Hybrid Lagrange interpolation differential evolution algorithm for path synthesis

Zhang, Kai; Huang, Qiujun; Zhang, Yimin; Jun, Song; Shi, Jia

doi:10.1016/j.mechmachtheory.2019.01.012

Cited by 24 publications

(13 citation statements)

References 32 publications

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“…Application of the Synthesis Problem Metaheuristic Algorithms CHT [19] Four-bar mechanism Genetic Algorithm - [20] Four-bar mechanism Genetic Algorithm -Stephenson's six-bar mechanism Watt's six-bar mechanism [5] Four-bar mechanism Genetic Algorithm Penalty Function [21] Hand robot mechanism Pareto Optimum Evolutionary Feasibility rules Multiobjective Algorithm (POEMA) [22] Four-bar mechanism Differential Evolution Penality Function [23] Six-bar mechanism Differential Evolution Penality Function [24] Four-bar mechanism Differential Evolution Penality Function [25] Four-bar mechanism Genetic algorithm-fuzzy logic Penality Function [26] Four-bar and six-bar mechanisms MUMSA Penality Function [27] Four-bar mechanism Genetic Algorithm, Penality Function Differential Evolution, Particle Swarm Optimization [28] Four-bar mechanism Ant-gradient Penality Function [29] Four-bar mechanism GA-DE Penality Function [30] Six-bar mechanism Cuckoo Search Penality Function [31] Four-bar mechanism NSGA-II Feasibility rules [32] Four-bar mechanism Imperialist competitive algorithm, Penality Function Genetic Algorithm, Differential Evolution, Particle Swarm Optimization [33] Four-bar mechanism Modified Krill Herd Penality Function [34] Four-bar mechanism TLBO Penality Function Genetic Algorithm, Particle Swarm Optimization [35] Four-bar mechanism Hybrid Lagrange Interpolation DE Penality Function (HLIDE) [36] Four-bar and six-bar mechanisms Hybridization Differential Evolution Penality Function with Generalized Reduced Gradient Mechanisms for rehabilitation [18] Spherical parallel manipulator NSGA-II, MOPSO, MOEA/D Feasibility rules in prosthetic wrist [14] Six-bar mechanism in MUMSA Feasibility rules finger rehabilitation [15] Cam-linkage mechanism Genetic Algorithm Penalty Function. in gait rehabilitation [16] Four-bar mechanism Differential Evolution Feasibility rules in gait rehabilitation [17] Four-bar mechanism in Particle Swarm Optimization and TLBO gait rehabilitation and orthotic devices [3] Eight-bar mechanism in Differential Evolution Feasibility rules.…”

Section: Studymentioning

confidence: 99%

Empirical Study of Constraint-Handling Techniques in the Optimal Synthesis of Mechanisms for Rehabilitation

2021

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Currently, rehabilitation systems with closed kinematic chain mechanisms are low-cost alternatives for treatment and health care. In designing these systems, the dimensional synthesis is commonly stated as a constrained optimization problem to achieve repetitive rehabilitation movements, and metaheuristic algorithms for constrained problems are promising methods for searching solutions in the complex search space. The Constraint Handling Techniques (CHTs) in metaheuristic algorithms have different capacities to explore and exploit the search space. However, the study of the relationship in the CHT performance of the mechanism dimensional synthesis for rehabilitation systems has not been addressed, resulting in an important gap in the literature of such problems. In this paper, we present a comparative empirical study to investigate the influence of four CHTs (penalty function, feasibility rules, stochastic-ranking, and ϵ-constraint) on the performance of ten representative algorithms that have been reported in the literature for solving mechanism synthesis for rehabilitation (four-bar linkage, eight-bar linkage, and cam-linkage mechanisms). The study involves analysis of the overall performance, six performance metrics, and evaluation of the obtained mechanism. This identified that feasibility rules usually led to efficient optimization for most analyzed algorithms and presented more consistency of the obtained results in these kinds of problems.

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Section: Studymentioning

confidence: 99%

Empirical Study of Constraint-Handling Techniques in the Optimal Synthesis of Mechanisms for Rehabilitation

2021

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“…The Lagrange method (26) (w = a 1 y 1 x 1 + a 2 y 2 x 2 + ••• + a n y n x n ) is used to optimize the classification hyperplane; then, the issue of the binary classification of the SVM can be expressed as…”

Section: ( )mentioning

confidence: 99%

Optimizing Back Propagation Neural Network Parameters to Judge Fault Types of Ball Bearings

Jian¹,

Chang‐Jian²,

Guo³

et al. 2020

Sensors and Materials

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When current technology keeps advancing, global machine tool manufacturers are gradually moving toward smart production lines. The ball bearing is an important fixed part of a rotating shaft; its key function is to bear the load acting on the shaft and maintain the center position of the shaft. If the bearing is damaged, there will be abnormal vibration, runout, and abnormal noise. Hence, the fault detection and recognition of the ball bearing are particularly important. The fault signal data of the ball bearing used in this study are obtained from the Case Western Reserve University (CWRU), and we establish a ball bearing status recognition model according to different signal-captured positions. First, the infinite impulse response (IIR) filter and approximate entropy (ApEn) are used to extract the features of the signals. Afterwards, the data extracted from the features are used for model establishment and training through a back propagation neural network (BPNN) and a support vector machine (SVM). In general, the SVM classification is better than the BPNN, but through a series of experimental methods, we confirmed that the optimal BPNN parameters of this sample, including training function, data training ratio, and the number of neurons, make the recognition rate of the BPNN higher than that of the general SVM, and the accuracy rate reaches 95%.

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“…The graphical and analytical methods for the mechanism synthesis have limitations in the accuracy and complexity of the path to be followed. In the case of the analytical method, it is limited to five precision points [9]. When there are a large number of precision points, the path generation problem becomes over-constrained, and to find mechanisms that produce the desired path is a difficult task [10].…”

Section: Introductionmentioning

confidence: 99%

“…Once the problem is stated, it is solved with some state-of-the-art optimization technique. The second group is related to the research that concentrate on achieving modifications to optimization techniques in order to find better design solutions in the mechanism synthesis problem [9], [20]- [28]. The main motivation of modifying optimization techniques is that the complexity of the optimization problems produces multiple optimal local solutions [20].…”

Section: Introductionmentioning

confidence: 99%

“…In the second group, there are works such as the one presented in [20] where it uses a combined sequential scheme of the Ant Colony Optimization (ACO) algorithm with the gradient search algorithm to find solutions in the dimensional synthesis problem of the four-bar mechanism for hybrid tasks including path, motion and function generation. Other works related to modifications in the optimization technique for the four-bar mechanism synthesis are presented in [9]. In that work, an exploitation strategy is incorporated into the DE algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Optimum Synthesis of Four-Bar Mechanism by Using Relative Angle Method: A Comparative Performance Study

Valencia-Segura

Villarreal-Cervantes²,

Corona-Ramirez

et al. 2021

IEEE Access

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In this paper, the dimensional synthesis of the four-bar mechanism for path generation is formulated using the relative angle motion analysis and the link geometry parameterization with Cartesian coordinates. The Optimum Dimensional Synthesis using Relative Angles and the Cartesian space link Parameterization (ODSRA+CP) is stated as an optimization problem, and the solution is given by the differential evolution variant DE/best/1/bin. This study investigates the behavior and performance of such formulation and performs a comparative empirical study with the well-known synthesis method based on vector-loop equation motion analysis where different modifications in the metaheuristic algorithms are established in the literature to improve the obtained solution. Five study cases of dimensional synthesis for path generation with and without prescribed timing are solved and analyzed. The empirical results show that the way of stating the optimization problem in the ODSRA+CP significantly improves the search process for finding promising solutions in the optimizer without requiring algorithm modifications. Therefore, it is confirmed that the optimizer search process in the optimal synthesis of mechanisms is not the only way of improving the obtained solutions, but also the optimization problem formulation has a significant influence on the search for better solutions.INDEX TERMS Mechanism synthesis, four-bar mechanism, optimization, differential evolution.

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Hybrid Lagrange interpolation differential evolution algorithm for path synthesis

Cited by 24 publications

References 32 publications

Empirical Study of Constraint-Handling Techniques in the Optimal Synthesis of Mechanisms for Rehabilitation

Empirical Study of Constraint-Handling Techniques in the Optimal Synthesis of Mechanisms for Rehabilitation

Optimizing Back Propagation Neural Network Parameters to Judge Fault Types of Ball Bearings

Optimum Synthesis of Four-Bar Mechanism by Using Relative Angle Method: A Comparative Performance Study

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