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Open Access Article
1 - A Learning Automata Approach to Cooperative Particle Swarm Optimizer
Mohammad Hasanzadeh meybodi meybodi mohamad mehdi ebadzadeThis paper presents a modification of Particle Swarm Optimization (PSO) technique based on cooperative behavior of swarms and learning ability of an automaton. The approach is called Cooperative Particle Swarm Optimization based on Learning Automata (CPSOLA). The CPSOLA MoreThis paper presents a modification of Particle Swarm Optimization (PSO) technique based on cooperative behavior of swarms and learning ability of an automaton. The approach is called Cooperative Particle Swarm Optimization based on Learning Automata (CPSOLA). The CPSOLA algorithm utilizes three layers of cooperation which are intra swarm, inter swarm and inter population. There are two active populations in CPSOLA. In the primary population, the particles are placed in all swarms and each swarm consists of multiple dimensions of search space. Also there is a secondary population in CPSOLA which is used the conventional PSO's evolution schema. In the upper layer of cooperation, the embedded Learning Automaton (LA) is responsible for deciding whether to cooperate between these two populations or not. Experiments are organized on five benchmark functions and results show notable performance and robustness of CPSOLA, cooperative behavior of swarms and successful adaptive control of populations. Manuscript profile -
Open Access Article
2 - Node Classification in Social Network by Distributed Learning Automata
Ahmad Rahnama Zadeh meybodi meybodi Masoud Taheri KadkhodaThe 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 MoreThe 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 -
Open Access Article
3 - The Surfer Model with a Hybrid Approach to Ranking the Web Pages
Javad Paksima Homa KhajehUsers 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 MoreUsers 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 -
Open Access Article
4 - Eye Gaze Detection Based on Learning Automata by Using SURF Descriptor
Hassan Farsi Reza Nasiripour Sajad MohammadzadehIn the last decade, eye gaze detection system is one of the most important areas in image processing and computer vision. The performance of eye gaze detection system depends on iris detection and recognition (IR). Iris recognition is very important role for person iden MoreIn the last decade, eye gaze detection system is one of the most important areas in image processing and computer vision. The performance of eye gaze detection system depends on iris detection and recognition (IR). Iris recognition is very important role for person identification. The aim of this paper is to achieve higher recognition rate compared to learning automata based methods. Usually, iris retrieval based systems consist of several parts as follows: pre-processing, iris detection, normalization, feature extraction and classification which are captured from eye region. In this paper, a new method without normalization step is proposed. Meanwhile, Speeded up Robust Features (SURF) descriptor is used to extract features of iris images. The descriptor of each iris image creates a vector with 64 dimensions. For classification step, learning automata classifier is applied. The proposed method is tested on three known iris databases; UBIRIS, MMU and UPOL database. The proposed method results in recognition rate of 100% for UBIRIS and UPOL databases and 99.86% for MMU iris database. Also, EER rate of the proposed method for UBIRIS, UPOL and MMU iris database are 0.00%, 0.00% and 0.008%, respectively. Experimental results show that the proposed learning automata classifier results in minimum classification error, and improves precision and computation time. Manuscript profile -
Open Access Article
5 - Cluster-based Coverage Scheme for Wireless Sensor Networks using Learning Automata
Ali Ghaffari Seyyed Keyvan MousaviNetwork 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 MoreNetwork 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 -
Open Access Article
6 - Cooperation in Multi-Agent Systems Using Learning Automata
M. R. »hojasteh M. R. MeybodiAgents are software entities that act continuously and autonomously in a special environment. It is very essential for the agents to have the ability to learn how to act in the special environment for which they are designed to act in, to show reflexes to their environm MoreAgents are software entities that act continuously and autonomously in a special environment. It is very essential for the agents to have the ability to learn how to act in the special environment for which they are designed to act in, to show reflexes to their environment actions, to choose their way and pursue it autonomously, and to be able to adapt and learn. In multi-agent systems, many intelligent agents that can interact with each other, cooperate to achieve a set of goals. Because of the inherent complexity that exists in dynamic and changeable multi-agent environments, there is always a need to machine learning in such environments. As a model for learning, learning automata act in a stochastic environment and are able to update their action probabilities considering the inputs from their environment, so optimizing their functionality as a result. Learning automata are abstract models that can perform some numbers of actions. Each selected action is evaluated by a stochastic environment and a response is given back to the automata. Learning automata use this response to choose its next action. In this paper, the goal is to investigate and evaluate the application of learning automata to cooperation in multi-agent systems, using soccer server simulation as a test-bed. Because of the large state space of a complex multi-agent domains, it is vital to have a method for environmental states’ generalization. An appropriate selection of such a method can have a great role in determining agent states and actions. In this paper we have also introduced and designed a new technique called “The best corner in state square” for generalizing the vast number of states in the environment to a few number of states by building a virtual grid in agent’s domain environment. The efficiency of this technique in a cooperative multi-agent domain is investigated Manuscript profile -
Open Access Article
7 - A New Evolutionary Estimation of Distribution Algorithm Based on Learning Automata
M. R. MeybodiIn order to overcome the poor behaviors of genetic algorithms in some problems other classes of evolutionary algorithms have been recently developed by researchers. Although these algorithms do not have the simplicity of classic genetic algorithms but they are superior MoreIn order to overcome the poor behaviors of genetic algorithms in some problems other classes of evolutionary algorithms have been recently developed by researchers. Although these algorithms do not have the simplicity of classic genetic algorithms but they are superior to genetic algorithms. The Probabilistic Model Building Genetic Algorithms or Estimation of Distribution Algorithms (EDAs) is one of these classes which is recently developed. In this paper we introduce a new estimation of distribution algorithm based on Learning Automata. The proposed algorithm is a model based search optimization method that uses a set of learning automata as a probabilistic model of the population of solutions in the search space. The proposed algorithm is a simple algorithm which has produced good results for the optimization problems considered in this problem. Manuscript profile -
Open Access Article
8 - A Method Based on Learning Automata for Adaptation of the Vigilance factor in Fuzzy ARTMAP Network
M. Anjidani M. R. MeybodiIn 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 MoreIn 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 -
Open Access Article
9 - A Model Based on Entropy and Learning Automata for Solving Stochastic Games
B. Masoumi M. R. MeybodiStochastic games, as the generalization of Markov decision processes to the multi agent case, have long been used for modeling multi-agent system and are used as a suitable framework for Multi Agent Reinforcement Learning. Learning Automata (LA) were recently shown to b MoreStochastic games, as the generalization of Markov decision processes to the multi agent case, have long been used for modeling multi-agent system and are used as a suitable framework for Multi Agent Reinforcement Learning. Learning Automata (LA) were recently shown to be valuable tools for designing Multi-Agent Reinforcement Learning algorithms. In this paper a model based on learning automata and the concept of entropy for finding optimal policies in stochastic games is proposed. In the proposed model, for each state in the environment of the game and for each agent an S-model variable structure learning automaton is placed that tries to learn the optimal action probabilities in those states. The number of its adjacent states determines the number of actions of each learning automaton in each state and every joint action corresponds to a transition to an adjacent state. Entropy of the probability vector for the learning automaton of the next state is used to help learning process and improve the learning performance and is used a quantitative problem independent measurement for learning progress. We have also implemented a new version of the proposed algorithm that balances exploration with exploitation yielding improved performance. The experimental results show that the proposed algorithm has better learning performance than the other learning algorithms in terms of cost and the speed of reaching the optimal policy. Manuscript profile -
Open Access Article
10 - A New Statistical Characteristics Based Method for Adaptive Learning Rate Adjustment in Learning Automata
M. R. Mollakhalili Meybodi M. R. MeybodiThe value of learning rate and its change mechanisms is one of the issues in designing learning systems such as learning automata. In most cases a time-based reduction function is used to adjust the learning rate aim at reaching stability in training system. So the lear MoreThe value of learning rate and its change mechanisms is one of the issues in designing learning systems such as learning automata. In most cases a time-based reduction function is used to adjust the learning rate aim at reaching stability in training system. So the learning rate is a parameter that determines to what extent a learning system is based on past experiences, and the impact of current events on it. This method is efficient but does not properly function in dynamic and non-stationary environments. In this paper, a new method for adaptive learning rate adjustment in learning automata is proposed. In this method, in addition to the length of time to learn, some statistical characteristics of actions probability vector of Learning Automata are used to determine the increase or decrease of learning rate. Furthermore, unlike existing methods, during the process of learning, both increase and decrease of the learning rate is done and Learning Automata responds effectively to changes in the dynamic random environment. Empirical studies show that the proposed method has more flexibility in compatibility to the non-stationary dynamic environments and get out of local maximum points and the learned values are closer to the true values. Manuscript profile -
Open Access Article
11 - 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. GhateiIn 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 MoreIn 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 -
Open Access Article
12 - A New eDLA-Based Framework for Finding Optimal Stochastic Sub-Graph
M. R. Mollakhalili Meybodi M. R. MeybodiIn 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 MoreIn 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 -
Open Access Article
13 - A New Extended Distributed Learning Automata-Based Algorithm for Parameter Learning of a Bayesian Network
M. R. Mollakhalili Meybodi M. R. MeybodiIn this paper a new learning automata-based algorithm is proposed for learning of parameters of a Bayesian network. For this purpose, a new team of learning automata which is called eDLA is used. In this paper the structure of Bayesian network is assumed to be fixed. Ne MoreIn this paper a new learning automata-based algorithm is proposed for learning of parameters of a Bayesian network. For this purpose, a new team of learning automata which is called eDLA is used. In this paper the structure of Bayesian network is assumed to be fixed. New arriving sample plays role of the random environment and the accuracy of the current parameters generates the random environment reinforcement signal. Linear algorithm is used to update the action selection probability of the automata. Another key issue in Bayesian networks is parameter learning under circumstances that new samples are incomplete. It is shown that new proposed method can be used in this situation. The experiments show that the accuracy of the proposed automata based algorithm is the same as the traditional enumerative methods such as EM. In addition to the online learning characteristics, the proposed algorithm is in accordance with the conditions in which the data are incomplete and due to the use of learning automaton, has a little computational overhead. Manuscript profile -
Open Access Article
14 - PLAER: Penalty Base Learning Automata for Energy Aware Routing in WSN
M. Parvizi Omran A. Moeni H. Haj Seyyed JavadiSensors in WSN work with batteries that have limited energy capacity. Therefore, reduction in power consumption is a very important issue. In this paper, we present a new routing algorithm to reduce power consumption in wireless sensor networks. This algorithm deploys L MoreSensors in WSN work with batteries that have limited energy capacity. Therefore, reduction in power consumption is a very important issue. In this paper, we present a new routing algorithm to reduce power consumption in wireless sensor networks. This algorithm deploys Learning automata in each node to find a suitable path for routing data packets. In order to aim this goal the algorithm uses penalty based approach in learning automata and considers energy level of nodes and latency of packet delivery as well. Performance of our new developed algorithm has been compared with LABER and BEAR protocols in OMNET++ simulator. Simulation results show that, in a network with static nodes, energy consumption and control packets reduce significantly and network lifetime increases in comparison with two other protocols. Manuscript profile -
Open Access Article
15 - A New Criterion for Balancing Global Search and Local Search in Memetic Algorithm
Mehdi Rezapoor Mirsaleh M. R. MeybodiOne of the problems with traditional genetic algorithms is its premature convergence that makes them incapable of searching good solutions of the problem. A memetic algorithm (MA) which is an extension of the traditional genetic algorithm uses a local search method to e MoreOne of the problems with traditional genetic algorithms is its premature convergence that makes them incapable of searching good solutions of the problem. A memetic algorithm (MA) which is an extension of the traditional genetic algorithm uses a local search method to either accelerate the discovery of good solutions, for which evolution alone would take too long to discover, or to reach solutions that would otherwise be unreachable by evolution or a local search method alone. In this paper, a memetic algorithm based on learning automata (LA) and memetic algorithm, called LA-MA, is introduced. This algorithm is composed of two parts, genetic section and memetic section. Evolution is performed in genetic section and local search is performed in memetic section. The basic idea of LA-MA is to use learning automata during the process of searching for solutions in order to create a balance between exploration performed by evolution and exploitation performed by local search. To evaluate the efficiency of LA-MA, it has been used to solve two optimization problems: OneMax and graph isomorphism problems. The results of computer experimentations have shown that different versions of LA-MA outperform the others in terms of quality of solution and rate of convergence. Manuscript profile -
Open Access Article
16 - Leaning the Structure of Bayesian Networks Using Learning Automata
M. R. Mollakhalili Meybodi M. R. MeybodiThe structure of a Bayesian network represents a set of conditional independence relations that hold in the domain. Learning the structure of the Bayesian network model that represents a domain can reveal in sights into its underlying causal structure. Automatically lea MoreThe structure of a Bayesian network represents a set of conditional independence relations that hold in the domain. Learning the structure of the Bayesian network model that represents a domain can reveal in sights into its underlying causal structure. Automatically learning the graph structure of a Bayesian network is a challenge pursued within artificial intelligence studies. In this paper, a new algorithm based on learning automata is proposed for learning the structure of the Bayesian networks. In this algorithm, automata is used as a tool for searching in structure’s space (DAG’s space) of the Bayesian networks. The mathematical behavior of the proposed algorithm is studied. Manuscript profile -
Open Access Article
17 - A New Variance-Based Method for Solving Stochastic Graph Optimization Problem Using Learning Automata
M. R. Mollakhalili Meybodi M. R. MeybodiIn this paper, a new criterion is introduced for solving optimization problems on stochastic graphs- as a model of computer networks-by stochastic learning Automata. This proposed method, because of considering estimated variance of response of environment, can better a MoreIn this paper, a new criterion is introduced for solving optimization problems on stochastic graphs- as a model of computer networks-by stochastic learning Automata. This proposed method, because of considering estimated variance of response of environment, can better adaptation to changes of environment. As a result, the proposed method can produce better response to learning Automata actions. The proposed method, by entering a noise, can avoid learning Automata being stuck at a local optimum point. Our simulation shows that this proposed method can be improve the convergence rate of Automata-based algorithm. Manuscript profile -
Open Access Article
18 - A New Memetic Model based on the Fixed Structure Learning Automata
M. Rezapoor Mirsaleh M. R. MeybodiMemetic algorithm (MA) is a kind of evolutionary algorithms (EAs) that searches the problem solving space using local search and global search. The balance between global search and local search is one of the key issues in this algorithm. In this paper a new model is pr MoreMemetic algorithm (MA) is a kind of evolutionary algorithms (EAs) that searches the problem solving space using local search and global search. The balance between global search and local search is one of the key issues in this algorithm. In this paper a new model is proposed, called GALA2. This model is combined of genetic algorithm (GA) and object migration automata (OMA), which is a kind of fixed-structure learning automaton. In the proposed model, global search is performed by genetic algorithm and local learning is performed by learning automata. In this model, the Lamarckian and Baldwinian learning models have been used to increase the convergence rate and avoidance of premature convergence, simultaneously. In this evolutionary model, chromosomes are represented by object migration automata for the purpose of using positive effects of evolution and local learning. In order to show the superiority of the proposed model, GALA2 is used to solve the graph isomorphism problem. Manuscript profile -
Open Access Article
19 - A Transfer Learning Algorithm to Improve the Convergence Rate and Accuracy in Cellular Learning Automata
Seyyed Amir Hadi Minoofam Azam Bastanfard M. R. KeyvanpourCellular 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 MoreCellular 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 -
Open Access Article
20 - A New Algorithm Based on Distributed Learning Automata for Solving Stochastic Linear Optimization Problems on the Group of Permutations
mohammadreza mollakhalili meybodi masoumeh zojajiIn 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 MoreIn 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 -
Open Access Article
21 - TPALA: Two Phase Adaptive Algorithm based on Learning Automata for job scheduling in cloud Environment
Abolfazl Esfandi Javad Akbari Torkestani Abbas Karimi Faraneh ZarafshanDue 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 MoreDue 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
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