فهرس المقالات Distributed Learning Automata

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  1 - Connectivity Restoration in Wireless Sensor and Actor Networks using Distributed Learning Automata
  M. Jahanshahi M. Maddah
  Connectivity in WSNs is vital to preserve the network performance. Reported algorithms try to restore connectivity by appropriately selecting the failure handler. This paper, initially presents a hybrid algorithm based on distributed learning automata named DLA-BuS for أکثر

  Connectivity in WSNs is vital to preserve the network performance. Reported algorithms try to restore connectivity by appropriately selecting the failure handler. This paper, initially presents a hybrid algorithm based on distributed learning automata named DLA-BuS for critical node backup selection. Then, we present DLA-MRF to repair stimulant failure of two adjacent actors. Simulations using Castalia demonstrate that proposed algorithms outperform representative methods in terms of some well-known performance parameters. تفاصيل المقالة
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  2 - LA-Based Approaches to Infer Urban Structure from Traffic Dynamics Considering Costs
  Hamid Yasinian Mansour Esmaeilpour
  10.22094/jcr.2022.1972425.1285
  Successful future urban planning is highly dependent on optimal connectivity between important areas of cities. Discovering essential latent links will optimize the urban structure. Moving towards a better structure requires some information. There are a lot of sources أکثر

  Successful future urban planning is highly dependent on optimal connectivity between important areas of cities. Discovering essential latent links will optimize the urban structure. Moving towards a better structure requires some information. There are a lot of sources of information for urban structure inferring, including the current structure, the time-varying traffic dynamics, and the construction costs, which are the basics of the optimization problem formulation. This paper presents a new formulation for the problem. The model problem to be solved tries to utilize all data sources needed for inferring. There are some methods for solving the formulated problem. The methods need some development to apply to the model. Methods utilizing learning automata (LA) are very favorable in this field due to the interaction with the environment. This paper presents two LA-based approaches for the model: Distributed Learning Automata (DLA) and Cellular Learning Automata (CLA). The algorithms result in an optimal connectivity matrix considering urban structure, traffic dynamics, and costs, where the matrix must include the current urban structure and some new reasonable necessary links. Moreover, comparisons are possible because the model has a fitness value for evaluating the provided connectivity matrix. The CLA-based proposed method performed better than the others in most experiments. تفاصيل المقالة
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  3 - A Link Prediction Method Based on Learning Automata in Social Networks
  Sara YounessZadeh Mohammad Reza Meybodi
  Nowadays, online social networks are considered as one of the most important emerging phenomena of human societies. In these networks, prediction of link by relying on the knowledge existing of the interaction between network actors provides an estimation of the probabi أکثر

  Nowadays, online social networks are considered as one of the most important emerging phenomena of human societies. In these networks, prediction of link by relying on the knowledge existing of the interaction between network actors provides an estimation of the probability of creation of a new relationship in future. A wide range of applications can be found for link prediction such as electronic commerce and recommender systems or identification of terroristic relations in social networks. In this article, a new idea is presented for the prediction. It is an integration of the two methods of prediction of similarity score based link and prediction of probabilistic link, which is placed in a new category of link prediction methods. This idea acquires the similarity score between nodes from probabilistic techniques and through using learning automata, and provides better results compared to other criteria methods on standard datasets. تفاصيل المقالة