Software Engineering and Information Systems
Mohammad Reza Hassanzadeh; farshid keynia
Volume 7, Issue 1 , February 2021, , Pages 35-54
Abstract
Metaheuristic algorithms are typically population-based random search techniques. The general framework of a metaheuristic algorithm consisting of its main parts. The sections of a metaheuristic algorithm include setting algorithm parameters, population initialization, global search section, local search ...
Read More
Metaheuristic algorithms are typically population-based random search techniques. The general framework of a metaheuristic algorithm consisting of its main parts. The sections of a metaheuristic algorithm include setting algorithm parameters, population initialization, global search section, local search section, and checking the stopping conditions in a metaheuristic algorithm. In the parameters setting section, the user can monitor the performance of the metaheuristic algorithm and improve its performance according to the problem under consideration. In this study, an overview of the concepts, classifications, and different methods of population initialization in metaheuristic algorithms discussed in recent literature will be provided. Population initialization is a basic and common step between all metaheuristic algorithms. Therefore, in this study, an attempt has been made that the performance, methods, mechanisms, and categories of population initialization in metaheuristic algorithms. Also, the relationship between population initialization and other important parameters in performance and efficiency of metaheuristic algorithms such as search space size, population size, the maximum number of iteration, etc., which are mentioned and considered in the literature, are collected and presented in a regular format.
Computer Networks and Distributed Systems
Alireza Hedayati; Mehrin Rouhifar; Sahar Bahramzadeh; Vaheh Aghazarian; Mostafa Chahardoli
Volume 4, Issue 2 , May 2018, , Pages 101-134
Abstract
Internet of Things (IoT) is a novel and emerging paradigm to connect real/physical and virtual/logical world together. So, it will be necessary to apply other related scientific concepts in order to achieve this goal. The main focus of this paper is to identify the research topics in IoT. For this purpose, ...
Read More
Internet of Things (IoT) is a novel and emerging paradigm to connect real/physical and virtual/logical world together. So, it will be necessary to apply other related scientific concepts in order to achieve this goal. The main focus of this paper is to identify the research topics in IoT. For this purpose, a comprehensive study has been conducted on the vast range of research articles. IoT concepts and issues are classified into some research domains and sub-domains based on the analysis of reviewed papers that have been published in 2015 & 2016. Then, these domains and sub-domains have been discussed as well as it is reported their statistical results. The obtained results of analysis show the most of the IoT research works are concentrated on technology and software services domains similarly at first rank, communication at second rank and trust management at third rank with 19%, 14% and 13% respectively. Also, a more accurate analysis indicates the most important and challenging sub-domains of mentioned domains which are: WSN, cloud computing, smart applications, M2M communication and security. Accordingly, this study will offer a useful and applicable broad viewpoint for researchers. In fact, our study indicates the current trends of IoT area.
Pattern Analysis and Intelligent Systems
Mozhgan Rahimirad; Mohammad Mosleh; Amir Masoud Rahmani
Volume 1, Issue 2 , May 2015, , Pages 1-8
Abstract
With the explosive growth in amount of information, it is highly required to utilize tools and methods in order to search, filter and manage resources. One of the major problems in text classification relates to the high dimensional feature spaces. Therefore, the main goal of text classification is to ...
Read More
With the explosive growth in amount of information, it is highly required to utilize tools and methods in order to search, filter and manage resources. One of the major problems in text classification relates to the high dimensional feature spaces. Therefore, the main goal of text classification is to reduce the dimensionality of features space. There are many feature selection methods. However, only a few methods are utilized for huge text classification problems. In this paper, we propose a new wrapper method based on Particle Swarm Optimization (PSO) algorithm and Support Vector Machine (SVM). We combine it with Learning Automata in order to make it more efficient. This helps to select better features using the reward and penalty system of automata. To evaluate the efficiency of the proposed method, we compare it with a method which selects features based on Genetic Algorithm over the Reuters-21578 dataset. The simulation results show that our proposed algorithm works more efficiently.