جستجوی مقالات مرتبط با کلیدواژه "meta-heuristic algorithm" در نشریات گروه "مهندسی دریا"

In the spoofing attack, the attacker sends his fake request messages to the destination server computer and pretends that the requests were sent from a source that is a node in the network with a valid and reliable IP address. In this article, an intelligent method is introduced to detect the presence of several fake users. Evolutionary processing has also been used to improve the accuracy and precision of detecting the number of attackers. The artificial neural network reveals the spoofing attack by detecting the change in the signal pattern of the IP packets received from a node in the access point. The metaheuristic algorithm of imperial competition also determines the number of attackers using clustering. By extracting the features of signal strength, signal phase, and received signal energy, this algorithm can cluster the signal pattern of users with a specific IP address. In the worst case, it correctly detects attacks with a probability of 98. As a result, if the number of clusters is more than the number of active IP addresses of the wireless network, this number of excess clusters shows the number of attackers. The simulation results of the proposed algorithm show that it has 99% precision and 98% accuracy in detecting attacks.

Due to the presence of too many parameters in the problem of the marine propeller design such as the existence of several conflicting objectives, unstable and very complex environmental conditions, marine propellers design has been one of the challenging subjects for designers and researchers in this field. Nowadays, meta-heuristic algorithms are used as one of the highly efficient solutions to solve complex engineering problems. In this paper, for the first time, SMA algorithm is used to design the marine propellers. For this purpose, two targets viz. maximize the efficiency and minimize the cavitation, which are in conflict with each other, as the fitness function is considered. To do this, during several trials, the effect of two important parameters on the fitness function, i.e. chord and thickness of the propeller, is calculated and the most optimal mode is selected by the SMA algorithm. In order to compare the results, the benchmark algorithms such as GWO، ALO، MFO و BBO are used. The results show that the propeller with 5 or 6 blades with rotation speeds between 180 to 190 RPM will have the best performance in the tradeoff between efficiency and cavitation. Results indicate the improvement of 2.4 percent in efficiency and 21dB reduction in cavitation level.

Due to the presence of too many parameters in the problem of the marine propeller design has been one of the challenging subjects for designers and researchers in this field. Nowadays, meta-heuristic algorithms are used to solve complex engineering problems. In this paper, for the first time, Independent Group Particle Swarm Optimization (IGPSO) algorithm is used to design the marine propellers. For this purpose, two targets viz. maximize the efficiency and minimize the cavitation as the fitness function are considered. To do this, during several trials, the effect of two important parameters on the fitness function, i.e. chord and thickness of the propeller, are calculated and the most optimal mode is selected by the IGPSO algorithm. In order to compare the results, the benchmark algorithms such as PSO and GA are used. The results show that the propeller with 5 or 6 blades with rotation speeds between 180 to 190 RPM will have the best performance in the tradeoff between efficiency and cavitation. The designed propeller by using IGPSO algorithm produced 86.64 dB and 106.79 dB noise for cavitation and non-cavitation mode, respectively. So, they produce 1.24 dB and 0.80 dB noise level lesser than other benchmark algorithms.