ant colony algorithm
در نشریات گروه فناوری اطلاعات-
Nowadays, with the advancement of database information technology, databases has led to large-scale distributed databases. According to this study, database management systems are improved and optimized so that they provide responses to customer questions with lower cost. Query processing in database management systems is one of the important topics that grabs attentions. Until now, many techniques have been implemented for query processing in database system. The purpose of these methods is to optimize query processing in the database. The main topics that is interested in query processing in the database makes run-time adjustments of processing or summarizing topics by using the new approaches. The aim of this research is to optimize processing in the database by using adaptive methods. Ant Colony Algorithm (ACO) is used for solving optimization problems. ACO relies on the created pheromone to select the optimal solution. In this article, in order to make adaptive hybrid query processing. The proposed algorithm is fundamentally divided into three parts: separator, replacement policy, and query similarity detector. In order to improve the optimization and frequent adaption and correct selection in queries, the Ant Colony Algorithm has been applied in this research. In this algorithm, based on Versatility (adaptability) scheduling, Queries sent to the database have been attempted be collected. The simulation results of this method demonstrate that reduce spending time in the database. According to the proposed algorithm, one of the advantages of this method is to identify frequent queries in high traffic times and minimize the time and the execution time. This optimization method reduces the system load during high traffic load times for adaptive query Processing and generally reduces the execution runtime and aiming to minimize cost. The rate of reduction of query cost in the database with this method is 2.7%. Due to the versatility of high-cost queries, this improvement is manifested in high traffic times. In the future Studies, by adapting new system development methods, distributed databases can be optimized.
Keywords: Database, Ant Colony Algorithm, Query Processing, Versatility, Optimization -
یکی از مسایل مهم در شبکه های کامپیوتری پویا از قبیل شبکه های اینترنت اشیاء که در آن هزینه اتصالات به طور پی درپی تغییر می کند، ایجاد توازن بار ترافیکی و افزایش سرعت انتقال بسته ها در شبکه است. بطوری که بسته های داده از مسیرهایی با حداقل تراکم به مقصد برسند؛ درنتیجه یکی از روش های اصلی برای حل مسایل مسیریابی و توازن بار استفاده از الگوریتم های مبتنی بر مورچه است.با استفاده از روشی جدید مبتنی بر بهینه سازی کلونی مورچه چندگانه ، هدف این پژوهش ارایه یک الگوریتم مسیریابی مناسب در جهت کوتاه کردن و بهبود بخشیدن مسیر با توجه به پارامترهای تاخیر انتها به انتها ، نرخ اتلاف بسته ،پهنای باند و نرخ مصرف انرژی است تا داده ی حس شده در سیستم های اینترنت اشیاء به مقصد برسد. این روش در نرم افزار متلب پیاده سازی شده است . نتایج حاصل از آزمایش ها، بهبود در پارامترهای مذکور را نشان میدهد.
کلید واژگان: مسیریابی، توازن بار، اینترنت اشیا و الگوریتم کلونی مورچه چندگانهAn important issue in dynamic computer networks such as Internet networks, where the cost of connections varies continuously, is to create a traffic load balancing and increase the transmission speed of packets in the network, so that data packets are using paths with minimal congestion, as a result, one of the main approaches to solve routing problems and load balancing algorithms is based on ant - based algorithms using a novel approach based on optimization of multiple ant colony optimization, the purpose of this research is to present an appropriate routing algorithm in order to shorten and improve the path due to end - to - end delay parameters, packet loss rate, bandwidth and energy consumption rate, to reach a sense of data on the Internet systems. this method has been implemented in MATLAB software and shows the results of the improvement experiments in the mentioned parameters.
Keywords: Routing, load balancing, Internet of things, ant colony algorithm -
Ant colony optimization (ACOR) is a meta-heuristic algorithm for solving continuous optimization problems (MOPs). In the last decades, some improved versions of ACOR have been proposed. The UACOR is a unified version of ACOR that is designed for continuous domains. By adjusting some specified components of the UACOR, some new versions of ACOR can be deduced. By doing that, it becomes more practical for different types of MOPs. Based on the nature of meta-heuristic algorithms, the performance of meta-heuristic algorithms are depends on the exploitation and exploration, which are known as the two useful factors to generate solutions with different qualities. Since all the meta-heuristic algorithms with random parameters use the probability functions to generate the random numbers and as a result, there is no any control over the amount of diversity; hence in this paper, by using the best parameters of UACOR and making some other changes, we propose a new version of ACOR to increase the efficiency of UACOR. These changes include using chaotic sequences to generate various random sequences and also using a new local search to increase the quality of the solution. The proposed algorithm, the two standard versions of UACOR and the genetic algorithm are tested on the CEC05 benchmark functions, and then numerical results are reported. Furthermore, we apply these four algorithms to solve the utilization of complex multi-reservoir systems, the three-reservoir system of Karkheh dam, as a case study. The numerical results confirm the superiority of proposed algorithm over the three other algorithms.
Keywords: Ant colony algorithm, Continuous optimization, Chaotic sequences, Multi-reservoir systems, Genetic algorithm -
Online communities are the most popular interactive environments on the Internet, which provide users with a platform to share their knowledge and expertise. The most important use of online communities in cyberspace is sharing knowledge. These communities are a great place to ask questions and find answers. The important challenges of these communities are the large volume of information and the lack of a method to determine their validity as well as expert finding which attracted a lot of attention in both industry and academia in. Therefore, identifying persons with relevant knowledge on a given topic and ranking them according to their expertise score can help to calculate the accuracy of the comments submitted on the internet. In this research, a model for finding experts and determining their domain expertise level by the aid of statistical calculations and the ant colony algorithm in the MetaFilter online community was presented. The WordNet Dictionary was used to determine the relevance of the user’s questions with the intended domain. The proposed algorithm determines the level of people’s expertise in the intended field by using the pheromone section of the Ant colony algorithm, which is based on the similarity of the questions sent by the users and the shared knowledge of the users from their interactions in the online community
Keywords: Online Communities, Experts Finding, Ant Colony Algorithm, Word Net -
This paper addresses the eye gaze estimation problem in low-resolution images, using the low-cost camera in order to eliminate problems caused by infrared high-resolution imaging such as needing an expensive camera, complex setup, special light sources, and being limited in lab research environments. In the proposed method, the human face is detected with Ant Colony Optimization (ACO) algorithm, and then the Kirsch compass mask is utilized to detect the position of humans’ eyes. For iris detection, a novel strategy based on ACO algorithm, which has been rarely used before, is applied. The pupil is recognized by morphological processing. Finally, the extracted features, obtained from the radius and position of the irises of the pupils, are given to the Support Vector Machine (SVM) classifier to detect the gaze pointing. In order to receive assurance of the reliability and superiority of the newly designed ACO algorithm, some other metaheuristic algorithms such as (GA, PSO, and BBO) are implemented and evaluated. Additionally, a novel dataset, comprising 700 images gazing at seven different major orientations, is created in this research. The extensive experiments are performed on three various datasets, including Eye-Chimera with 92.55% accuracy, BIOID dataset with 96% accuracy, and the newly constructed dataset with 90.71% accuracy. The suggested method outperformed the state of the art gaze estimation methods in terms of the robustness and accuracy.Keywords: Eye-Gaze Estimation, Ant Colony Algorithm, Low-Resolution Image, Kirsch Filter, 2D image
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