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Showing 5 results for Optimization
Mahdi Ahmadipari, Meysam Moradi,
Volume 4, Issue 1 (9-2015)
Abstract
In recent years, the use of Meta-heuristic algorithms on various problem taken into consideration. Meta-heuristic algorithms in solving various problem, different performance show. An Meta-heuristic algorithm to solve a particular problem may have better performance than other algorithms and poorer performance have in other issue. In this study the performance of Meta-heuristic algorithms for a specific problem that explore cryption key Vigenere encryption algorithm will be examined. And Meta-heuristic different algorithms performance in terms of accuracy and speed of convergence of the results will be cryptanalyzed and the best algorithm is selected
Mahtab Roozbahani, Meysam Moradi, Parvaneh Mansoori,
Volume 5, Issue 1 (9-2016)
Abstract
In mathematics and computer science an optimization problem, the problem is finding the best solution among all possible solutions. Given the importance of the knapsack in computer sciences, different algorithms are used to solve it. Knapsack problem is a combinational problem of selectivity and the purpose of solving the most benefit by taking the capacity is the tolerable knapsack. Since the knapsack is a problem of constrained maximization. In this study, a mathematical model in the form of a function unlimited minimization and designed for it, hen this model on Particle Swarm Optimization , Firefly Algorithm and Artificial Bee Colony has been implemented in MATLAB software environment, The results show that the artificial bee colony algorithm, the model is better than the other two algorithms .The advantage of this model is the objective function , because minimization and unlimited models , to implement with many Bio-Inspired algorithms.
Meysam Moradi, Mahdi Abbasi,
Volume 7, Issue 2 (3-2019)
Abstract
For many years, cryptanalysis has been considered as an attractive topic in jeopardizing the security and resistance of an encryption algorithm. The SDES encryption algorithm is a symmetric cryptography algorithm that performs a cryptographic operation using a crypt key. In the world of encryption, there are many search algorithms to cryptanalysis. In these researches, brute force attack algorithm has been used as a complete search algorithm, genetic algorithm as an evolutionary intelligence algorithm, and standard particle swarm as an optimization a swarm intelligence as algorithm. Along with these algorithms, a genetic algorithm has been also introduced by adjusting and designing the parameters and design algorithms has been introduced to discover of crypt key. There are attempts to evaluate the performance of different algorithms for cryptanalysis of the SDES encryption algorithm.
Atefeh Mortazavi, Dr Farhad Soleimanian Gharehchopogh,
Volume 8, Issue 1 (9-2019)
Abstract
Emails are one of the fastest economic communications. Increasing email users has caused the increase of spam in recent years. As we know, spam not only damages user’s profits, time-consuming and bandwidth, but also has become as a risk to efficiency, reliability, and security of a network. Spam developers are always trying to find ways to escape the existing filters, therefore new filters to detect spams need to be developed. Most of these filters take advantage of a combination of several methods, such as black or white lists, using keywords, rule-based filters, machine learning methods and so on, to identify spams more accurately. many approaches about email spam detection exhausted up to now. In this paper, we propose a new approach for spam detection based on Particle Swarm Optimization Algorithm and K-Nearest Neighbor optimization, and we measure performance based on Accuracy, Precision, Recall, And f-measure. The results show that the proposed approach has a better performance than other models and the basic algorithms.
Farhad Soleimanian Gharehchopogh, Mohammad Sakhidek Hovshin,
Volume 9, Issue 1 (8-2020)
Abstract
Unfortunately, among internet services, users are faced with several unwanted messages that are not even related to their interests and scope, and they contain advertising or even malicious content. Spam email contains a huge collection of infected and malicious advertising emails that harms data destroying and stealing personal information for malicious purposes. In most cases, spam emails contain malware that is usually sent to users in the form of scripts or attachments, and the user infects the computer with malware by downloading and executing the attached file. In this paper, a new model for detecting spam e-mail is proposed based on the hybrid of the Harmony Search Algorithm (HAS) with the Magnetic Optimization Algorithm (MOA). The proposed model is used to select the effective features and then the classification is performed using the K Nearest Neighbor's (KNN) algorithm. In the proposed model, using the MOA was found the best features for the HSA, and the harmony matrix is formed based on them. Then the HSA changes based on the update and rate of step-change in each step of the harmony vectors so that the best vector is selected as the vector of characteristics among them. The results show that the accuracy of the proposed model on the Spam base dataset with 200 iterations is 94.17% and also the accuracy of the diagnostic model of the proposed model is more than other models.
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