جستجوی مقالات مرتبط با کلیدواژه « الگوریتم بهینه سازی فاخته » در نشریات گروه « برق »

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.

In this paper, a novel hybrid algorithm is represented named SA-COAMF by using cuckoo optimization algorithm, simulated annealing and expected maximization for motif finding problem. SA-COAMF is very efficient in global optimal convergence. In the algorithm, two models of motif representation, consensus and probability matrix representations, are applied to take the advantage of them. SA-COAMF is run on experimental datasets (SCPD database). The results are compared with some well-known algorithms (GA- DPAF, PSO and MEME) to show that our algorithm is efficient.

Fractional-order PID (FOPID) controller is a generalization of standard PID controller using fractional calculus. Compared with the Standard PID controller، two adjustable variables “differential order” and “integral order” are added to the PID controller. Three tank system is a nonlinear multivariable process that is a good prototype of chemical industrial processes. Cuckoo Optimization Algorithm (COA)، that was recently introduced has shown its good performance in optimization problems. In this study، Improved Cuckoo Optimization Algorithm (ICOA) has been presented. The aim of the paper is to compare different controllers tuned with a Improved Cuckoo Optimization Algorithm (ICOA) for Three Tank System. In order to compare the performance of the optimized FOPID controller with other controllers، Genetic Algorithm (GA)، Particle swarm optimization (PSO)، Cuckoo Optimization Algorithm (COA) and Imperialist Competitive Algorithm (ICA).

In this paper، for solving the Multi-Objective Multi-area Environmental Economic Load Dispatch، Cuckoo Optimization Algorithm is proposed. In resent year، the economic load dispatch is considered as a multi-area power systems problem، but، in none of these studies the environmental problem is not considered. Accordingly in this paper the model of pollution constraint is imposed and the terms of issue of the new model is considered. On the other hand، the cuckoo optimization algorithm is an evolutionary search algorithm which is proposed in recent years by researchers in various fields of engineering. However، the advantage of this algorithm for solving power systems has not been investigated. So، the ability of Cuckoo Optimization Algorithm for solving environmental economic load dispatch with respect to the contamination (the proposed model) in multi-area power systems is investigated. The proposed technique is applied on different issues in many single area systems (6، 10 and 40 generation unit) and the distribution of the economic burden of pollution provisions regarding the function of the immune system in single area (6 units، 30 buses). Finally، the proposed algorithm in solving economic load dispatch in the region of 4 units and two additional constraints is investigated. The performance of this algorithm is compared with other techniques which demonstrate the superiority of this method than the others.

Regarding the growing use of distributed generation resources, optimal design of these units is necessary in the power system especially in the distribution network. In this paper, Cuckoo Optimization Algorithm with multiple purposes was used to determine optimal location and size of distributed generation resources. The investigated purposes include reducing line losses, reducing medium voltage deviation and voltage stability. Cuckoo Algorithm is an algorithm based on population which is derived from the life of cuckoos in nature. Using this algorithm leads to increased accuracy and speed of convergence. In order for the implementation of the proposed algorithm, distributed generation resources are considered in two modes. In the first mode, distributed generation resources only produce active power; but in the second mode, they produce reactive power too. Simulation is done in 33 bus bar radial system. The results of simulation show optimal convergence of the suggested algorithm. In addition, the obtained results were compared with the results of optimization algorithm of the aggregation of particles and genetics algorithm in order to show the efficiency of the suggested algorithm. The comparison indicated tangible superiority of Cuckoo Algorithm in comparison with the mentioned methods.