دانلود مقاله ISI انگلیسی شماره 92990
ترجمه فارسی عنوان مقاله

یک مسیر قابل اعتماد بین کاربران هدف و مشتریان در شبکه های اجتماعی با استفاده از الگوریتم بهینه سازی کلون مورچه معکوس

عنوان انگلیسی
A reliable path between target users and clients in social networks using an inverted ant colony optimization algorithm
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
92990 2017 10 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Karbala International Journal of Modern Science, Volume 3, Issue 3, July 2017, Pages 143-152

ترجمه کلمات کلیدی
شبکه های اجتماعی، اعتماد، الگوریتم بهینه سازی حلزون مورچه معکوس، تعادل بار، زمان انتظار،
کلمات کلیدی انگلیسی
Social networks; Trust; Inverted ant colony optimization algorithm; Load balancing; Waiting time;
پیش نمایش مقاله
پیش نمایش مقاله  یک مسیر قابل اعتماد بین کاربران هدف و مشتریان در شبکه های اجتماعی با استفاده از الگوریتم بهینه سازی کلون مورچه معکوس

چکیده انگلیسی

Internet has become an integral section of human life. Millions of people are joining online social networks every day, interacting with others whom they did not know already. Establishing trust among those indirectly connected users performs a crucial role in improving the quality of social network services and creating the security for them. Nowadays, there are many paths between clients (service requesters) and target users (service providers) in online social networks. Among existing paths finding a trust path for trustworthy services is a vital job. Also, in many previous methods, such as ant colony optimization algorithm (ACO) load balancing among target users is inefficient. Therefore, in this paper we propose an inverted ant colony optimization algorithm to find a reliable path along with improving load balancing among the target users. The inverted ant colony optimization algorithm is a diversity of the basic ant colony optimization algorithm in which, the updated pheromone has a reverse effect on the selected path by the ants. Finally, we simulate the proposed method by using the original experimental dataset and evaluate the proposed method in terms of load balancing, waiting time and execution time in comparison with the ant colony optimization algorithm. The obtained results are very promising.