forward using the theory of MDO in small vertical wind turbine structural optimization. Then,the paper analyzes and points out the key technology of using MDO theory to optimize small vertical wind turbine, and provides a new train of thought for further in-depth study of small vertical wind turbine to improve the overall performance of the small vertical wind turbine products.

Interaction balance optimization in multidisciplinary design optimization problems

2015 ◽  
Vol 24 (1) ◽  
pp. 48-57 ◽  
Author(s):  
Debiao Meng ◽  
Xiaoling Zhang ◽  
Yuan-Jian Yang ◽  
Huanwei Xu ◽  
Hong-Zhong Huang
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Optimization Problems ◽  
Multidisciplinary Design ◽  
Interaction Balance

scholarly journals Metamodel-Assisted Multidisciplinary Design Optimization of a Radial Compressor

2019 ◽  
Vol 4 (4) ◽  
pp. 35
Author(s):  
Mohamed H. Aissa ◽  
Tom Verstraete
Keyword(s):  
Design Optimization ◽  
Ordinary Kriging ◽  
Multidisciplinary Design Optimization ◽  
Optimization Problems ◽  
Pressure Ratio ◽  
Multidisciplinary Design ◽  
Feasible Region ◽  
Radial Compressor ◽  
Baseline Design ◽  
Operating Points

Kriging is increasingly used in metamodel-assisted design optimization. For expensive simulations; however, one can afford only a few samples to build the Kriging model, which consequently lacks prediction accuracy. We propose a bounded Kriging able to handle optimization problems with a small initial database. During the optimization, the proposed Kriging suggests designs close to database samples and finds optimal designs while staying in a feasible region (with respect to mesh and CFD convergence). The bounded Kriging is applied along with the ordinary Kriging to a multidisciplinary design optimization of a radial compressor. The shape of the compressor blades is optimized by considering the aero performance at different operating points and the mechanical stresses. The objective of the optimization is to maximize the efficiency at two operating points, while constraints are imposed on the maximum stress level in the material, the choke mass flow, the pressure ratio and the momentum of inertia of the impeller. While ordinary Kriging stopped prematurely because of many failing design evaluations, the bounded Kriging satisfied all constraints and reached an improvement of 2.59% in efficiency over the baseline design that does not comply with any constraints. The bounded Kriging covers a special need for robust methods in optimization able to deal with challenging geometries and a small database, which is the case for most industrial design optimizations.

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