scholarly journals Optimization of TIG Welding Parameters Using a Hybrid Nelder Mead-Evolutionary Algorithms Method

2020 ◽  
Vol 4 (1) ◽  
pp. 10 ◽  
Author(s):  
Rohit Kshirsagar ◽  
Steve Jones ◽  
Jonathan Lawrence ◽  
Jim Tabor
Keyword(s):  
Evolutionary Algorithms ◽  
Global Minimum ◽  
Error Function ◽  
Approximate Solutions ◽  
Optimization Method ◽  
Computational Effort ◽  
Welding Parameters ◽  
Additional Function ◽  
Effort Function ◽  
Better Than

A number of evolutionary algorithms such as genetic algorithms, simulated annealing, particle swarm optimization, etc., have been used by researchers in order to optimize different manufacturing processes. In many cases these algorithms are either incapable of reaching global minimum or the time and computational effort (function evaluations) required makes the application of these algorithms impractical. However, if the Nelder Mead optimization method is applied to approximate solutions cheaply obtained from these algorithms, the solution can be further refined to obtain near global minimum of a given error function within only a few additional function evaluations. The initial solutions (vertices) required for the application of Nelder-Mead optimization can be obtained through multiple evolutionary algorithms. The results obtained using this hybrid method are better than that obtained from individual algorithms and also show a significant reduction in the computation effort.

scholarly journals An Adaptive Optimization Method Based on Learning Rate Schedule for Neural Networks

2021 ◽  
Vol 11 (2) ◽  
pp. 850
Author(s):  
Dokkyun Yi ◽  
Sangmin Ji ◽  
Jieun Park
Keyword(s):  
Artificial Intelligence ◽  
Cost Function ◽  
Numerical Experiments ◽  
Global Minimum ◽  
Optimization Method ◽  
Learning Method ◽  
Adaptive Optimization ◽  
The Cost ◽  
Proof Of Convergence ◽  
Learning Data

Artificial intelligence (AI) is achieved by optimizing the cost function constructed from learning data. Changing the parameters in the cost function is an AI learning process (or AI learning for convenience). If AI learning is well performed, then the value of the cost function is the global minimum. In order to obtain the well-learned AI learning, the parameter should be no change in the value of the cost function at the global minimum. One useful optimization method is the momentum method; however, the momentum method has difficulty stopping the parameter when the value of the cost function satisfies the global minimum (non-stop problem). The proposed method is based on the momentum method. In order to solve the non-stop problem of the momentum method, we use the value of the cost function to our method. Therefore, as the learning method processes, the mechanism in our method reduces the amount of change in the parameter by the effect of the value of the cost function. We verified the method through proof of convergence and numerical experiments with existing methods to ensure that the learning works well.

scholarly journals Surrogate-Based Optimization of Horizontal Axis Hydrokinetic Turbine Rotor Blades

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 4045
Author(s):  
David Menéndez Arán ◽  
Ángel Menéndez
Keyword(s):  
Turbine Blade ◽  
Design Method ◽  
Optimization Method ◽  
Turbine Rotor ◽  
Computational Effort ◽  
Rotor Blades ◽  
Linear Functions ◽  
Cavitation Inception ◽  
Hydrokinetic Turbine ◽  
Blade Geometry

A design method was developed for automated, systematic design of hydrokinetic turbine rotor blades. The method coupled a Computational Fluid Dynamics (CFD) solver to estimate the power output of a given turbine with a surrogate-based constrained optimization method. This allowed the characterization of the design space while minimizing the number of analyzed blade geometries and the associated computational effort. An initial blade geometry developed using a lifting line optimization method was selected as the base geometry to generate a turbine blade family by multiplying a series of geometric parameters with corresponding linear functions. A performance database was constructed for the turbine blade family with the CFD solver and used to build the surrogate function. The linear functions were then incorporated into a constrained nonlinear optimization algorithm to solve for the blade geometry with the highest efficiency. A constraint on the minimum pressure on the blade could be set to prevent cavitation inception.

A Mathematical Principle and Application on Design of Planar Six-Bar Mechanism with unto Three-Ordered Intermission at Limit Position(s)

2010 ◽  
Vol 37-38 ◽  
pp. 9-13
Author(s):  
Hong Xin Wang ◽  
Ning Dai
Keyword(s):  
Design Method ◽  
Optimization Method ◽  
Basic Function ◽  
Mathematical Principle ◽  
Transmission Characteristics ◽  
Basic Functions ◽  
Combined Mechanism ◽  
Limit Position ◽  
Derivatives Of ◽  
Better Than

A non-iterative design method about high order intermittent mechanisms is presented. The mathematical principle is that a compound function produced by two basic functions, and then one to three order derivatives of the compound function are all zeroes when one order derivative of each basic function is zero at the same moment. The design method is that a combined mechanism is constructed by six bars; the displacement functions of the front four-bar and back four-bar mechanisms are separately built, let one order derivatives of two displacement functions separately be zero at the same moment, and then get geometrical relationships and solution on the intermittent mechanism. A design example shows that this method is simpler and transmission characteristics are better than optimization method.

Generative Optimization for Automatic Creation of 3D CAD Platforms

2021 ◽  
Author(s):  
William F. Quintero-Restrepo ◽  
Brian K. Smith ◽  
Junfeng Ma
Keyword(s):  
Evolutionary Algorithms ◽  
Six Sigma ◽  
Design Space ◽  
Optimization Method ◽  
Efficiency Improvement ◽  
3D Cad ◽  
Systems Model ◽  
Electric Machinery ◽  
Definition Of ◽  
Six Sigma Approach

Abstract The efficient creation of 3D CAD platforms can be achieved by the optimization of their design process. The research presented in this article showcases a method for allowing such efficiency improvement. The method is based on the DMADV six sigma approach. During the Define step, the definition of the scope and design space is established. In the Measure step, the initial evaluation of the platforms to be improved is done with the help of a Metrics framework for 3D CAD platforms. The Analyze Step includes the identification and optimization of the systems’ model of the process based on the architecture and the multiple objectives required for the improvement. The optimization method used that is based on evolutionary algorithms allows for the identification of the best improvement alternatives for the next step. During Design step of the method, the improvement alternatives are planned and executed. In the final Verification step, the evaluation of the improved process is tested against the previous status with the help of the Metrics Framework for 3D CAD platforms. The method is explained with an example case of a 3D CAD platform for creating metallic boxes for electric machinery.

scholarly journals Evolutionary optimization in classification of early-MCI patients from healthy controls using graph measures of resting-state fMRI

2021 ◽  
Author(s):  
Jafar Zamani ◽  
Ali Sadr ◽  
Amir-Homayoun Javadi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Evolutionary Algorithms ◽  
Resting State ◽  
Optimization Methods ◽  
Resting State Fmri ◽  
Optimization Method ◽  
Effective Parameters

AbstractsIdentifying individuals with early mild cognitive impairment (EMCI) can be an effective strategy for early diagnosis and delay the progression of Alzheimer’s disease (AD). Many approaches have been devised to discriminate those with EMCI from healthy control (HC) individuals. Selection of the most effective parameters has been one of the challenging aspects of these approaches. In this study we suggest an optimization method based on five evolutionary algorithms that can be used in optimization of neuroimaging data with a large number of parameters. Resting-state functional magnetic resonance imaging (rs-fMRI) measures, which measure functional connectivity, have been shown to be useful in prediction of cognitive decline. Analysis of functional connectivity data using graph measures is a common practice that results in a great number of parameters. Using graph measures we calculated 1155 parameters from the functional connectivity data of HC (n=36) and EMCI (n=34) extracted from the publicly available database of the Alzheimer’s disease neuroimaging initiative database (ADNI). These parameters were fed into the evolutionary algorithms to select a subset of parameters for classification of the data into two categories of EMCI and HC using a two-layer artificial neural network. All algorithms achieved classification accuracy of 94.55%, which is extremely high considering single-modality input and low number of data participants. These results highlight potential application of rs-fMRI and efficiency of such optimization methods in classification of images into HC and EMCI. This is of particular importance considering that MRI images of EMCI individuals cannot be easily identified by experts.

scholarly journals Random adjustment - based Chaotic Metaheuristic algorithms for image contrast enhancement

2017 ◽  
Vol 10 (2) ◽  
pp. 67
Author(s):  
Vina Ayumi ◽  
L.M. Rasdi Rere ◽  
Mohamad Ivan Fanany ◽  
Aniati Murni Arymurthy
Keyword(s):  
Contrast Enhancement ◽  
Harmony Search ◽  
Search Space ◽  
Optimization Method ◽  
Large Solution ◽  
Swarm Optimization ◽  
Image Contrast Enhancement ◽  
Simulation Results ◽  
Better Than

Metaheuristic algorithm is a powerful optimization method, in which it can solve problemsby exploring the ordinarily large solution search space of these instances, that are believed tobe hard in general. However, the performances of these algorithms signicantly depend onthe setting of their parameter, while is not easy to set them accurately as well as completelyrelying on the problem's characteristic. To ne-tune the parameters automatically, manymethods have been proposed to address this challenge, including fuzzy logic, chaos, randomadjustment and others. All of these methods for many years have been developed indepen-dently for automatic setting of metaheuristic parameters, and integration of two or more ofthese methods has not yet much conducted. Thus, a method that provides advantage fromcombining chaos and random adjustment is proposed. Some popular metaheuristic algo-rithms are used to test the performance of the proposed method, i.e. simulated annealing,particle swarm optimization, dierential evolution, and harmony search. As a case study ofthis research is contrast enhancement for images of Cameraman, Lena, Boat and Rice. Ingeneral, the simulation results show that the proposed methods are better than the originalmetaheuristic, chaotic metaheuristic, and metaheuristic by random adjustment.

Optimization of Fluidized Bed Reactor of Oxidative Coupling of Methane

2012 ◽  
Vol 10 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Mohammad-Hosein Eghbal-Ahmadi ◽  
Masoud Zaerpour ◽  
Mahdi Daneshpayeh ◽  
Navid Mostoufi
Keyword(s):  
Fluidized Bed ◽  
Oxidative Coupling ◽  
Operating Temperature ◽  
Fluidized Bed Reactor ◽  
Optimization Method ◽  
Oxidative Coupling Of Methane ◽  
Process Variables ◽  
Algorithm Optimization ◽  
Reactor Performance ◽  
Better Than

Optimization of process variables in the oxidative coupling of methane (OCM) over Mn/Na2WO4/SiO2 catalyst in a fluidized bed reactor was carried out. Effects of operating temperature, distribution pattern of oxygen injected to the reactor and the number of injections on the reactor performance on C2 (ethane + ethylene) yield were investigated. Process variables for one, two and three secondary oxygen injections were investigated to obtain the maximum C2 yield by genetic algorithm optimization method. The maximum C2 selectivity and yield of 47.1% and 22.87%, respectively, were achieved for three secondary oxygen injections at operating temperature of 746.05°C. The C2 yield achieved in this study is approximately 4% better than previous works reported in literature while the optimum temperature is lower.

Imprecise Solutions of Ordinary Differential Equations for Boundary Value Problems Using Metaheuristic Algorithms

2016 ◽  
pp. 401-421
Author(s):  
Ali Sadollah ◽  
Joong Hoon Kim
Keyword(s):  
Differential Equations ◽  
Exact Solutions ◽  
Ordinary Differential Equations ◽  
Water Cycle ◽  
Error Function ◽  
Boundary Value ◽  
Approximate Solutions ◽  
Metaheuristic Algorithms ◽  
General Strategy ◽  
Optimization Task

In this chapter, a general strategy is recommended to solve variety of linear and nonlinear ordinary differential equations (ODEs) with boundary value conditions. With the aid of certain fundamental concepts of mathematics, Fourier series expansion, and metaheuristic algorithms, ODEs can be represented as an optimization problem. The purpose is to reduce the weighted residual error (error function) of the ODEs. Boundary values of ODEs are considered as constraints for the optimization model. Inverted generational distance metric is utilized for evaluation and assessment of approximate solutions versus exact solutions. Four ODEs having different orders and features are approximately solved and compared with their exact solutions. The optimization task is carried out using different optimizers including the particle swarm optimization and the water cycle algorithm. The optimization results obtained show that the proposed method equipped with metaheuristic algorithms can be successfully applied for approximate solving of different types of ODEs.

scholarly journals Motion-Based Design of Passive Damping Devices to Mitigate Wind-Induced Vibrations in Stay Cables

Vibration ◽  
2018 ◽  
Vol 1 (2) ◽  
pp. 269-289 ◽  
Author(s):  
Javier Naranjo-Pérez ◽  
Javier Jiménez-Manfredi ◽  
Javier Jiménez-Alonso ◽  
Andrés Sáez
Keyword(s):  
Objective Function ◽  
Design Method ◽  
Optimization Method ◽  
Design Criterion ◽  
Passive Damping ◽  
Characteristic Parameters ◽  
Additional Function ◽  
Wind Action ◽  
Multi Objective ◽  
Stay Cables

Wind action can induce large amplitude vibrations in the stay cables of bridges. To reduce the vibration level of these structural elements, different types of passive damping devices are usually installed. In this paper, a motion-based design method is proposed and implemented in order to achieve the optimum design of different passive damping devices for stay cables under wind action. According to this method, the design problem is transformed into an optimization problem. Thus, its main aim is to minimize the different terms of a multi-objective function, considering as design variables the characteristic parameters of each considered passive damping device. The multi-objective function is defined in terms of the scaled characteristic parameters, one single-function for each parameter, and an additional function that checks the compliance of the considered design criterion. Genetic algorithms are considered as a global optimization method. Three passive damping devices have been studied herein: viscous, elastomeric and friction dampers. As a benchmark structure, the Alamillo bridge (Seville, Spain), is considered in order to validate the performance of the proposed method. Finally, the parameters of the damping devices designed according to this proposal are successfully compared with the results provided by a conventional design method.

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