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Go to Editorial ManagerThis paper describes the problem of minimizing-the sidelobe levels in the radiation pattern of antenna arrays by using the genetic algorithm. Two types of genetic algorithms representation are used here:., binaiy and continuous genetic algorithms depending on the nature of the problem at hand. Adaptive genetic algorithm wnich is a special type of genetic algorithm is used in this work. The obtained results explain the capability of this approach to obtain the desired sidelobe level.
A linked simulation-optimization model for obtaining the optimum management of groundwater flow is presented in this research. (MODFLOW, 98) packages are used to simulate the flow of the groundwater system. This model is integrated with an optimization model which is based on the genetic algorithm (GA). Three management cases were undertaken by running the model with adopted calibrated parameters. In the first case found the optimum value of the objective function is (0.32947E+08 m3/year), in other words, the pumping rates could be raised to nine times the current pumping rates, with a highest decline in the hydraulic heads of groundwater compared with initial hydraulic heads reached to 6 cm. In a second case twenty six wells out of thirty five can be operated with "on/off" status associated with each well to obtain the maximum value of pumping rate. In third case is allowed to move a location of well anywhere within a user defined region of the model grid until the optimal location is reached. The optimum value of objective function in third case is (0.35539E+08 m3/year) with 8% increasing of the pumping rates compared with the first case. This is due to the random distribution of existing well locations.
An intelligent and anticipatory speed controller for internal combustion engines was designed theoretically and examined experimentally. This design was based on the addition of a torque loop to the main speed loop. The model can sense the external load with the help of a load cell and send this signal to a soft computing unit for analysis and processing. This scheme will improve the ability of anticipation of controller since it treats the factors that affect the speed, not the speed itself. The experimental design was implemented using two types of actuating techniques; an intelligent throttling actuator and an intelligent injection actuator. The signal was analyzed by using intelligent techniques such as fuzzy logic, neural network and genetic algorithm. The experimental data were used to train the neural and the Adaptive Neuro–Fuzzy Inference System. The comparison of the results obtained in this work with other available models proved the efficiency and the robustness of the present model.
The objective function is to satisfy certain constraints and achieve minimum capital, maintenance, and operation costs. Ion exchange unit was used in this study. This study includes development of computer program for advanced wastewater treatment plants design adopting genetic algorithm. The program was developed using Matlab software. The output of the genetic algorithm includes the finding of optimum design criteria for advanced wastewater treatment plants. The obtained design criteria are satisfying the required effluent quality with minimum treatment cost. Based on results of applying GA on ion exchange treatment plant, it was found that the optimum values of bed depth, service flowrate, regenerate flowrate, and back wash rate are 0.71m, 25m3/m3.hr, 8 m3/m3.hr, and 55 m/hr respectively.
This paper presents an approach for the recognition of off-line handwritten numeric strings using genetic algorithm. The proposed scheme is divided in two parts. The first part is remove the image noise, then the vertical projection is used to segment the numeric strings at isolated digits and every digit will be presented separately to the second part. The second part using improved genetic algori_thm to recognize isolated handwritten digit. The result of the recognition of the numeric strings will display at the exit of the global system.
The present work includes a study on the effect of loading rubber waste into cement mortar on the thermal and mechanical properties of a thermal insulator.The experimental work of the study included the preparation of ten models of 35 mm diameter and 5 mm thickness. Portland cement and natural sand were used as a matrix and rubber waste (extracted from the consumed tires) as a filler was added in weight percentages ( 5% ,10% ,15% ,20% ,25% ,30% ,35% ,40%,45% and 50%). Water was also used as a binder.Also, the experimental work included conducting a thermal conductivity test using Lee’s Disk method, and a hardness test using the Shore scale. The theoretical side included extraction of empirical equations, depending on the experimental results. The thermal conductivity equation was for two variables, temperature and mass fraction. While the hardness equation was for one variable, mass fraction. Theoretically determined heat capacity was extracted using the equations of the composites. Based on the empirical equations of thermal conductivity and hardness and using the technique of multi- objectives genetic algorithm, the optimum values of temperature and mass fraction were extracted, which achieve the best thermal insulation of the mortar. The results showed a significant decrease in thermal conductivity. The reduction in thermal conductivity was (90.3%) at 5% and reduced to (95.73%) at 50%. The specific heat capacity was increasing as the percentage of rubber waste increase. The results also indicated a decrease in hardness. The optimal value of thermal insulation was (0.02658 W/m 2 .ºC ) as a thermal conductivity and (58.07 N/m 2 ) as a hardness, at temperature (50°C) and mass fraction (27.764%) of rubber waste.
In this article, a hybrid optimization method has been proposed consisting of Adaptive Genetic Algorithms (AGAs) and Constrained Nonlinear Programming (NLP) to solve the problems of performance optimization of circular array antenna consisting paraOel center feeding short dipoles elements with two complex nonlinear optimization problems. In the first problem. the hybrid optimization algorithm is used to reduce the value of sidelobe level in the circular array radiation pattern by finding the oPtlmal values of the excitation coefficients of each element in the clrcular array. In the second problem, a synthesis of circular array with different forms of the desired radiation pattern is considered. Several examples are considered here to verify the validlty of this method. Comparisons were made between the results of this method and the results obtained by {SGA) Standard Genetic Algorithm, and it is clearly shown that this method is more efficient and flexible in solving the problems of performance optimization of circular array antenna .
This paper deals with the modeling and control strategics of the motion of wheeled mobile robot. The model of the mobile robot has two driving wheels and the azimuth and velocity are dependently controlled by two PID controllers. The PID controller is one of the earliest and famous industrial controllers. It has many advantages: It is economic, simple easy to be tuned and robu.~t. The tuning of these controllers is governed by system nonlinearities and continuous parameter variations. lbis paper deals with the optimal design of a PID controller for path tracking of mobile robot by using genetic algorithms (GA). The designed controller is tested for different paths.
Crow Search Algorithm is an innovative meta- heuristic optimization algorithm. In this paper, chaotic maps are combined into Crow Search Algorithm to increase its global optimization. Ten variant chaotic maps are used and the Tent map is found as the best choices for high dimensional problems. The novel Chaotic Crow Search Algorithm is relied on the substitution of a random location of search space and the awareness parameter of crow with chaotic sequences. The results show that the chaotic maps are able to enhance the performance of the Crow Search Algorithm. Also the novel Chaotic Crow Search Algorithm outperforms the conventional Crow Search Algorithm, first version of Chaotic Crow Search Algorithm, Genetic Algorithm, and Particle Swarm Optimization Algorithm from the point view of speed convergence and the function dimensions.