scholarly journals Optimization Algorithm for Reduction the Size of Dixon Resultant Matrix: A Case Study on Mechanical Application

2019 ◽  
Vol 58 (2) ◽  
pp. 567-583 ◽  
Author(s):  
Shang Zhang ◽  
Seyedmehdi Karimi ◽  
Shahaboddin Shamshirband ◽  
Amir Mosavi
Keyword(s):  
Optimization Algorithm ◽  
Resultant Matrix

Modeling and optimizing logistics, information, economic and financial intercluster cooperation using the ant colony optimization algorithm

2020 ◽  
Vol 26 (11) ◽  
pp. 2427-2447
Author(s):  
S.N. Yashin ◽  
E.V. Koshelev ◽  
S.A. Borisov
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Population Decline ◽  
Innovation System ◽  
Federal District ◽  
Original Model ◽  
Ant Colony ◽  
Industrial Agglomeration ◽  
Ant Colony Optimization Algorithm

Subject. This article discusses the issues related to the creation of a technology of modeling and optimization of economic, financial, information, and logistics cluster-cluster cooperation within a federal district. Objectives. The article aims to propose a model for determining the optimal center of industrial agglomeration for innovation and industry clusters located in a federal district. Methods. For the study, we used the ant colony optimization algorithm. Results. The article proposes an original model of cluster-cluster cooperation, showing the best version of industrial agglomeration, the cities of Samara, Ulyanovsk, and Dimitrovgrad, for the Volga Federal District as a case study. Conclusions. If the industrial agglomeration center is located in these three cities, the cutting of the overall transportation costs and natural population decline in the Volga Federal District will make it possible to qualitatively improve the foresight of evolution of the large innovation system of the district under study.

Study of grasp-energy based optimal distribution of contact forces on a humanoid robotic hand during object grasp

Robotica ◽  
2021 ◽  
pp. 1-26
Author(s):  
Sourajit Mukherjee ◽  
Abhijit Mahapatra ◽  
Amit Kumar ◽  
Avik Chatterjee
Keyword(s):  
Optimization Algorithm ◽  
Degrees Of Freedom ◽  
Simulated Data ◽  
Performance Measure ◽  
Contact Forces ◽  
Contact Model ◽  
Robotic Hand ◽  
Net Energy ◽  
Optimal Distribution

Abstract A novel grasp optimization algorithm for minimizing the net energy utilized by a five-fingered humanoid robotic hand with twenty degrees of freedom for securing a precise grasp is presented in this study. The algorithm utilizes a compliant contact model with a nonlinear spring and damper system to compute the performance measure, called ‘Grasp Energy’. The measure, subject to constraints, has been minimized to obtain locally optimal cartesian trajectories for securing a grasp. A case study is taken to compare the analytical (applying the optimization algorithm) and the simulated data in MSC.Adams $^{^{\circledR}}$ , to prove the efficacy of the proposed formulation.

Jaya Optimization Algorithm Implemented on A New Novel Design of 6-DOF AUV Body: A Case Study

2021 ◽  
Author(s):  
Noorulden Basil ◽  
Hamzah M. Marhoon ◽  
Ahmed R. Ibrahim
Keyword(s):  
Optimization Algorithm ◽  
Autonomous Underwater Vehicle ◽  
Optimization Technique ◽  
Pid Controllers ◽  
The Novel ◽  
Proportional Integral Derivative ◽  
New Novel ◽  
Novel Design ◽  
Optimum Output

Abstract The Novel Jaya Optimization Algorithm (JOA) was utilized in this research to evaluate the efficiency of a new novel design of Autonomous Underwater Vehicle (AUV). The Three Proportional Integral Derivative (PID) controllers were used to obtain the optimum output for the AUV Trajectory, which can be considered as a main side of the research for solving the AUV Performance. The optimization technique has been developed to solving the motion model of the AUV in order to reduce the rotations of trajectory for the AUV 6-DOF Body in the axis’s in x, y and z for the overall positions, velocity... etc., and to execute the optimum output for the dynamic kinematics model based on the Novel Euler-6 DOF AUV Body Equation implemented on MATLAB R2021a Version.

scholarly journals Multidispatch for Microgrid including Renewable Energy and Electric Vehicles with Robust Optimization Algorithm

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2813 ◽  
Author(s):  
Ruifeng Shi ◽  
Penghui Zhang ◽  
Jie Zhang ◽  
Li Niu ◽  
Xiaoting Han
Keyword(s):  
Renewable Energy ◽  
Robust Optimization ◽  
Electric Vehicles ◽  
Optimization Algorithm ◽  
Environmental Cost ◽  
Uncertain Variables ◽  
System A ◽  
Ev Charging ◽  
Numerical Case Study

With the deterioration of the environment and the depletion of fossil fuel energy, renewable energy has attracted worldwide attention because of its continuous availability from nature. Despite this continuous availability, the uncertainty of intermittent power is a problem for grid dispatching. This paper reports on a study of the scheduling and optimization of microgrid systems for photovoltaic (PV) power and electric vehicles (EVs). We propose a mathematical model to address the uncertainty of PV output and EV charging behavior, and model scheduling optimization that minimizes the economic and environmental cost of a microgrid system. A semi-infinite dual optimization model is then used to deal with the uncertain variables, which can be solved with a robust optimization algorithm. A numerical case study shows that the security and stability of the solution obtained by robust optimization outperformed that of stochastic optimization.

scholarly journals Optimizing the Geometry of Flexure System Topologies Using the Boundary Learning Optimization Tool

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Ali Hatamizadeh ◽  
Yuanping Song ◽  
Jonathan B. Hopkins
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Optimization Algorithm ◽  
Geometric Parameters ◽  
Average Error ◽  
Computational Tool ◽  
Model Training ◽  
Constraint Topologies ◽  
Synthesis Approach

We introduce a new computational tool called the Boundary Learning Optimization Tool (BLOT) that identifies the boundaries of the performance capabilities achieved by general flexure system topologies if their geometric parameters are allowed to vary from their smallest allowable feature sizes to their largest geometrically compatible feature sizes for given constituent materials. The boundaries generated by the BLOT fully define the design spaces of flexure systems and allow designers to visually identify which geometric versions of their synthesized topologies best achieve desired combinations of performance capabilities. The BLOT was created as a complementary tool to the freedom and constraint topologies (FACT) synthesis approach in that the BLOT is intended to optimize the geometry of the flexure topologies synthesized using the FACT approach. The BLOT trains artificial neural networks to create models of parameterized flexure topologies using numerically generated performance solutions from different design instantiations of those topologies. These models are then used by an optimization algorithm to plot the desired topology’s performance boundary. The model-training and boundary-plotting processes iterate using additional numerically generated solutions from each updated boundary generated until the final boundary is guaranteed to be accurate within any average error set by the user. A FACT-synthesized flexure topology is optimized using the BLOT as a simple case study.

Kinematic Calibration for Redundantly Actuated Parallel Mechanisms

2004 ◽  
Vol 126 (2) ◽  
pp. 307-318 ◽  
Author(s):  
Jay il Jeong ◽  
Dongsoo Kang ◽  
Young Man Cho ◽  
Jongwon Kim
Keyword(s):  
Optimization Algorithm ◽  
Parallel Mechanism ◽  
Kinematic Calibration ◽  
Parallel Mechanisms ◽  
Geometrical Constraint ◽  
Kinematic Parameters ◽  
Angle Error ◽  
Calibration Algorithm ◽  
Redundantly Actuated

We present a new kinematic calibration algorithm for redundantly actuated parallel mechanisms, and illustrate the algorithm with a case study of a planar seven-element 2-degree-of-freedom (DOF) mechanism with three actuators. To calibrate a nonredundantly actuated parallel mechanism, one can find actual kinematic parameters by means of geometrical constraint of the mechanism’s kinematic structure and measurement values. However, the calibration algorithm for a nonredundant case does not apply for a redundantly actuated parallel mechanism, because the angle error of the actuating joint varies with position and the geometrical constraint fails to be consistent. Such change of joint angle error comes from constraint torque variation with each kinematic pose (meaning position and orientation). To calibrate a redundant parallel mechanism, one therefore has to consider constraint torque equilibrium and the relationship of constraint torque to torsional deflection, in addition to geometric constraint. In this paper, we develop the calibration algorithm for a redundantly actuated parallel mechanism using these three relationships, and formulate cost functions for an optimization algorithm. As a case study, we executed the calibration of a 2-DOF parallel mechanism using the developed algorithm. Coordinate values of tool plate were measured using a laser ball bar and the actual kinematic parameters were identified with a new cost function of the optimization algorithm. Experimental results showed that the accuracy of the tool plate improved by 82% after kinematic calibration in a redundant actuation case.

Assessment of the use of n-alkanes as markers to describe the complex diets of herbivores

2002 ◽  
Vol 138 (4) ◽  
pp. 425-434 ◽  
Author(s):  
H. MARTINS ◽  
D. A. ELSTON ◽  
R. W. MAYES ◽  
J. A. MILNE
Keyword(s):  
Least Squares ◽  
Plant Species ◽  
Optimization Algorithm ◽  
Diet Composition ◽  
Diet Selection ◽  
Optimization Techniques ◽  
Wild Rabbit ◽  
Sensitivity Tests ◽  
The Individual

Previous approaches to the description of complex diets, based on n-alkanes and optimization techniques, have grouped the plant species to reduce the number of components. Diet estimates have been obtained with least-squares routines by minimizing the discrepancy between faecal alkane concentrations calculated from herbage concentrations and actual faecal alkane concentrations. The effect of diet selection within groups can only be assessed by using sensitivity tests or by giving subjective weights to the individual plants. In the current study, a new optimization algorithm was developed that selects weightings that lead to consistent estimates of group proportions. The diet of the wild rabbit in a southern Portuguese montado was used as a case study. Estimates of the diet composition obtained using the new algorithm were compared with those of a conventional routine. The new algorithm was shown to provide, on average, more accurate estimates of the proportions of the groups in the diet. The effect of grouping plant species according to criteria other than similarity in n-alkane pattern on the accuracy of estimates was shown to be non-significant.

scholarly journals Modified Grasshopper Optimization Algorithm Based Genetic Algorithm for Global Optimization Problems: The System of Nonlinear Equations Case Study

2021 ◽  
Author(s):  
Hala A. Omar ◽  
Mohammed El-Shorbagy
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Local Minimum ◽  
Optimization Problems ◽  
Search Space ◽  
System Of Nonlinear Equations ◽  
Grasshopper Optimization Algorithm ◽  
Speed Up ◽  
Grasshopper Optimization

Abstract Grasshopper optimization algorithm (GOA) is one of the promising optimization algorithms for optimization problems. But, it has the main drawback of trapping into a local minimum, which causes slow convergence or inability to detect a solution. Several modifications and combinations have been proposed to overcome this problem. In this paper, a modified grasshopper optimization algorithm (MGOA) based genetic algorithm (GA) is proposed to overcome this problem. Modifications rely on certain mathematical assumptions and varying the domain of the Cmax control parameter to escape from the local minimum and move the search process to a new improved point. Parameter C is one of the most important parameters in GOA where it balances the exploration and exploitation of the search space. These modifications aim to lead to speed up the convergence rate by reducing the repeated solutions and the number of iterations. The proposed algorithm will be tested on the 19 main test functions to verify and investigate the influence of the proposed modifications. In addition, the algorithm will be applied to solve 5 different cases of nonlinear systems with different types of dimensions and regularity to show the reliability and efficiency of the proposed algorithm. Good results were achieved compared to the original GOA.

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