Comparison of physics-based optimization algorithms by using benchmark functions

Optimizasyon problemlerinin çözümü için kullanılan sezgisel optimizasyon algoritmalarının çoğu biyolojik evrim, fiziksel, sosyal ve kimyasal süreçlerden esinlenilerek geliştirilmiştir. Bu çalışmada incelenen Elektromanyetizma Benzeri Algoritma (Electromagnetismlike Algorithm, EM), Büyük Patlama-Büyük Çöküş Algoritması (Big Bang–Big Crunch Algorithm, BB-BC) ve Yerçekimsel Arama Algoritması (Gravitational Search Algorithm, GSA) fizik tabanlı sezgisel yöntemlerdendir. EM, BB-BC ve GSA’nın performansları, Rastrigin, Rosenbrock ve De Jong kalite testi fonksiyonları kullanılarak karşılaştırılmıştır

Web pages classification with parliamentary optimization algorithm

In recent years, data on the Internet has grown exponentially, attaining enormous dimensions. This situation makes it difficult to obtain useful information from such data. Web mining is the process of using data mining techniques such as association rules, classification, clustering, and statistics to discover and extract information from Web documents. Optimization algorithms play an important role in such techniques. In this work, the parliamentary optimization algorithm (POA), which is one of the latest social-based metaheuristic algorithms, has been adopted for Web page classification. Two different data sets (Course and Student) were selected for experimental evaluation, and HTML tags were used as features. The data sets were tested using different classification algorithms implemented in WEKA, and the results were compared with those of the POA. The POA was found to yield promising results compared to the other algorithms. This study is the first to propose the POA for effective Web page classification

Power Side Channel Analysis and Anomaly Detection of Modular Exponentiation Method in Digital Signature Algorithm Based Fpga

In this study, digital signature application was performed on FPGA with classical RSA and Chinese Remainder Theorem (CRT). The power consumption of the system was observed when the digital signature process was performed on the FPGA. In order to distinguish the modular exponentiation methods as the classical RSA and the Chinese Remainder Theorem (CRT), the anomaly detection method was applied to the digital signature application using the power side channel analysis of the system. According to the obtained result, it is proved that information about the structure of the algorithm executing in the system can be obtained by using the power information consumed by a cryptographic device

Melez elektromanyetizma benzeri-parçacık sürü optimizasyon algoritması

Optimizasyon, bir problemin alternatif çözümleri içinden en uygununu seçme işlemidir. Optimizasyon problemlerinin çözümü için, kabul edilebilir sürede optimuma yakın çözümler verebilen birçok sezgisel optimizasyon algoritması önerilmiştir. Literatürde çok başarılı sezgisel optimizasyon algoritmaları bulunsa da; tüm problemlerin çözümü için en optimum çözümü bulan algoritmalar henüz tasarlanmamıştır. Bu yüzden yeni sezgisel optimizasyon algoritmaları önerilmekte ya da var olanların daha etkili çalışması için öneriler sunulmaktadır. Bu çalışmada, global optimizasyon için, Elektromanyetizma Benzeri (EM) algoritma ile Parçacık Sürü Optimizasyon (PSO) algoritmasının birleşiminden oluşan yeni bir melez yöntem olan EM-PSO önerilmiştir. Önerilen yöntemde, PSO algoritmasının hız denklemindeki sabit katsayılar yerine EM algoritmasındaki yük ve toplam kuvvet değerleri kullanılmış ve EM algoritmasındaki parçacıkların hareketi bu denklem ile gerçekleştirilmiştir. Önerilen yöntemin performansı beş farklı kalite testi fonksiyonu kullanılarak test edilmiştir ve sonuçlar standart EM ve PSO algoritmalarının sonuçları ile karşılaştırılmıştır. Deneysel sonuçlar, önerilen yöntemin standart EM ve PSO algoritmalarına göre daha başarılı olduğunu göstermiştir

Power Side Channel Analysis and Anomaly Detection of Modular Exponentiation Method in Digital Signature Algorithm Based Fpga

In this study, digital signature application was performed on FPGA with classical RSA and Chinese Remainder Theorem (CRT). The power consumption of the system was observed when the digital signature process was performed on the FPGA. In order to distinguish the modular exponentiation methods as the classical RSA and the Chinese Remainder Theorem (CRT), the anomaly detection method was applied to the digital signature application using the power side channel analysis of the system. According to the obtained result, it is proved that information about the structure of the algorithm executing in the system can be obtained by using the power information consumed by a cryptographic device

Hybrid parliamentary optimization and big bang-big crunch algorithm for global optimization

Researchers have developed different metaheuristic algorithms to solve various optimization problems. The efficiency of a metaheuristic algorithm depends on the balance between exploration and exploitation. This paper presents the hybrid parliamentary optimization and big bang-big crunch (HPO-BBBC) algorithm, which is a combination of the parliamentary optimization algorithm (POA) and the big bang-big crunch (BB-BC) optimization algorithm. The intragroup competition phase of the POA is a process that searches for potential points in the search space, thereby providing an exploration mechanism. By contrast, the BB-BC algorithm has an effective exploitation mechanism. In the proposed method, steps of the BB-BC algorithm are added to the intragroup competition phase of the POA in order to improve the exploitation capabilities of the POA. Thus, the proposed method achieves a good balance between exploration and exploitation. The performance of the HPO-BBBC algorithm was tested using well-known mathematical test functions and compared with that of the POA, the BB-BC algorithm, and some other metaheuristics, namely the genetic algorithm, multiverse optimizer, crow search algorithm, dragonfly algorithm, and moth-flame optimization algorithm. The HPO-BBBC algorithm was found to achieve better optimization performance and a higher convergence speed than the above-mentioned algorithms on most benchmark problems