optimization algorithm

This research uses a new multi-objective optimization algorithm to design a single pole antenna with specific electromagnetic characteristics. This algorithm uses a hybrid chaotic function to integrate the customized mutated particle swarm algorithm with the modified genetic algorithm. By avoiding getting trapped in local minima, the new hybrid approach achieves desired results faster than conventional particle swarm algorithms and genetic algorithms. The performance of the proposed meta-heuristic algorithm has been successfully simulated and stabilized using benchmark functions such as Rastrigen's function, Ackley's function, Rosenbrook's function, and Booth's function. Finally, the validity of the presented approach for electromagnetic applications is demonstrated by optimizing a planar microstrip monopole antenna with a simple structure. The proposed algorithm allows the optimization criteria to be customized to achieve the predetermined results for return loss and resonance frequency. The optimization algorithm developed in MATLAB is used to determine the necessary parameter settings in order to achieve the expected frequency bands using custom mutated particle swarm algorithm or heuristic modified genetics. The dimensions of the proposed antenna elements significantly affect the antenna performance.

In this paper, the process of designing and construction of three-drive laboratory Drone system with cold gas thrusters as well as modeling and estimation of its model parameters are presented. These three thrusters are designed with an angle difference of 120 degrees to control the pitch and roll angles. According to the structure of the designed platform, a three-inputs-two-outputs linear model is considered for modeling the mentioned system, and all connections between inputs and outputs are second-order linear models. The parameters of the model have been extracted based on the collection of laboratory data and the use of genetic algorithm. The results obtained from the simulation based on the extracted model and the comparison with the results of the practical tests indicate that a suitable modeling and estimation of the system has been provided. The results show that there is 16% error for modeling and estimation around the roll axis and 19% around the vertical axis, so the estimation around the roll axis is better.

Face recognition is a pattern recognition process that is specifically performed on faces. Face recognition has many applications in identifying credit cards, security systems, and other cases. Creating a face recognition system with high accuracy is a big challenge that has been the focus of various researchers in recent years. The feature extraction process and classification are two important issues in diagnosis systems that can play a significant role in increasing the accuracy of diagnosis. Considering this issue, in this study, taking into account the combined features and optimizing the cuckoo algorithm, a method to improve the accuracy of face recognition is proposed. In the presented method, seven features are extracted from the images in the database, and then by obtaining the feature vector, the steps related to feature selection are performed using the cuckoo algorithm. The proposed method has been implemented with MATLAB software and compared with other methods. The evaluation results show that the proposed method was able to perform the detection on the images of ORL and FDBB databases with 93.00% and 95.12% accuracy, respectively. The result obtained for this evaluation criterion has a higher value than other compared methods.

Frequency load management is a critical challenge in the field of engineering and power system operation. This study introduces a new approach to address this issue using the Particle Swarm Optimization (PSO) algorithm. By employing a multi-objective cost function, this method optimizes the benefits of state feedback matrix. Additionally, the proposed cost function strategically places the closed-loop system poles within a defined range to accelerate frequency stability and minimize power transfer differentials between regions. To minimize the specified cost function, an optimal teaching-learning-based optimization strategy is adopted. Furthermore, the integration of fuzzy logic techniques for combining essential objective functions is recommended. The evaluation of the proposed method involves applying the controller to a two-area power system while considering governor saturation constraints and comparing the results with those of a traditional PI controller. Simulation results emphasize the effectiveness of the proposed approach, demonstrating improvements in system features such as settling time and peak response time.

The use of FACTS devices is increasing due to the improvement of the transmission capacity of lines and the reduction of losses and costs related to the power system, and for this reason, new devices are used. One of these tools is the of interline power flow controller (IPFC), whose main task is to control the inter-line power of the transmission system, and it is one of the newest FACTS tools. In this article, the placement and regulation of the controllable parameters of IPFC has been discussed in an optimal way. The main goal of this optimal placement of IPFC in a multi-objective manner in the power network is to simultaneously reduce system losses, cost, and also improve system load taking into account the relevant constraints. In order to carry out placement studies, the selection algorithm based on beam forming or PESA_II has been used, and the placement process has been carried out on the IEEE 30-bus network. The results show that all indicators have improved so that compared to the case where IPFC is not present in the network, active power loss (%) is reduced by 59.43, load capacity (%) is increased by 32.08, reactive power loss ( 26.63% reduction and power plant fuel cost has decreased 59.01%.document.

Due to the many applications of DC to DC converters in electronics, regulating their output voltage is very important. In many applications it is necessary to change the DC voltage from one level to another. DC -DC converters are used for this purpose. The conversion of DC voltage from one level to another is done by switching elements such as transistors and diodes. Recently, the control of these converters has found a special place in scientific texts. Therefore, one of the objectives of this paper is to control and regulate the output voltage of the converter. The controller proposed in this paper to control the DC voltage level of the converter output is an optimized neural network controller with an algorithm based on colonial competition. The proposed controller function is that first the neural network is designed according to the expected goals of the system and then it is optimized by determining a suitable multi-objective benchmark function using the network structure optimization algorithm. This improves the performance of the control system. Because the proper selection of design parameters has a great role in the performance of the neural network that plays the role of controller. The proposed neural network function is to apply the appropriate signal transducer (PWM signal) to the switching elements in order to increase the performance. The results compared to the PID controller indicate the superiority of the proposed method.

Recently, the most common tool to compensate for various organ defects is tissue transplantation with several problems involved. These problems have led to the rapid growth of tissue engineering with a designed tissue approach or organ substitute in the last decade. For this purpose, it is important to determine the tissue mechanical properties. In this study, to obtain the cartilage structural parameters, isotropic Pro-Hyper-Viscoelastic Mooney-Rivlin and Neo-Hooke are used. These model coefficients are obtained by reverse engineering methods and using a coupled finite element-optimization algorithm utilized unconfined stress relaxation tests with root-mean-square error (RMSE)) less than 0.036, 0.033 for Neo-Hooke and Mooney-Rivlin respectively. Using Neo-Hooke and Mooney-Rivlin models, the modulus of elasticity was 0.47 MPa and 0.44 MPa, and the shear modulus was 0.188 MPa and 0.184 MPa, respectively. The predicted tissue mechanical response obtained by the finite element model showed that the Mooney-Rivlin model is more consistent with the stress relaxation experiments than Neo-Hooke one. The results showed that during the stress relaxation test, by applying a compressing load on the sample, initially the fluid pressurization in the matrix pores has the most contribution in the load-bearing (total stress). When time elapses, the fluid contribution in the load-bearing decreases, and the solid matrix contribution increases.

In this study, supply electrical, heating and cooling loads in the building by a separate production system and a cogeneration system, particle swarm optimization algorithm in five different scenarios were studied and analyzed (For example, Data and Information a high-rise building base 72, in the city of Kerman is used). The results show that, cold and heat power system of micro gas turbine with a capacity of 195kW as the primary driver, of absorption chiller and electric chiller respectively with a capacity of 281kW and 439kW to provide cooling load, boiler with a capacity of 187kW to compensate for the lack of heat load and from photovoltaic/thermal system with a capacity of 52.8kW to generation electric load and supply hot water used, optimal system is to supply the base building loads. In this optimized system, the annual electricity sales revenue of  2,814,420,000R, The annual cost of buying the power of 211,560,000R, The cost of buying fuel annual consumption of 475,560,000R, and annual production carbon dioxide emissions in the building are 230.42tons. The initial payback period of the project is estimated at 5.167 years.

The stator of the doubly-fed induction generator-based wind turbine is connected directly to the grid, which makes possible reactive power compensation of the grid. In this paper, controlling reactive power in DFIG in two modes, the first assumption is neglecting stator resistance while the second one is considering stator resistance and its flux variations in four performance modes including maximum stator reactive power absorption, maximum stator reactive power generation, mode minimum casualties and minimization modes are provided. For each mode in each of assumptions and optimization problem introduced and PSO algorithm utilized to find a feasible solution. By solving the optimization problems with aim of PSO algorithm, the required for controlling reactive power in each mode is achieved. To demonstrate the efficiency of proposed method, the results compared with another method based on an iterative algorithm. Simulation results show that considering linkage flux variations of stator as a constraint of the optimization problems has led to good performance in controlling reactive power of DFIG.

The design of three-phase induction motors is a challenge in electrical engineering. Therefore, new design techniques are continuously provided. Since the design of the induction motors is carried out for different purposes, it is difficult to find a method that can addresses all the targets. Nowadays, the normal methods used to solve multi-objective problems are the optimization strategies. In this paper, the meta-heuristic optimization method has been used to design the squirrel cage induction motors with the aim of increasing efficiency and reducing costs. In this way, modeling of induction motor is done accurately and to solve this optimization problem, a new method of Multi-objective optimization of the Salp Swarm Algorithm (MSSA) has been provided. This algorithm is inspired by the social behavior of Salps. The proposed method is applied to the data of an induction motor of 2.5 KW with IE2 efficiency. The results of single-objective and multi-objective optimization exhibit that the design of an induction motor with two objective functions increases the efficiency and reduces the cost. Additionally, the MSSA algorithm is compared with the methods of genetic algorithms for indirect sorting (NSGA-II) and multi-object particle optimization algorithms (MOPSO). The MSSA algorithm has a convergence rate and a set of optimal response sets which demonstrates a good performance in optimal design of the induction motor to increase efficiency and reduce its costs.