جستجوی مقالات مرتبط با کلیدواژه « bat algorithm » در نشریات گروه « برق »

The solar radiation value parameter is one of the most important parameters in determining the output power value of photovoltaic panels. Accurate prediction of this parameter is crucial for dispatching and load management planning. Managers and designers encounter economic and managerial challenges due to the uncertainty and difficulty in predicting solar radiation levels. This research introduces a highly accurate prediction method utilizing tree-based methods, enhanced by meta-heuristic algorithms to boost performance. The proposed method emphasizes preventing overfitting and ensuring high reliability for use in Internet of Things systems. Meta-heuristic algorithms are utilized for optimizing tree-based methods, as well as for feature and instance selection. Employing meta-heuristic methods as the main innovation in this research not only optimizes machine learning model settings but also mitigates the impact of noise, outliers, and ineffective inputs, thereby enhancing the final output quality. Utilizing an innovative fitness function in model optimization enhances prediction accuracy and adaptability to real photovoltaic power plant environments. The final outcome is a strong model that has a score of 0.95 with the R-square criterion and is optimal model.

Recognition of handwritten numbers is a complex issue in the field of image processing. Due to being in the digital age, speed and accuracy of intelligent systems have always been developing, which is why image processing has become particularly important. On the other hand, due to the importance of handwritten numbers recognitions and its various applications, in this study, an algorithm has been proposed to recognize Persian handwritten numbers. In this research, two methods of “framing and wavelet transform” have been used to extract the feature. The KNN classification method is also used. After testing different dimensions of the mold, the accuracy percentage of the framing method has been obtained above 87% By selecting the features of the third level of wavelet transform, 95% accuracy is obtained. Using the evolutionary bat algorithm, it has been tried to reduce the features and improve the system.

Wireless sensor networks (WSNs) consist of sensor nodes with limited energy. Energy efficiency is an important issue in WSNs as the sensor nodes are deployed in rugged and non-care areas and consume a lot of energy to send data to the central station or sink if they want to communicate directly with the sink. Recently, the IEEE 802.15.4 protocol is employed as a low-power, low-cost, and low rate communication standard for WSNs, which ensures real-time applications via Guaranteed Time Slot (GTS). Accordingly, in this paper, a new protocol energy efficient bat algorithm and a mobile sink are presented. It can choose the optimal route based on: (1) distance of a sensor node to sink, (2), bat loudness, and (3) energy level of its battery. Using the OPNET simulator version 11.5, the proposed method was simulated by bat and NODIC protocols, and IEEE 802.15.4 and the results were considered in terms of energy consumption, end to end delay, signal to noise ratio, the success probability of sending data to sink and rate of passing data. The results of the simulation showed the use of the above-mentioned parameters in the proposed method leads to the improvement of the network throughput against using the IEEE 802.15.4 protocol, Bat algorithm, and NODIC protocol.