Multi-Objective Pareto Concurrent Subspace Optimization for Multidisciplinary Design

AIAA Journal ◽  
2007 ◽  
Vol 45 (8) ◽  
pp. 1894-1906 ◽  
Author(s):  
C.-H. Huang ◽  
J. Galuski ◽  
C. L. Bloebaum
Keyword(s):  
Multidisciplinary Design ◽  
Multi Objective ◽  
Subspace Optimization ◽  
Concurrent Subspace Optimization

Concurrent Subspace Optimization Using Design Variable Sharing in a Distributed Computing Environment

1995 ◽  
Author(s):  
Brett A. Wujek ◽  
John E. Renaud ◽  
Stephen M. Batill ◽  
Jay B. Brockman
Keyword(s):  
Distributed Computing ◽  
Design Variable ◽  
High Performance ◽  
Low Cost ◽  
Multidisciplinary Design ◽  
Computing Environment ◽  
Subspace Optimization ◽  
System A ◽  
Implementation Studies ◽  
Concurrent Subspace Optimization

Abstract This paper reviews recent implementation advances and modifications in the continued development of a Concurrent Subspace Optimization (CSSO) algorithm for Multidisciplinary Design Optimization (MDO). The CSSO-MDO algorithm implemented in this research incorporates a Coordination Procedure of System Approximation (CP-SA) for design updates. Implementation studies detail the use of a new discipline based decomposition strategy which provides for design variable sharing across discipline design regimes (i.e., subspaces). The algorithm is implemented in a distributed computing environment, providing for concurrent discipline design. Implementation studies introduce a new multidisciplinary design test problem, the optimal design of a high performance, low cost structural system. A graphical user interface is developed which provides for menu driven execution and results display; this new programming environment highlights the modularity of the algorithm. Significant time savings are observed when using distributed computing for concurrent design across disciplines. The use of design variable sharing across disciplines does not introduce any difficulties in implementation as the design update in the CSSO-MDO algorithm is generated in the coordination procedure of system approximation (CP-SA).

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.

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.

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