cuckoo algorithm

The Internet of Things (IoT) refers to the connection of various devices to each other via the internet. Conceptually, the IoT can be defined as a dynamic, self-configuring network infrastructure based on standards and participatory communication protocols. The main goal of the IoT is to lead towards a better and safer community. However, one of the fundamental challenges in developing the IoT is the issue of security, and intrusion detection systems are one of the main methods to create security in the IoT. On the other hand, Convolutional Neural Network (CNN), with its specific features, is one of the best methods for analyzing network data. This network is a type of deep neural network composed of multiple layers that can ultimately reduce the dimensions of features. Additionally, the cuckoo algorithm has parameters required for configuration in the initial search, which are very few and can naturally and efficiently cope with multi-state problems. In this paper, a new method for intrusion detection in the IoT using CNN and feature selection by the cuckoo algorithm is presented. Simulation results indicate the satisfactory performance of the proposed method.

The Internet of Things (IoT) refers to the connection of various devices to each other via the internet. Conceptually, the IoT can be defined as a dynamic, self-configuring network infrastructure based on standards and participatory communication protocols. The main goal of the IoT is to lead towards a better and safer community. However, one of the fundamental challenges in developing the IoT is the issue of security, and intrusion detection systems are one of the main methods to create security in the IoT. On the other hand, Convolutional Neural Network (CNN), with its specific features, is one of the best methods for analyzing network data. This network is a type of deep neural network composed of multiple layers that can ultimately reduce the dimensions of features. Additionally, the cuckoo algorithm has parameters required for configuration in the initial search, which are very few and can naturally and efficiently cope with multi-state problems. In this paper, a new method for intrusion detection in the IoT using CNN and feature selection by the cuckoo algorithm is presented. Simulation results indicate the satisfactory performance of the proposed method.

This paper aims to study the appropriate data mining method to extract the rules from a data set and examining the benefits of using the cuckoo algorithm to extract association rules and compare the execution time of the cuckoo algorithm and genetic algorithm (GA). Therefore, an algorithm is proposed that includes two parts: preprocessing and mining. The first part presents the procedures related to the calculation of cuckoo fit values and in the second part of the algorithm, which is the main achievement of this research. Support and confidence The best position can show the least confidence and support.These mining results can be used to continue mining the association rules. The proposed algorithm is based on the cuckoo search. It hides the sensitive relationship rules with a lower time cost and, at the same time, controls the peripheral effects of non-sensitive rules in a better way. This aim is achieved using recurring to the objective function. The GA is set to be the evaluation criterion to show the prominence of the proposed method. In this method, we compare the speed of the cuckoo algorithm with the genetic algorithm, which uses genetic evolution as a problem-solving model. In general, it is an algorithm based on repetition, most of its parts are selected as random processes, and these algorithms are part of the fitting function. It was chosen as a criterion and we paid .It is scientifically proven that the cuckoo algorithm outperforms the GA in the execution time.

Recommending systems are systems that, by taking limited information from the user and features such as what the user has searched for in the past and what product they have rated, can correctly identify the user and the desired items Offer the user. The user's desired items are suggested to him through the user profile. In this research, a new method is presented to recommend the user's interests in the form of the user's personalized profile. The way to do this is to use other users' searched information in the form of a database to recommend to new users. The procedure is that we first collect a log file from the items searched by users, then we pre-process this log file to remove the data from the raw state and clean it. Then, using data weighting and using the score function, we extract the most searched items of users in the past and provide them to the user in the form of a recommendation system based on participatory filtering. Finally, we use our data using an algorithm. We optimize the cuckoo that this information can be of interest to the user. The results of this study showed 99% accuracy and 97% frequency, which can to a large extent correctly predict the user's favorite items and pages and start with the problem that is the problem of most recommender systems To confront.

Software-Defined networks (SDNs) are a new generation of computer networks that have eliminated many of the problems of traditional networks. These networks use a three-tier architecture in which the physical layers, controller, and management are located at different levels. This new architecture has made the network very dynamic, and many of the previous problems in the network have been solved. As the size of the network increases, using a controller across the network will cause issues such as increasing the average latency between the switches and the controller, as well as forming a bottleneck in the controller. For this reason, it is recommended to use multiple physical controllers on the control plane. Due to the cost of purchasing and maintaining the controller, it is necessary to solve the mentioned problem with the least controllers. The question is, to achieve a goal such as reducing latency to an acceptable threshold, at least how many controllers are needed, where the controllers should be located, and which switches should be monitored by which controller? Since this is an NP-Hard problem, methods based on meta-heuristic algorithms can be effective in solving it. In this article, we have solved the problem of controller placement in software-based networks to reduce latency using the cuckoo meta-heuristic algorithm. The simulation results show that the efficiency of our proposed method is between 16 to 70 percent better than the method proposed by the PSO algorithm.

Due to the growing number of videos available on the web, it seems necessary to have a system that can extract users' favorite videos from a huge amount of information that is increasing day by day. One of the best ways to do this is to use referral systems. In this research, a method is provided to improve the recommender systems in the field of film recommendation to the user. In this research, DBSCAN clustering algorithm is used for data clustering. Then we will optimize our data using the cuckoo algorithm, then the genetic algorithm is used to predict the data, and finally, using a recommender system based on participatory refinement, a list of different movies that can be of interest to the user is provided. The results of evaluating the proposed method indicate that this recommender system obtained a score of 99% in the accuracy of the system and a score of 95% in the call section Suggest the user's favorite videos correctly to the user.

In this study, an efficient logistics network was designed to optimize both time and cost as the most effective factors using a mathematical model (two-objective fuzzy optimization) in a reverse logistics system. This paper attempted to determine value of goods sent between return products processing centers in any time period, in such a way to minimize total cost and time of delay within supply chain. The fuzzy approach was adopted in order to consider uncertainty in reverse logistics network. The validity of model was measured through a model proposed by Azar Resin Co and then implemented and solved by GAMS software. According to previous studies and implementation of model at smaller scale, the problem revolved around designing NP-hard logistics network. Hence, exact methods cannot solve these problems on large scale, for which Cuckoo algorithm was considered. In order to validate the newly proposed algorithm, results were compared against the exact solution. The results suggested that the proposed Cuckoo algorithm was sufficiently accurate to solve the problem and achieve values similar to exact solution.

This contribution deals with an optimal design of a brushless DC motor, using optimization algorithms, based on collective intelligence. For this purpose, the case study motor is perfectly explained and its significant specifications are obtained as functions of the motor geometric parameters. In fact, the geometric parameters of the motor are considered as optimization variables. Then, the objective function has been defined. This function consists of three terms i.e. losses, construction cost and the volume of the motor which should be minimized simultaneously. Three algorithms i.e. cuckoo, genetic and particle swarm have been studied in this paper. It is noteworthy that, cuckoo optimization algorithm has been used for the first time for brushless DC motor design optimization. A comparative study between the mentioned optimization approaches shows that, cuckoo optimization algorithm has been converged to optimal response in less than 250 iterations and its standard deviation is , while the convergence rate of the genetic and particle swarm algorithms are about 400 and 450 with standard deviations of and , respectively for the case study motor. The obtained results show the best performance for cuckoo optimization algorithm among all mentioned algorithms in brushless DC motor design optimization.

The Generators used in aerial industries should have the characteristics such as high efficiency, power density and reliability, low weight and volume. Among different generators, permanent magnet synchronous generator adequately satisfied these requirements. In this paper, first the dimensions and other quantities of this generator are calculated through an analytical model. Then using Cuckoo Optimization Algorithm (COA), these quantities are optimized to suit for desirable needs of aerospace systems, in order that the total volume of the generator could be minimized and its efficiency could be maximized. The results of this design for the permanent magnet synchronous generator application in aerospace systems have been satisfactory.