scholarly journals DESIGN OF OPTIMAL INVERSE KINEMATIC SOLUTION FOR HUMANOID ROBOTIC ARMS

2021 ◽  
Vol 1 (1) ◽  
pp. 11-24
Author(s):  
Saif F. Abulhail ◽  
Mohammed Z. Al-Faiz
Keyword(s):  
Mean Squared Error ◽  
Optimization Algorithms ◽  
Optimal Solution ◽  
Absolute Error ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Inverse Kinematic ◽  
Point Of View ◽  
Computational Time ◽  
Robotic Arms

One of the main problems in robotics is the Inverse Kinematics (IK) problem. In this paper, three optimization algorithms are proposed to solve the IK of Humanoid Robotic Arms (HRAs). A Particle Swarm Optimization (PSO), Social Spider Optimization (SSO), and Black Hole Optimization (BHO) algorithms are proposed in order to optimize the parameters of the proposed IK. Also, in this paper, each optimization method is applied on both right and left arms to find the desired positions and required angles with a minimum error. Denavit-Hartenberg (D-H) method is used to design and simulate the mathematical model of HRAs for both arms in which each arm has five Degree Of Freedom (DOF). The HRAs model is tested for performance by several positions to be reached by both arms in the same time to find which optimization algorithm is better. Optimal solution obtained by SSO, PSO and BHO algorithms are evaluated and listed in comparison table between them. These optimization algorithms are assessed by calculating the Computational Time (CT) and Root Mean Squared Error (RMSE) for the absolute error vector of the positions. Calculation and simulation results showed that BHO algorithm is better than the other optimization algorithms from point of view of CT and RMSE. The worst RMSE is 0.0864 was calculated using PSO algorithm. But longer CT is 7.6521 second, which was calculated using SSO. While the best RMSE and shorter CT.are  and 3.0156 second respectively were calculated by BHO algorithm. Moreover, in this paper, the Graphical User Interface (GUI) is designed and built for motional characteristics of the HRAs model in the Forward Kinematics (FK) and IK. The optimization algorithms are designed using MATLAB package facilities to simulate the HRAs model and the solution of IK problem.

scholarly journals Particle Swarm Optimization

2017 ◽  
pp. 614-635 ◽  
Author(s):  
Alaa Tharwat ◽  
Tarek Gaber ◽  
Aboul Ella Hassanien ◽  
Basem E. Elnaghi
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Parameter Tuning ◽  
Pso Algorithm ◽  
Search Space ◽  
Learning Problems ◽  
Swarm Optimization ◽  
Numerical Example

Optimization algorithms are necessary to solve many problems such as parameter tuning. Particle Swarm Optimization (PSO) is one of these optimization algorithms. The aim of PSO is to search for the optimal solution in the search space. This paper highlights the basic background needed to understand and implement the PSO algorithm. This paper starts with basic definitions of the PSO algorithm and how the particles are moved in the search space to find the optimal or near optimal solution. Moreover, a numerical example is illustrated to show how the particles are moved in a convex optimization problem. Another numerical example is illustrated to show how the PSO trapped in a local minima problem. Two experiments are conducted to show how the PSO searches for the optimal parameters in one-dimensional and two-dimensional spaces to solve machine learning problems.

scholarly journals SuperMeshing: A New Deep Learning Architecture for Increasing the Mesh Density of Physical Fields in Metal Forming Numerical Simulation

2021 ◽  
pp. 1-11
Author(s):  
Qingfeng Xu ◽  
Zhenguo Nie ◽  
Handing Xu ◽  
Haosu Zhou ◽  
Hamid Reza Attar ◽  
...  
Keyword(s):  
Stress Field ◽  
Mean Squared Error ◽  
Interpolation Method ◽  
Computing Time ◽  
Absolute Error ◽  
Linear Interpolation ◽  
Field Analysis ◽  
Computational Time ◽  
Mesh Density ◽  
Stress Field Analysis

Abstract In stress field analysis, the finite element method is a crucial approach, in which the mesh-density has a significant impact on the results. High mesh density usually contributes authentic to simulation results but costs more computing resources. To eliminate this drawback, we propose a data-driven mesh-density boost model named SuperMeshingNet that uses low mesh-density as inputs, to acquire high-density stress field instantaneously, shortening computing time and cost automatically. Moreover, the Res-UNet architecture and attention mechanism are utilized, enhancing the performance of SuperMeshingNet. Compared with the baseline that applied the linear interpolation method, SuperMeshingNet achieves a prominent reduction in the mean squared error (MSE) and mean absolute error (MAE) on the test data. The well-trained model can successfully show more excellent performance than the baseline models on the multiple scaled mesh-density, including 2X, 4X, and 8X. Enhanced by SuperMeshingNet with broaden scaling of mesh density and high precision output, FEA can be accelerated with seldom computational time and cost.

EFFECTIVE DYNAMIC PROGRAMMING ALGORITHMS

2020 ◽  
pp. 3-11
Author(s):  
D. A. Karpov ◽  
V. I. Struchenkov
Keyword(s):  
Dynamic Programming ◽  
Optimal Solution ◽  
Optimization Method ◽  
Point Of View ◽  
Theoretical Point ◽  
Regular Grid ◽  
Multi Stage ◽  
Effective Dynamic ◽  
Programming Algorithms ◽  
Two Parameter

This article is devoted to the analysis of the possibilities of increasing the speed of dynamic programming algorithms in solving applied problems of large dimension. Dynamic programming is considered rather than as an optimization method, but as a methodology that allows developing, from a single theoretical point of view, algorithms for solving problems that can be formalized in the form of multi-stage (multi-step) processes in which similar tasks are solved at all steps. It is shown that traditional dynamic programming algorithms based on preliminary setting of a regular grid of states are ineffective, especially if the parameters defining the states are not integer. The problems are considered, in the solution of which it is advisable to build a set of states in the process of counting, moving from one stage to another. Additional conditions are formulated that must be satisfied by the problem so that deliberately hopeless states do not fall into sets of states at each step. This ensures the rejection of not only the paths leading to each of the states, as in traditional dynamic programming algorithms, but also the unpromising states themselves, which greatly increases the efficiency of dynamic programming. Examples of applied problems are given, for the solution of which traditional dynamic programming algorithms were previously proposed, but which can be more efficiently solved by the proposed algorithm with state rejection. As applied to two-parameter problems, the concrete examples demonstrate the effectiveness of the algorithm with rejecting states in comparison with traditional algorithms, especially with increasing the dimension of the problem. An applied problem is considered, in the solution of which dynamic programming is used to construct recurrent formulas for calculating the optimal solution without enumerating options at all.

scholarly journals Estimating one-diode-PV model using autonomous groups particle swarm optimization

2021 ◽  
Vol 10 (1) ◽  
pp. 166
Author(s):  
Mohammad AlShabi ◽  
Chaouki Ghenai ◽  
Maamar Bettayeb ◽  
Fahad Faraz Ahmad
Keyword(s):  
Particle Swarm Optimization ◽  
Convergence Rate ◽  
Mean Squared Error ◽  
Particle Swarm ◽  
Absolute Error ◽  
Pso Algorithm ◽  
Maximum Absolute Error ◽  
Swarm Optimization ◽  
The Social ◽  
The One

In this paper, the one-diode model of a photovoltaic PV solar cell (PVSC) is estimated for an experimental characteristic curves data by using a recently proposed version of the Particle Swarm Optimization (PSO) algorithm, which is known as the Autonomous Groups Particles Swarm Optimization (PSOAG). This meta-heuristic algorithm is used to identify the model of the PVSC. The PSOAG divides the particles into groups and then, uses different functions to tune the social and cognitive parameters of these groups. This is done to show the individuals’ diversity inside the swarm. Although, these individuals do their duties as part of the society, they are not similar in terms of intelligence and ability. By using these groups, the performance of the PSO is improved in terms of convergence rate and escaping the local minima/maxima. Six versions of PSOAG algorithms were developed in this work. Therefore, nine versions of PSOAG, including these six algorithms and three newly developed PSOAG reported previously, will be used in this research to cover more social’s behaviors. The results are compared to the original PSO and other versions of PSO like conventional and Asymmetric Time-varying Accelerated Coefficient PSOs, and the improved PSO. The result shows that the proposed methods improve the performance by up to 14% in terms of root mean squared error and maximum absolute error, and by up to 20% in term of convergence rate, when these were compared to the best results obtained from the other algorithms.

Simultaneous Optimization of Mistuned Bladed Disks for Forced and Self-Excited Vibration Considering Amount of Unbalance

2021 ◽  
Author(s):  
Yasutomo Kaneko ◽  
Toshio Watanabe ◽  
Tatsuya Furukawa
Keyword(s):  
Optimal Solution ◽  
Optimization Method ◽  
Forced Response ◽  
Simultaneous Optimization ◽  
Computational Time ◽  
Bladed Disks ◽  
Bladed Disk ◽  
Excited Vibration ◽  
Blade Flutter ◽  
The Stability

Abstract Actual bladed disks with small variations are called mistuned systems. Many researchers suggest that mistuning, although negatively affecting the forced response, has a beneficial (stabilizing) effect on blade flutter (self-excited vibration). Therefore, in blade design, a bladed disk must be optimized for forced vibration and blade flutter. We proposed a simultaneous optimization method of bladed disks for forced and self-excited vibration, considering the amount of unbalance that causes rotor vibration. This method uses alternate mistuning to suppress the blade flutter. We measured the natural frequency and weight of all the blades of a disk, as in the traditional development process. Then, we assembled a mistuned system retaining the alternate mistuning, and generated analysis models based on the measured natural frequencies and weights of the blades. Finally, we analyzed the resonant stress and the amount of unbalance in the mistuned system repeatedly, sorting the blades and retaining the alternate mistuning of the disk. The simultaneous optimal solution was explored by MCS or DDE (Genetic algorithm). To reduce the computational time, we used the reduced order model FMM to calculate the resonant stress and the stability of the mistuned bladed disks. Further, we verified the validity of the proposed method by applying it to a mistuned bladed disk of a steam turbine.

scholarly journals Tuning of different controlling techniques for magnetic suspending system using an improved bat algorithm

2020 ◽  
Vol 10 (3) ◽  
pp. 2402
Author(s):  
Nizar Hadi Abbas
Keyword(s):  
Optimization Problems ◽  
Optimal Solution ◽  
Bat Algorithm ◽  
Frequency Factor ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Benchmark Problems ◽  
Local Optimum ◽  
Weak Point ◽  
Inertia Weight

In this paper, design of proportional- derivative (PD) controller, pseudo-derivative-feedback (PDF) controller and PDF with feedforward (PDFF) controller for magnetic suspending system have been presented. Tuning of the above controllers is achieved based on Bat algorithm (BA). BA is a recent bio-inspired optimization method for solving global optimization problems, which mimic the behavior of micro-bats. The weak point of the standard BA is the exploration ability due to directional echolocation and the difficulty in escaping from local optimum. The new improved BA enhances the convergence rate while obtaining optimal solution by introducing three adaptations namely modified frequency factor, adding inertia weight and modified local search. The feasibility of the proposed algorithm is examined by applied to several benchmark problems that are adopted from literature. The results of IBA are compared with the results collected from standard BA and the well-known particle swarm optimization (PSO) algorithm. The simulation results show that the IBA has a higher accuracy and searching speed than the approaches considered. Finally, the tuning of the three controlling schemes using the proposed algorithm, standard BA and PSO algorithms reveals that IBA has a higher performance compared with the other optimization algorithms

scholarly journals A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran

2021 ◽  
Vol 13 (14) ◽  
pp. 7612
Author(s):  
Mahdis sadat Jalaee ◽  
Alireza Shakibaei ◽  
Amin GhasemiNejad ◽  
Sayyed Abdolmajid Jalaee ◽  
Reza Derakhshani
Keyword(s):  
Hybrid Method ◽  
Computational Intelligence ◽  
Energy Demand ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Coal Consumption ◽  
Squared Error ◽  
Artificial Neural ◽  
Socio Economic Variables ◽  
Intelligence Approach

Coal as a fossil and non-renewable fuel is one of the most valuable energy minerals in the world with the largest volume reserves. Artificial neural networks (ANN), despite being one of the highest breakthroughs in the field of computational intelligence, has some significant disadvantages, such as slow training, susceptibility to falling into a local optimal points, sensitivity of initial weights, and bias. To overcome these shortcomings, this study presents an improved ANN structure, that is optimized by a proposed hybrid method. The aim of this study is to propose a novel hybrid method for predicting coal consumption in Iran based on socio-economic variables using the bat and grey wolf optimization algorithm with an artificial neural network (BGWAN). For this purpose, data from 1981 to 2019 have been used for modelling and testing the method. The available data are partly used to find the optimal or near-optimal values of the weighting parameters (1980–2014) and partly to test the model (2015–2019). The performance of the BGWAN is evaluated by mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), standard deviation error (STD), and correlation coefficient (R^2) between the output of the method and the actual dataset. The result of this study showed that BGWAN performance was excellent and proved its efficiency as a useful and reliable tool for monitoring coal consumption or energy demand in Iran.

scholarly journals Estimating Physical Activity Energy Expenditure Using an Ensemble Model-Based Patch-Type Sensor Module

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 861
Author(s):  
Kyeung Ho Kang ◽  
Mingu Kang ◽  
Siho Shin ◽  
Jaehyo Jung ◽  
Meina Li
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Ensemble Model ◽  
Direct Calorimetry ◽  
Patch Type ◽  
Mortality And Morbidity ◽  
Squared Error ◽  
Sensor Module

Chronic diseases, such as coronary artery disease and diabetes, are caused by inadequate physical activity and are the leading cause of increasing mortality and morbidity rates. Direct calorimetry by calorie production and indirect calorimetry by energy expenditure (EE) has been regarded as the best method for estimating the physical activity and EE. However, this method is inconvenient, owing to the use of an oxygen respiration measurement mask. In this study, we propose a model that estimates physical activity EE using an ensemble model that combines artificial neural networks and genetic algorithms using the data acquired from patch-type sensors. The proposed ensemble model achieved an accuracy of more than 92% (Root Mean Squared Error (RMSE) = 0.1893, R2 = 0.91, Mean Squared Error (MSE) = 0.014213, Mean Absolute Error (MAE) = 0.14020) by testing various structures through repeated experiments.

scholarly journals Optimal solution of the fractional order breast cancer competition model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
H. Hassani ◽  
J. A. Tenreiro Machado ◽  
Z. Avazzadeh ◽  
E. Safari ◽  
S. Mehrabi
Keyword(s):  
Breast Cancer ◽  
Fractional Order ◽  
Legendre Polynomials ◽  
Numerical Experiments ◽  
Optimal Solution ◽  
Optimization Method ◽  
Competition Model ◽  
Basis Functions ◽  
Cell Populations ◽  
Operational Matrices

AbstractIn this article, a fractional order breast cancer competition model (F-BCCM) under the Caputo fractional derivative is analyzed. A new set of basis functions, namely the generalized shifted Legendre polynomials, is proposed to deal with the solutions of F-BCCM. The F-BCCM describes the dynamics involving a variety of cancer factors, such as the stem, tumor and healthy cells, as well as the effects of excess estrogen and the body’s natural immune response on the cell populations. After combining the operational matrices with the Lagrange multipliers technique we obtain an optimization method for solving the F-BCCM whose convergence is investigated. Several examples show that a few number of basis functions lead to the satisfactory results. In fact, numerical experiments not only confirm the accuracy but also the practicability and computational efficiency of the devised technique.

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