List of Articles Learning Automata

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  1 - Node Classification in Social Network by Distributed Learning Automata
  Ahmad Rahnama Zadeh meybodi meybodi Masoud Taheri Kadkhoda
  10.7508/jist.2017.18.005
  The aim of this article is improving the accuracy of node classification in social network using Distributed Learning Automata (DLA). In the proposed algorithm using a local similarity measure, new relations between nodes are created, then the supposed graph is partitio More

  The aim of this article is improving the accuracy of node classification in social network using Distributed Learning Automata (DLA). In the proposed algorithm using a local similarity measure, new relations between nodes are created, then the supposed graph is partitioned according to the labeled nodes and a network of Distributed Learning Automata is corresponded on each partition. In each partition the maximal spanning tree is determined using DLA. Finally nodes are labeled according to the rewards of DLA. We have tested this algorithm on three real social network datasets, and results show that the expected accuracy of presented algorithm is achieved. Manuscript profile
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  2 - The Surfer Model with a Hybrid Approach to Ranking the Web Pages
  Javad Paksima Homa  Khajeh
  10.7508/jist.2016.03.008
  Users who seek results pertaining to their queries are at the first place. To meet users’ needs, thousands of webpages must be ranked. This requires an efficient algorithm to place the relevant webpages at first ranks. Regarding information retrieval, it is highly impor More

  Users who seek results pertaining to their queries are at the first place. To meet users’ needs, thousands of webpages must be ranked. This requires an efficient algorithm to place the relevant webpages at first ranks. Regarding information retrieval, it is highly important to design a ranking algorithm to provide the results pertaining to user’s query due to the great deal of information on the World Wide Web. In this paper, a ranking method is proposed with a hybrid approach, which considers the content and connections of pages. The proposed model is a smart surfer that passes or hops from the current page to one of the externally linked pages with respect to their content. A probability, which is obtained using the learning automata along with content and links to pages, is used to select a webpage to hop. For a transition to another page, the content of pages linked to it are used. As the surfer moves about the pages, the PageRank score of a page is recursively calculated. Two standard datasets named TD2003 and TD2004 were used to evaluate and investigate the proposed method. They are the subsets of dataset LETOR3. The results indicated the superior performance of the proposed approach over other methods introduced in this area. Manuscript profile
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  3 - Cluster-based Coverage Scheme for Wireless Sensor Networks using Learning Automata
  Ali Ghaffari Seyyed Keyvan  Mousavi
  10.52547/jist.9.35.197
  Network coverage is one of the most important challenges in wireless sensor networks (WSNs). In a WSN, each sensor node has a sensing area coverage based on its sensing range. In most applications, sensor nodes are randomly deployed in the environment which causes the d More

  Network coverage is one of the most important challenges in wireless sensor networks (WSNs). In a WSN, each sensor node has a sensing area coverage based on its sensing range. In most applications, sensor nodes are randomly deployed in the environment which causes the density of nodes become high in some areas and low in some other. In this case, some areas are not covered by none of sensor nodes which these areas are called coverage holes. Also, creating areas with high density leads to redundant overlapping and as a result the network lifetime decreases. In this paper, a cluster-based scheme for the coverage problem of WSNs using learning automata is proposed. In the proposed scheme, each node creates the action and probability vectors of learning automata for itself and its neighbors, then determines the status of itself and all its neighbors and finally sends them to the cluster head (CH). Afterward, each CH starts to reward or penalize the vectors and sends the results to the sender for updating purposes. Thereafter, among the sent vectors, the CH node selects the best action vector and broadcasts it in the form of a message inside the cluster. Finally, each member changes its status in accordance with the vector included in the received message from the corresponding CH and the active sensor nodes perform environment monitoring operations. The simulation results show that the proposed scheme improves the network coverage and the energy consumption. Manuscript profile
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  4 - A Method Based on Learning Automata for Adaptation of the Vigilance factor in Fuzzy ARTMAP Network
  M. Anjidani M. R. Meybodi
  In this paper, a method based on learning automata for adaptation of the vigilance factor in Fuzzy ARTMAP network when used for classification problems is proposed. The performance of the proposed algorithm is independent of the initial value for vigilance factor. Fuzz More

  In this paper, a method based on learning automata for adaptation of the vigilance factor in Fuzzy ARTMAP network when used for classification problems is proposed. The performance of the proposed algorithm is independent of the initial value for vigilance factor. Fuzzy ARTMAP network in which the vigilance factor adapted using learning automata generates smaller structure with higher recognition rate. To study the performance of the proposed method it has been applied to several problems: circle in square, spirals and square in square problems. The results of experiments show the effectiveness of the proposed method. Manuscript profile
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  5 - Select the Optimal Subset of LABP Features Based on CLA-EC Method in Face Recognition System
  A. Hazrati Bishak K. Faez H. Barghi Jond S. Ghatei
  In this paper, we present a new efficient method based on local binary pattern descriptor, for face recognition. Because, the calculations in Local binary pattern are done between two pixels values, so, small changes in the binary pattern affect its performance. In this More

  In this paper, we present a new efficient method based on local binary pattern descriptor, for face recognition. Because, the calculations in Local binary pattern are done between two pixels values, so, small changes in the binary pattern affect its performance. In this paper, a new local average binary pattern descriptor is presented based on cellular learning automata and evolutionary computation (CLA-EC). In the proposed method, first, the LABP operator are used to extract uniform local binary patterns from face images; it should be noted that, in LABP operator to obtain more robust feature representation, many sample points has been used. Then, the best subset of patterns found by CLA-EC methods, and the histogram of these patterns is obtained. Finally, support vector machine is used for classification. The results of experiment on FERET data base show the advantage of the proposed algorithm compared to other algorithms. Manuscript profile
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  6 - A New eDLA-Based Framework for Finding Optimal Stochastic Sub-Graph
  M. R. Mollakhalili Meybodi M. R. Meybodi
  In this paper a new structure of learning automata which is called as extended distributed learning automata (eDLA) is introduced. A new eDLA-based iterative sampling method for finding optimal sub-graph in stochastic graphs is proposed. Some mathematical analysis of th More

  In this paper a new structure of learning automata which is called as extended distributed learning automata (eDLA) is introduced. A new eDLA-based iterative sampling method for finding optimal sub-graph in stochastic graphs is proposed. Some mathematical analysis of the proposed algorithm is presented and the convergence property of the algorithm is studied. Our study shows that the proposed algorithm can be converge to the optimal sub-graph. Manuscript profile
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  7 - A Transfer Learning Algorithm to Improve the Convergence Rate and Accuracy in Cellular Learning Automata
  Seyyed Amir Hadi Minoofam Azam Bastanfard M. R.  Keyvanpour
  Cellular learning automaton is an intelligent model as a composition of cellular automaton and learning automaton. In this study, an extended algorithm of cellular learning automata is proposed based on transfer learning as the TL-CLA algorithm. In this algorithm, trans More

  Cellular learning automaton is an intelligent model as a composition of cellular automaton and learning automaton. In this study, an extended algorithm of cellular learning automata is proposed based on transfer learning as the TL-CLA algorithm. In this algorithm, transfer learning is used as an approach for computation deduction and minimizing the learning cycle. The proposed algorithm is an extended model based on merit function and attitude vector for transfer learning. In the TL-CLA algorithm, the value of the merit function is computed based on the local environment, and the value of the attitude vector is calculated based on the global environment. When these two measures get the threshold values, the transfer of action probabilities causes the transfer learning from the source CLA to the destination CLA. The experimental results show that the proposed TL-CLA model leads to increment the convergence accuracy as 2.7% and 2.2% in two actions and multi-action standard environments, respectively. The improvements in convergence rate are also 8% and 2% in these two environments. The TL-CLA could be applied in knowledge transfer from learning one task to learning another similar task Manuscript profile
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  8 - A New Algorithm Based on Distributed Learning Automata for Solving Stochastic Linear Optimization Problems on the Group of Permutations
  mohammadreza mollakhalili meybodi masoumeh zojaji
  In the present research, a type of permutation optimization was introduced. It is assumed that the cost function has an unknown probability distribution function. Since the solution space is inherently large, solving the problem of finding the optimal permutation is com More

  In the present research, a type of permutation optimization was introduced. It is assumed that the cost function has an unknown probability distribution function. Since the solution space is inherently large, solving the problem of finding the optimal permutation is complex and this assumption increases the complexity. In the present study, an algorithm based on distributed learning automata was presented to solve the problem by searching in the permutation answer space and sampling random values. In the present research, in addition to the mathematical analysis of the behavior of the proposed new algorithm, it was shown that by choosing the appropriate values of the parameters of the learning algorithm, this new method can find the optimal solution with a probability close to 100% and by targeting the search using the distributed learning algorithms. The result of adopting this policy is to decrease the number of samplings in the new method compared to methods based on standard sampling. In the following, the problem of finding the minimum spanning tree in the stochastic graph was evaluated as a random permutation optimization problem and the proposed solution based on learning automata was used to solve it. Manuscript profile
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  9 - TPALA: Two Phase Adaptive Algorithm based on Learning Automata for job scheduling in cloud Environment
  Abolfazl Esfandi Javad Akbari Torkestani Abbas Karimi Faraneh Zarafshan
  10.61186/jist.39109.12.45.29
  Due to the completely random and dynamic nature of the cloud environment, as well as the high volume of jobs, one of the significant challenges in this environment is proper online job scheduling. Most of the algorithms are presented based on heuristic and meta-heuristi More

  Due to the completely random and dynamic nature of the cloud environment, as well as the high volume of jobs, one of the significant challenges in this environment is proper online job scheduling. Most of the algorithms are presented based on heuristic and meta-heuristic approaches, which result in their inability to adapt to the dynamic nature of resources and cloud conditions. In this paper, we present a distributed online algorithm with the use of two different learning automata for each scheduler to schedule the jobs optimally. In this algorithm, the placed workload on every virtual machine is proportional to its computational capacity and changes with time based on the cloud and submitted job conditions. In proposed algorithm, two separate phases and two different LA are used to schedule jobs and allocate each job to the appropriate VM, so that a two phase adaptive algorithm based on LA is presented called TPALA. To demonstrate the effectiveness of our method, several scenarios have been simulated by CloudSim, in which several main metrics such as makespan, success rate, average waiting time, and degree of imbalance will be checked plus their comparison with other existing algorithms. The results show that TPALA performs at least 4.5% better than the closest measured algorithm. Manuscript profile