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Abstract - In this paper, an optimal trajectory planning method is presented for robot manipulators with multiple degrees of freedom in 3D space using a new analytical technique for collision avoidance in the presence of ellipsoidal obstacles. To generate the robot’s trajectory, a genetic algorithm with a fuzzy mutation rate is introduced to have a quick access to optimal solutions in a complex workspace. A cubic spline interpolation polynomial is applied to approximate trajectories in the joint space. In order to optimize the objective function, the genetic algorithm determines a number of interior points for curve fitting using interpolation polynomials. The performance of the proposed technique is demonstrated by simulations.

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Vehicles are subject to random road excitations due to road unevenness and variable velocity which causes ride discomfort and fatigue. Ride comfort could be improved by decreasing vehicle accelerations. In this paper, to evaluate the vehicle ride comfort, root mean square acceleration response (RMSAR) is calculated using power spectral density (PSD) of road excitations and these quantities are compared with the ISO2631 boundary values. Then by considering ISO2631, the vehicle’s RMSAR is minimized by optimal design of vehicle suspension viscous damping and stiffness parameters. To solve this nonlinear constrained optimization problem, we utilize genetic algorithms. Also, in the design process the physical restrictions are included. Obtained results demonstrate a considerable improvement of vehicle ride comfort and its dynamic response as a result of reduced accelerations. Comparing the obtained results with those obtained by method of nonlinear programming confirms the supremacy of genetic algorithms.

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The current article presents an analytical approach, for determining the natural frequencies of a rotating cracked Euler–Bernoulli beam with a varying transverse cross-section, using the so-called differential transform method (DTM). First, the natural frequencies of the beam are obtained for different values of the crack position and depth. The results have been validated against those obtained from experimental modal test, Abaqus software and some other methods reported in the literature and a good agreement between the results is observed. Then, the inverse problem is investigated. For this reason, the position and depth of the crack of the rotating beam with a varying transverse cross-section are estimated using the genetic algorithm and then, the natural frequencies are obtained from the modal test. It is seen that the numerical results have a suitable agreement with the actual position and depth of the crack that indicates the effectiveness of this method in determining the parameters of the crack in the rotating beams.

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This paper studies the vibration characteristics of a cracked Timoshenko beam with a varying transverse cross-section using Differential Transform Method (DTM). The effects of the crack location and the crack size in calculating the natural frequencies and mode shapes are investigated. The result have been validated for a beam with and without the crack against those obtained from experimental modal test, Abaqus software and some other methods reported in the literature and a good agreement between the results is observed. The results show that the Timoshenko theory predicts fewer values for the natural frequencies because there is less rigidity, especially for large values of cross-section moment of inertia. Then, the inverse problem is investigated. For this reason, the position and depth of the crack of the beam with a varying transverse cross-section are estimated using the genetic algorithm and then, the natural frequencies are obtained from the modal test. It is seen that the numerical results have a suitable agreement with the actual position and depth of the crack that indicates the effectiveness of this method in determining the parameters of the crack in the Timoshenko beam.

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During recent decades, Energy as one of the most important factors of production and also as one of the most important end products, a special place in the country's economic development and growth development. Hence, the country authorities should try to predict anything more precise energy consumption in the proper planning and guidance consumption, to control the way they desired energy demand and supply parameters. The purpose of this paper is Evaluation Hybrid model of artificial neural networks and genetic algorithms in the forecast consumption energy of Iran. Therefore in this study, data from the annual energy consumption as the output forecasting model range and was used as input variables, data of the annual total population, GDP, imports and exports.The end results were assessed with of different models (RSE), (ME) and (RMSE). Evaluation results showed that the hybrid model of neural networks and genetic algorithm (ANN-GA), compared to other models with the highest accuracy in predicting consumption energy of Iran.

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Lipid oxidation is important issues that can lead to the degradation and destruction of foods containing lipids. A number of antioxidants have been used to solve this problem. Stachys lavandulifolia is a medicinal herb with antioxidant properties. Given that the impact of new technologies compared to traditional methods in terms of saving time, energy, and increase the efficiency of extraction have been identified. The aim of this study was modeling the extraction of antioxidant compounds from Stachys lavandulifolia by ultrasound-assisted extraction method. For this purpose, to model the extraction efficiency of neural network antioxidant compounds, artificial neural network hybrids - genetic algorithm and response surface methodology were used. The best model was obtained based on the results of the neural network model with gradient optimization method, with trainbr training and tansig transfer function and the number of hidden layers of this combination with two neurons 8 in the first layer and 4 in the second layer. For this network structure, an error of 0.0128 and a correlation coefficient of 97.30% were determined. By comparing this method with the response level, the model accuracy increased from 92% to 94.68%. The best result for the hybrid model occurred in the trainbr learning algorithm with the tansig transfer function with a hidden layer and 18 neurons. The error rate and correlation coefficient in this method were equal to 0.0693 and 83.27%, respectively. According to the results of the neural network with the logger method, it answered better and the hybrid method of the genetic algorithm with the neural network was not a suitable model for prediction. Finally, it can be said that mountain tea can be considered as a potential source of antioxidant compounds and neural network can be considered as a successful application method to predict the extraction efficiency of antioxidant compounds.

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The purpose of this study is to develope a sustainable recovery model  for used vehicle in which economic, environmental and social impacts are balanced. This study is result or purpose based and is applied and in terms of method is mathematical programming modeling – quantitative. In order to collecting required data, a questionnaire of paired comparison applied. As well as data modeling for research in case of research, for solving various problems of mathematical programming model by Hypothetical data documentation and experts ideas in Saipa company gathered. Fuzzy Analytic Hierarchy Process has been set appropriate criteria weight for defining social functions applied. Then, using concepts and principles of mathematical modeling has been paid to determine sets, parameters, variables, objective functions and limitations of the mathematical model for locating car waste recovery sites. Then mathematical modeling provided by multi-objective genetic algorithm solved. In this study proposed hybrid algorithm and simple NSGA-II for solving the model used and compared. Some of test problems randomly generated and solved by both algorithms. Also, case study problem is solved using proposed NSGA-II hybrid algorithm. The analysis of data for this study has been done using Excel and MATLAB. Results show that dismantling facility should be establish in Tehran, Semnan, Khorasan, Tabriz, Kashan, and processing facility should be set in Semnan, Khorasan and Tabriz. Solving the test problems show that the proposed NSGA-II hybrid algorithm in terms of solution quality is better simple NSGA-II algorithm and proposed NSGA-II hybrid algorithm is more time consuming to solve.