جستجوی مقالات مرتبط با کلیدواژه « meta-heuristic algorithm » در نشریات گروه « برق »

Nowadays, clustering methods have received much attention because the volume and variety of data are increasing considerably.The main problem of classical clustering methods is that they easily fall into local optima. Meta-heuristic algorithms have shown good results in data clustering. They can search the problem space to find appropriate cluster centers. One of these algorithms is gray optimization wolf (GWO) algorithm. The GWO algorithm shows a good exploitation and obtains good solutions in some problems, but its disadvantage is poor exploration. As a result, the algorithm converges to local optima in some problems. In this study, an improved version of gray optimization wolf (GWO) algorithm called 4-gray wolf optimization (4GWO) algorithm is proposed for data clustering. In 4GWO, the exploration capability of GWO is improved, using the best position of the fourth group of wolves called scout omega wolves. The movement of each wolf is calculated based on its score. The better score is closer to the best solution and vice versa. The performance of 4GWO algorithm for the data clustering (4GWO-C) is compared with GWO, particle swarm optimization (PSO), artificial bee colony (ABC), symbiotic organisms search (SOS) and salp swarm algorithm (SSA) on fourteen datasets. Also, the efficiency of 4GWO-C is compared with several various GWO algorithms on these datasets. The results show a significant improvement of the proposed algorithm compared with other algorithms. Also, EGWO as an Improved GWO has the second rank among the different versions of GWO algorithms. The average of F-measure obtained by 4GWO-C is 82.172%; while, PSO-C as the second best algorithm provides 78.284% on all datasets.

Digital transformers are considered as one of the digital circuits being widely used in signal and data processing systems, audio and video processing, medical signal processing as well as telecommunication systems. Transforms such as Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) and Fast Fourier Transform (FFT) are among the ones being commonly used in this area. As an illustration, the DCT is employed in compressing the images. Moreover, the FFT can be utilized in separating the signal spectrum in signal processing systems as fast as possible. The DWT is used in separating the signal spectrum in a variety of applications from signal processing to telecommunication systems, as well. In order to build a VLSI circuit, several steps have to be taken from chip design to final construction. The first step in the synthesis of the integrated circuits is called high-level synthesis (HLS), in which a structural characteristic is obtained from a behavioral or algorithmic description. The resulting structural characteristic is equivalent to the one being considered in the behavioral description and it somehow represents the method for implementing the behavioral description as a result several structural descriptions could be implementable for each behavioral description. Therefore, depending on the intended use, the characteristic will be selected that outperforms the others. The main purpose of the HLS is to optimize the power consumption, the chip occupied area and delayed and is fulfilled by selecting the appropriate number of operating units and how they are implemented to the operators. This is generally accomplished through a graph analysis called the data flow graph (DFG) which is a graphical representation of the type and how the operators connect. In the DFG, each node is equivalent to an operator while the edges represent the relationship between these operators. Experience has proved that if the level of design optimization is high, in addition to higher efficiency, the design time will be lower, which is why the researchers are far more interested in optimization at higher levels of design than the lower levels. The complex, extensive, and discrete nature of the HLS problems have been ranked them among the most complex problems in VLSI circuits engineering. Bearing this mind, using meta-heuristic and Swarm intelligence methods to solve high-level synthesis projects seems to be a favored option. In this paper, a heuristic method called Moth-Flame Optimization (MFO) has been used to solve the HLS problem in the design of digital transformer to find the optimal response. The MFO is a population-based heuristic algorithm that optimizes the problems using the laws of nature. The leading notion behind the MFO algorithm inspired from the moths’ movements and their instinctive navigation during the night. In the MFO algorithm, the moths are like chromosomes in the GA and like the particles in the PSO algorithm. In order to compare and prove the efficiency of the proposed method, it was applied on the test data with the GA-based method separately but with the same initial conditions. The comparative results along with the results of the GA-based method demonstrated that the proposed method exhibits a higher ability to provide the appropriate hardware structure and high-level synthesis of various types of transformers. Another outstanding feature of the proposed method is its high speed of finding an optimal response with an average of more than 20% greater than the GA based method.

In this research, 12 approaches were proposed to create an optimal vector based on supporting vector machine and neural networks based on genetic algorithm, cuckoo and particle swarm Optimization (PSO). In this regard, we have tried to design a system that reduces the cost of data collection. For this purpose, three data sets with time series capability of standard UCI data were used in this study. The results of the approaches used in this research show the good performance of all the used algorithms. However, the ability and performance of each approach vary according to the type and nature of the data. This has sometimes led to better results from the MLP neural network and the GA or Cuckoo algorithm, and in some cases, the PSO algorithm has better outcomes. Regarding the results, it can be said that the use of feature selection based on semi-regulatory classification reduces system error, increases the accuracy and increases the speed of time series estimation. Hence, by using the efficient and powerful MLP Neural Network and backup vector machine along with the optimization algorithm and metamorphic, an optimal combination classification system can be designed for time series estimation.