scholarly journals Hybrid Approach of Finite Element Method, Kigring Metamodel, and Multiobjective Genetic Algorithm for Computational Optimization of a Flexure Elbow Joint for Upper-Limb Assistive Device

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Duc Nam Nguyen ◽  
Thanh-Phong Dao ◽  
Ngoc Le Chau ◽  
Van Anh Dang
Keyword(s):  
Elbow Joint ◽  
Strain Energy ◽  
Hybrid Approach ◽  
Hybrid Optimization ◽  
Assistive Device ◽  
Optimization Approach ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Multiobjective Genetic Algorithm ◽  
Design Variables

Modeling for robotic joints is actually complex and may lead to wrong Pareto-optimal solutions. Hence, this paper develops a new hybrid approach for multiobjective optimization design of a flexure elbow joint. The joint is designed for the upper-limb assistive device for physically disable people. The optimization problem considers three design variables and two objective functions. An efficient hybrid optimization approach of central composite design (CDD), finite element method (FEM), Kigring metamodel, and multiobjective genetic algorithm (MOGA) is developed. The CDD is used to establish the number of numerical experiments. The FEM is developed to retrieve the strain energy and the reaction torque of joint. And then, the Kigring metamodel is used as a black-box to find the pseudoobjective functions. Based on pseudoobjective functions, the MOGA is applied to find the optimal solutions. Traditionally, an evolutionary optimization algorithm can only find one Pareto front. However, the proposed approach can generate 6 Pareto-optimal solutions, as near optimal candidates, which provides a good decision-maker. Based on the user’s real-work problem, one of the best optimal solutions is chosen. The results found that the optimal strain energy is about 0.0033 mJ and the optimal torque is approximately 588.94 Nm. Analysis of variance is performed to identify the significant contribution of design variables. The sensitivity analysis is then carried out to determine the effect degree of each parameter on the responses. The predictions are in a good agreement with validations. It confirms that the proposed hybrid optimization approach has an effectiveness to solve for complex optimization problems.

scholarly journals An Efficient Hybrid Approach of Finite Element Method, Artificial Neural Network-Based Multiobjective Genetic Algorithm for Computational Optimization of a Linear Compliant Mechanism of Nanoindentation Tester

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Ngoc Le Chau ◽  
Thanh-Phong Dao ◽  
Van Thanh Tien Nguyen
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Finite Element Method ◽  
Artificial Neural Network ◽  
Compliant Mechanism ◽  
Hybrid Optimization ◽  
Pareto Optimal ◽  
Optimization Approach ◽  
Optimal Solutions ◽  
Multiobjective Genetic Algorithm

This paper proposes a new evolutionary multiobjective optimization technique for a linear compliant mechanism of nanoindentation tester. The mechanism design is inspired by the elastic deformation of flexure hinge. To improve overall static performances, a multiobjective optimization design was carried out. An efficient hybrid optimization approach of central composite design (CDD), finite element method (FEM), artificial neural network (ANN), and multiobjective genetic algorithm (MOGA) is developed to solve the optimization problem. In this procedure, the CDD is used to lay out the experimental data. The FEM is developed to retrieve the quality performances. And then, the ANN is developed as black box to call the pseudo-objective functions. Unlike previous studies on multiobjective evolutionary algorithms, most of which generating only one Pareto-optimal solution, this proposed approach can generate more than three Pareto-optimal solutions. Based on the user’s real-work problem, one of the best optimal solutions is chosen. The results showed that the optimal results were found at the displacement of 330.68 μm, stress of 140.65 MPa, and safety factor of 3.6. The statistical analysis is conducted to investigate the behavior of the MOGA. The sensitivity analysis was carried out to determine the significant contribution of each factor. The results revealed that the lengths and thickness almost significantly affect both responses. It confirms that the proposed hybrid optimization approach gains high robustness and effectiveness with flexible decision maker rules to solve complex optimization engineering problems.

Solving Multi-Objective Multicast Routing Problem Using a New Hybrid Approach

2018 ◽  
Vol 9 (4) ◽  
pp. 22-36
Author(s):  
Mohammed Mahseur ◽  
Abdelmadjid Boukra ◽  
Yassine Meraihi
Keyword(s):  
Multicast Routing ◽  
Hybrid Approach ◽  
Bat Algorithm ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Routing Problem ◽  
Multi Objective ◽  
Strength Pareto Evolutionary Algorithm ◽  
Quality Of Service Parameters

Multicast routing is the problem of finding the spanning tree of a set of destinations whose roots are the source node and its leaves are the set of destination nodes by optimizing a set of quality of service parameters and satisfying a set of transmission constraints. This article proposes a new hybrid multicast algorithm called Hybrid Multi-objective Multicast Algorithm (HMMA) based on the Strength Pareto Evolutionary Algorithm (SPEA) to evaluate and classify the population in dominated solutions and non-dominated solutions. Dominated solutions are evolved by the Bat Algorithm, and non-dominated solutions are evolved by the Firefly Algorithm. Old and weak solutions are replaced by new random solutions by a process of mutation. The simulation results demonstrate that the proposed algorithm is able to find good Pareto optimal solutions compared to other algorithms.

Multiobjective Weighting Selection for Optimization-Based Control Design

1997 ◽  
Vol 122 (3) ◽  
pp. 567-569 ◽  
Author(s):  
Ricardo H. C. Takahashi ◽  
Juan F. Camino and ◽  
Douglas E. Zampieri ◽  
Pedro L. D. Peres
Keyword(s):  
Control Design ◽  
Active Suspension ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Cost Functional ◽  
Design Variables ◽  
Optimal Controllers ◽  
Selection For ◽  
Multiobjective Design

A methodology for the multiobjective design of controllers is presented, motivated by the problem of designing an active suspension controller. This problem has, as a particular feature, the possibility of being defined with two design variables only. The multiobjective controller is searched inside the space of “optimal controllers” defined by a weighted cost functional. The weightings are taken as the optimization variables for the multiobjective design. The method leads to (local) Pareto-optimal solutions and allows the direct specification of controller constraints in terms of some primary objectives which are taken into account in the multiobjective search. [S0022-0434(00)01403-9]

Multi-Objective Optimization of Film-Cooling Holes Considering Heat Transfer and Aerodynamic Loss

2011 ◽  
Author(s):  
Ki-Don Lee ◽  
Sun-Min Kim ◽  
Kwang-Yong Kim
Keyword(s):  
Film Cooling ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Aerodynamic Loss ◽  
Multi Objective ◽  
Design Variables ◽  
Expansion Angle ◽  
Objective Function Values ◽  
Film Cooling Holes

In the present work, multi-objective shape optimization of a row of laidback fan shaped film cooling holes has been performed using a hybrid multi-objective evolutionary approach in order to achieve an acceptable compromise between two competing objectives, i.e., enhancement of the film cooling effectiveness and reduction of the aerodynamic loss. In order to perform comprehensive optimization of film-cooling hole shape, the injection angle of the hole, the lateral expansion angle of the diffuser, the forward expansion angle of the hole and the pitch to hole diameter ratio, are chosen as design variables. Forty experimental designs within design spaces are selected by Latin hypercube sampling method. The response surface approximation method is used to construct the surrogate with objective function values for the experimental designs calculated through Reynolds-averaged Navier-Stokes analysis. The shear stress transport turbulence model is used as a turbulence closure. The optimization results are processed by the Pareto-optimal method. The Pareto optimal solutions are obtained using a combination of the evolutionary algorithm NSGA-II and a local search method. The optimum designs are grouped by k-means clustering technique and the six optimal points selected in the Pareto optimal solutions are evaluated by numerical analysis. The different trends in the variations of the design variables for each blowing ratios were found, and the optimum designs show enhanced objective function values.

Multi-Objective Optimization for a Novel Electrostatic-Feedback Micro-Sensor Based on Genetic Algorithm

2007 ◽  
Author(s):  
Yongquan Wang ◽  
Hualing Chen ◽  
Zhiying Ou ◽  
Xueming He
Keyword(s):  
Genetic Algorithm ◽  
Multidisciplinary Design Optimization ◽  
Optimal Solution ◽  
Optimization Approach ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Micro Sensor ◽  
Energy Relations

In this paper, we present the multi-objective optimization for an entire microsystem, a novel capacitive electrostatic feedback accelerometer. From the energy relations of the coupled electrostatic-field, the dynamic model of the system is constructed. Aiming at the global performance, a multi-objective optimization model, where sensitivity, resolution and damping resonant frequency are selected as objectives, is established based on the concept of multidisciplinary design optimization (MDO). Genetic algorithm (GA) is used to solve this problem, and compared with a traditional optimization approach, sequence quadratic programming (SQP). Both the two algorithms can achieve our aim commendably, and the optimal solution given by GA is more satisfied. The research provides us a good foundation to develop the stochastic and implicit parallel properties of GA to obtain Pareto optimal solutions.

547 Hybrid Optimization Method Using Grouping of Pareto-Optimal Solutions

2010 ◽  
Vol 2010 (0) ◽  
pp. _547-1_-_547-6_
Author(s):  
Yuichiro SAKAMOTO ◽  
Yasuhiro BONKOBARA ◽  
Takahiro KONDOU ◽  
Kenzi ABE ◽  
Hiroyuki KUROKI
Keyword(s):  
Optimization Method ◽  
Hybrid Optimization ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Hybrid Optimization Method

Multiobjective Hybrid Optimization–Antioptimization for Force Design of Tensegrity Structures

2012 ◽  
Vol 79 (2) ◽  
Author(s):  
Makoto Ohsaki ◽  
Jingyao Zhang ◽  
Isaac Elishakoff
Keyword(s):  
Optimization Problem ◽  
Hybrid Approach ◽  
Programming Approach ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Objective Functions ◽  
Tensegrity Structures ◽  
Tangent Stiffness Matrix ◽  
Lowest Eigenvalue

Properties of Pareto optimal solutions considering bounded uncertainty are first investigated using an illustrative example of a simple truss. It is shown that the nominal values of the Pareto optimal solutions considering uncertainty are slightly different from those without considering uncertainty. Next a hybrid approach of multiobjective optimization and antioptimization is presented for force design of tensegrity structures. We maximize the lowest eigenvalue of the tangent stiffness matrix and minimize the deviation of forces from the specified target distribution. These objective functions are defined as the worst values due to the possible errors in the fabrication and construction processes. The Pareto optimal solutions are found by solving the two-level optimization–antioptimization problems using a nonlinear programming approach for the upper optimization problem and enumeration of the vertices of the uncertain region for the lower antioptimization problem.

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