Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System

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...

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Published in:	Journal of marine science and engineering Vol. 9; no. 5; p. 478
Main Authors:	Chen, Hao, Li, Weikun, Cui, Weicheng, Yang, Ping, Chen, Linke
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 2021
Subjects:	Adaptability Algorithms artificial neural network Autonomous underwater vehicles Back propagation Back propagation networks Biomimetics Complex systems Computational fluid dynamics computational fluid dynamics (CFD) Computer applications Design Design optimization Equilibrium Finite volume method Fish Fluid dynamics Fluid flow Fluid mechanics Hydrodynamics Mathematical models multi-objective optimization Multidisciplinary design optimization Multiple objective analysis Neural networks optimal design Optimization algorithms robotic fish Robotics Robots Simulation Turbulence models
ISSN:	2077-1312, 2077-1312
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Abstract	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.
AbstractList	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	Yang, Ping Li, Weikun Cui, Weicheng Chen, Hao Chen, Linke
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SubjectTerms	Adaptability Algorithms artificial neural network Autonomous underwater vehicles Back propagation Back propagation networks Biomimetics Complex systems Computational fluid dynamics computational fluid dynamics (CFD) Computer applications Design Design optimization Equilibrium Finite volume method Fish Fluid dynamics Fluid flow Fluid mechanics Hydrodynamics Mathematical models multi-objective optimization Multidisciplinary design optimization Multiple objective analysis Neural networks optimal design Optimization algorithms robotic fish Robotics Robots Simulation Turbulence models
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