جستجوی مقالات مرتبط با کلیدواژه « firefly algorithm » در نشریات گروه « صنایع »

Diabetes is one of the most common, dangerous and costly diseases in the world today, which is increasing at an alarming rate. The use of data mining methods can help in the early diagnosis of diabetes, which prevents the progression of this disease and many of its complications such as cardiovascular disease, vision problems and kidney disease. Providing care and health services to people with diabetes provides useful information that can be used to identify, treat, follow-up care and even prevent diabetes. In this study, a new method is presented to improve the diagnosis and prevention of diabetes using data mining methods. In this research, the DBSCAN clustering algorithm is used to cluster the data. Then, using SVM, we classify the data to identify useful data, and finally, with the firefly algorithm, we increase the obtained data to increase we optimize performance with this algorithm. The results of this study indicate that the DBSCAN algorithm is more efficient than other clustering algorithms. Also, the SVM algorithm can achieve 98% accuracy, which compared to other data mining algorithms could achieve a higher accuracy percentage.

Sensor networks are a new generation of networks. In this paper, an algorithm has been proposed to improve the performance of the EAMMH algorithm. For this purpose, the evolutionary firefly algorithm and genetic algorithm that itself has been modeled after natural biological evolution modeling have been used, and instead of randomly choosing cluster heads, it works on the possible answers that have superior attributes and also have a higher survival rate. The results of comparing LEACH and EAMMH protocols and the proposed method in terms of the number of dead nodes compared to the number of execution times based on 50, 100, and 200 nodes indicates that the number of dead nodes for the simulation of LEACH protocol is almost equal to the number of dead nodes for the EAMMH protocol, but the proposed algorithm has approximately 10 percent less dead nodes, but by an increase in the number of nodes (200 nodes) the number of dead nodes have decreased 35 percent and 22 percent compared to LEACH and EAMMH algorithms respectively.

Sensor networks are a new generation of networks. In this paper, an algorithm has been proposed to improve the performance of the EAMMH algorithm. For this purpose, the evolutionary firefly algorithm and genetic algorithm that itself has been modeled after natural biological evolution modeling have been used, and instead of randomly choosing cluster heads, it works on the possible answers that have superior attributes and also have a higher survival rate. The results of comparing LEACH and EAMMH protocols and the proposed method in terms of the number of dead nodes compared to the number of execution times based on 50, 100, and 200 nodes indicates that the number of dead nodes for the simulation of LEACH protocol is almost equal to the number of dead nodes for the EAMMH protocol, but the proposed algorithm has approximately 10 percent less dead nodes, but by an increase in the number of nodes (200 nodes) the number of dead nodes have decreased 35 percent and 22 percent compared to LEACH and EAMMH algorithms respectively.

Wireless sensor networks (WSN) comprise of a large number of low-power but low-cost small sensing nodes which distributed randomly in a specific area far from the human reach , for the purpose of surveillance, recognition and monitoring the nearby environment based on their inter communication. Each node includes units i.e. sensing, processing, transducing, location positioning and power supply. Owing to various features of sensors such as quickness, self-awareness and self configurability, WSNs have various applications in different areas and many methods are being developed to improve their performance in an application specific way. WSNs face many challenges, including energy restrictions, security, communication reliability, design, and so on. It should be mentioned that it is hardly possible to balance all these challenges due to the conflicts they have with each other. Hitherto, researchers have done extensive studies to bridle these concerns. Sensor nodes are small and have often limited and irreplaceable sources of energy. Furthermore, they can send information at short distances. In long run operations, each node generally does the data collection singly. In this paper, a multi-objective swarm intelligence-based algorithm built on Shuffled frog-leaping and Firefly Algorithm (named MOFSA) is presented as an adaptive clustering-based multi-hop routing protocol for WSNs. MOFSA's multi-objective function regards different criteria (e.g., inter- and intra-cluster distances, residual energy of nodes, distances from the sink, overlap and load of clusters) to select appropriate cluster heads at each round. Moreover, another multi-objective function is proposed to select the forwarder nodes in the routing phase. The controllable parameters of MOFSA in both clustering and multi-hop phases can be adaptively tuned to achieve the best performance based on the network requirements according to the specific application. Simulation outcomes demonstrate average lifetime improvements of 230% compared with LEACH, 100% compared with ERA, 38% compared with SIF and 260% compared with FSFLA in different network scenarios.

Sensor networks are a new generation of networks. In this paper, an algorithm has been proposed to improve the performance of the EAMMH algorithm. For this purpose, the evolutionary firefly algorithm and genetic algorithm that itself has been modeled after natural biological evolution modeling have been used, and instead of randomly choosing cluster heads, it works on the possible answers that have superior attributes and also have a higher survival rate. The results of comparing LEACH and EAMMH protocols and the proposed method in terms of the number of dead nodes compared to the number of execution times based on 50, 100, and 200 nodes indicates that the number of dead nodes for the simulation of LEACH protocol is almost equal to the number of dead nodes for the EAMMH protocol, but the proposed algorithm has approximately 10 percent less dead nodes, but by an increase in the number of nodes (200 nodes) the number of dead nodes have decreased 35 percent and 22 percent compared to LEACH and EAMMH algorithms respectively.