Dimensional synthesis of mechanisms to trace given points is an important issue in mechanism and machine science. Having no exact solution makes this issue an optimization problem. This study offers an optimization approach for the dimensional synthesis of planar mechanisms. Four-bar mechanisms having one degree of freedom (DOF) are chosen as the configurations. The proposed method is implemented by establishing the objective functions with specified constraints and searching for the results by using an optimization algorithm. Genetic Algorithm (GA) in Optimization Toolbox-Matlab® is selected as a solver. Different types of four-bar mechanisms like crank-rocker and double-crank including different target points are performed. Mechanisms are depicted by resulted parameters and a Matlab® script prepared plays their animations. As a result, it is proved that the mechanisms whose dimensional properties are obtained by the GA solver have a good tracing capability for the desired paths. This study has the property of being a design guide. Its application is not limited to four bar mechanism. Planar mechanisms with different configurations can be easily synthesized by using this technique.

This study focuses on the investigation of intelligent form-finding and vibration analysis of a triangular polyhedral tensegrity that is enclosed within a sphere and subjected to external loads. The nonlinear dynamic equations of the system are derived using the Lagrangian approach and the finite element method. The proposed form-finding approach, which is based on a basic genetic algorithm, can determine regular or irregular tensegrity shapes without dimensional constraints. Stable tensegrity structures are generated from random configurations and based on defined constraints (nodes located on the sphere, parallelism, and area of upper and lower surfaces), and shape finding is performed using the fitness function of the genetic algorithm and multi-objective optimization goals. The genetic algorithm's efficacy in determining the shape of structures with unpredictable configurations is evaluated in two distinct scenarios: one involving a known connection matrix and the other involving fixed or random member positions (struts and cables). The shapes obtained from the algorithm suggested in this study are validated using the force density approach, and their vibration characteristics are examined. The findings of the comparative study demonstrate the efficacy of the proposed methodology in determining the vibrational behavior of tensegrity structures through the utilization of intelligent shape seeking techniques.

In this paper we investigate the performance of simulated annealing (SA) and genetic algorithm (GA) in preventive part replacement for minimum downtime maintenance planning. Therefore some evaluation criteria are explained in order to analyze the performance of the algorithms. So it can be decided which algorithm is more suitable to apply in preventive part replacement.

A Genetic Algorithm (GA) method for optimization of multi-reservoir systems operation is proposed in this paper. In this method, the parameters of operating policies are optimized using system simulation results. Hence, any operating problem with any sort of objective function, constraints and structure of operating policy can be optimized by GA. The method is applied to a 3-reservoir system and is compared with two traditional methods of Stochastic Dynamic Programming and Dynamic Programming and Regression. The results show that GA is superior both in objective function value and in computational speed. The proposed method is further improved using a mutation power updating rule and a varying period simulation method. The later is a novel procedure proposed in this paper that is believed to help in solving computational time problem in large systems. These revisions are evaluated and proved to be very useful in converging to better solutions in much less time. The final GA method is eventually evaluated as a very efficient procedure that is able to solve problems of large multi-reservoir system which is usually impossible by traditional methods. In fact, the real performance of the GA method starts where others fail to function.

In this paper an optimized high frequency lumped model of Induction motor is presented. Model parameters are identified and optimized using Genetic Algorithm (GA). A novel model and approach in an improved high frequency based on GA for parameter identification are used. At first, parameters are limited and then fitted using GA for best fitting. The proposed model considered accurate simulation of both differential and common mode behavior in the EMI-frequency range from 100 Hz to 30MHz. Model parameters which extracted from GA are compared with experimental data in both magnitude and phase at the same time and results show a good accordance between the experimental results and simulation results of the proposed model. A least mean square (LMS) method was used with a GA optimization method to solve the identification problem. The proposed model is suitable to obtain the simulation models to predict high frequency conducted Electromagnetic Interference (EMI), over voltage on terminated motor and common mode current in cable fed induction motor.

In this study, the future situation energy demand of earthy transport section has been predicted by using the trend of effective variables in energy demand of earthy transport section in Iran. In other words, two forms of non-linear gasoline and gasoil energy demand functions of earthy transport section have been simulated, by using Genetic algorithm (GA) Approach. GA-based transport section energy demand Simulation (GATEDS) model has been developed by using population, gross domestic production, vehicle-km and collective value of imported and produced vehicle quantity in variables. In this essay, the simulated deman equations is exponential and quadratic. In energy demand function, the quadratic form provides better results in the prediction of earthy transport section gasoline demand and the exponential form provides better results in the prediction of earthy transport section gasoil demand which can be used for energy section projects in Iran with a higher simulation criterion.