Application of multi-objective alliance algorithm to multidisciplinary design optimization under uncertainty

Author(s):  
Xiaoqian Chen ◽  
Xingzhi Hu ◽  
Valerio Lattarulo ◽  
Wen Yao ◽  
Yong Zhao
2021 ◽  
Vol 9 (5) ◽  
pp. 478
Author(s):  
Hao Chen ◽  
Weikun Li ◽  
Weicheng Cui ◽  
Ping Yang ◽  
Linke Chen

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.


Author(s):  
Mohammad Reza Farmani ◽  
Jafar Roshanian ◽  
Meisam Babaie ◽  
Parviz M Zadeh

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.


Author(s):  
Xuan Sun ◽  
Kjell Andersson ◽  
Ulf Sellgren

Design of haptic devices requires trade-off between many conflicting requirements, such as high stiffness, large workspace, small inertia, low actuator force/torque, and a small size of the device. With the traditional design and optimization process, it is difficult to effectively fulfill the system requirements by separately treating the different discipline domains. To solve this problem and to avoid sub-optimization, this work proposes a design methodology, based on Multidisciplinary Design Optimization (MDO) methods and tools, for design optimization of six degree-of-freedom (DOF) haptic devices for medical applications, e.g. simulators for surgeon and dentist training or for remote surgery. The proposed model-based and simulation-driven methodology aims to enable different disciplines and subsystems to be included in the haptic device optimization process by using a robust model architecture that integrates discipline-specific models in an optimization framework and thus enables automation of design activities in the concept and detail design phase. Because of the multi-criteria character of the performance requirements, multi-objective optimization is included as part of the proposed methodology. Because of the high-level requirements on haptic devices for medical applications in combination with a complex structure, models such as CAD (Computer Aided Design), CAE (Computer Aided Engineering), and kinematic models are considered to be integrated in the optimization process and presenting a systems view to the design engineers. An integration tool for MDO is used as framework to manage, integrate, and execute the optimization process. A case study of a 6-DOF haptic device based on a TAU structure is used to illustrate the proposed methodology. With this specific case, a Multi-objective Genetic Algorithm (MOGA) with an initial population based on a pseudo random SOBOL sequence and Monte Carlo samplings is used for the optimization.


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