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Tracking Extrema in Dynamic Environments Using a Learning AutomataBased Immune Algorithm
 in Grid and Distributed Computing, Control and Automation
, 2010
"... Abstract. In recent years, bioinspired algorithms have increasingly been used by researchers for solving various optimization problems increasingly. Many real world problems are mostly time varying optimization problems, which require special mechanisms for detecting changes in environment and then ..."
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Abstract. In recent years, bioinspired algorithms have increasingly been used by researchers for solving various optimization problems increasingly. Many real world problems are mostly time varying optimization problems, which require special mechanisms for detecting changes in environment and then responding to them. The present paper has been proposed to combination the learning automata and artificial immune algorithm in order to improve the performance of immune system algorithm in dynamic environments. In the proposed algorithm, the immune cells are equipped with a learning automaton. So they can increase diversity in response the dynamic environments. Learning automata based immune algorithm for dynamic environment has been tested in the moving parabola as a popular standard dynamic environment and compared by several famous algorithms in dynamic environments.
A Cellular Learning Automatabased Deployment Strategy for Mobile Wireless Sensor Networks
"... Abstract: One important problem which may arise in designing a deployment strategy for a wireless sensor network is how to deploy a specific number of sensor nodes throughout an unknown network area so that the covered section of the area is maximized. In a mobile sensor network, this problem can be ..."
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Cited by 5 (3 self)
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Abstract: One important problem which may arise in designing a deployment strategy for a wireless sensor network is how to deploy a specific number of sensor nodes throughout an unknown network area so that the covered section of the area is maximized. In a mobile sensor network, this problem can be addressed by first deploying sensor nodes randomly in some initial positions within the area of the network, and then letting sensor nodes to move around and find their best positions according to the positions of their neighboring nodes. The problem becomes more complicated if sensor nodes have no information about their positions or even their relative distances to each other. In this paper, we propose a cellular learning automatabased deployment strategy which guides the movements of sensor nodes within the area of the network without any sensor to know its position or its relative distance to other sensors. In the proposed algorithm, the learning automaton in each node in cooperation with the learning automata in the neighboring nodes controls the movements of the node in order to attain high coverage. Experimental results have shown that in noise free environments, the proposed algorithm can compete with the existing algorithms such as PF, DSSA, IDCA, and VEC in terms of network coverage. It has also been shown that in noisy environments, where utilized location estimation techniques such as GPSbased devices and localization algorithms experience inaccuracies in their measurements, or the movements of sensor nodes are not perfect and follow a probabilistic motion model, the proposed algorithm outperforms the existing algorithms in terms of network coverage.
Decreasing Impact of SLA Violations: A Proactive Resource Allocation Approach for Cloud Computing Environments
"... Abstract—User satisfaction as a significant antecedent to user loyalty has been highlighted by many researchers in market based literatures. SLA violation as an important factor can decrease users ’ satisfaction level. The amount of this decrease depends on user’s characteristics. Some of these char ..."
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Abstract—User satisfaction as a significant antecedent to user loyalty has been highlighted by many researchers in market based literatures. SLA violation as an important factor can decrease users ’ satisfaction level. The amount of this decrease depends on user’s characteristics. Some of these characteristics are related to QoS requirements and announced to service provider through SLAs. But some of them are unknown for service provider and selfish users are not interested to reveal them truly. Most the works in literature ignore considering such characteristics and treat users just based on SLA parameters. So, two users with different characteristics but similar SLAs have equal importance for the service provider. In this paper, we use two user’s hidden characteristics, named willingness to pay for service and willingness to pay for certainty, to present a new proactive resource allocation approach with aim of decreasing impact of SLA violations. New methods based on learning automaton for estimation of these characteristics are provided as well. To validate our approach we conducted some numerical simulations in critical situations. The results confirm that our approach has ability to improve users ’ satisfaction level that cause to gain in profitability. Index Terms—Users satisfaction level, cloud service, resource allocation, willingness to pay, learning automaton Ç 1
Irregular Cellular Learning Automata and Its Application to Clustering in Sensor Networks
"... In the first part of this paper, we propose a generalization of cellular learning automata (CLA) called irregular cellular learning automata (ICLA) which removes the restriction of rectangular grid structure in traditional CLA. This generalization is expected because there are a number of applicatio ..."
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In the first part of this paper, we propose a generalization of cellular learning automata (CLA) called irregular cellular learning automata (ICLA) which removes the restriction of rectangular grid structure in traditional CLA. This generalization is expected because there are a number of applications which cannot be adequately modeled with rectangular grids. One category of such applications is in the area of wireless sensor networks. In these networks, nodes are usually scattered randomly throughout the environment, so no regular structure can be assumed for modeling their behavior. In the second part of the paper, based on the proposed model we design a clustering algorithm for sensor networks. Simulation results show that the proposed clustering algorithm is very efficient and outperforms similar existing methods.
A Hybrid Web Recommender System Based on Cellular Learning Automata
"... With the rapid growth of the World Wide Web (WWW), finding useful information from the Internet has become a critical issue. Web recommender systems help users make decisions in this complex information space where the volume of information available to them is huge. Recently, a number of web page r ..."
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With the rapid growth of the World Wide Web (WWW), finding useful information from the Internet has become a critical issue. Web recommender systems help users make decisions in this complex information space where the volume of information available to them is huge. Recently, a number of web page recommender systems have been developed to anticipate the information needs of online users and provide them with recommendations to facilitate and personalize their navigation. Recent studies show that a web usage recommender system which focuses solely on access history has some problems because sometimes this information is incomplete or incorrect. One common solution to this problem is to incorporate some semantic knowledge about pages being recommended into system. In this paper we exploit this idea to improve the dynamic web recommender system which primarily devised for web recommendation based on web usage and structure data. We propose a hybrid web page recommender system based on asynchronous cellular learning automata with multiple learning automata in each cell which try to identify user's multiple information needs and then assist them to recommend pages to users. The proposed system use web usage data, content and structure of the web site to learn user information needs and predicting user's
A Learning Automata Based Adaptive Uniform Fractional Guard Channel Algorithm
"... Abstract. Uniform fractional policy (UFC) is a call admission policy that accepts new calls with a probability pi. In order to find the optimal value of pi, we need to know all traffic parameters or to estimate them. In this paper, we first propose a new adaptive algorithm based on learning automata ..."
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Abstract. Uniform fractional policy (UFC) is a call admission policy that accepts new calls with a probability pi. In order to find the optimal value of pi, we need to know all traffic parameters or to estimate them. In this paper, we first propose a new adaptive algorithm based on learning automata for finding the optimal value of UFC parameter and then study its steady state behavior. It is shown that the given adaptive algorithm converges to an equilibrium point which is also optimal for UFC policy. In order to study the performance of the proposed call admission policy, the computer simulations are conducted. The simulation results show that the level of QoS is satisfied by the proposed algorithm and the performance of given algorithm is very close to the performance of uniform fractional guard channel policy which needs to know all parameters of input traffic.
An Adaptive Scheduling Algorithm for Set Cover Problem in Wireless Sensor Networks: A Cellular Learning Automata Approach
"... Abstract—Redundant node deployment is a common strategy in wireless sensor networks. This redundancy can be due to various reasons such as high probability of failures, long lifetime expectation, etc. One major problem in wireless sensor networks is to use this redundancy in order to extend the netw ..."
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Abstract—Redundant node deployment is a common strategy in wireless sensor networks. This redundancy can be due to various reasons such as high probability of failures, long lifetime expectation, etc. One major problem in wireless sensor networks is to use this redundancy in order to extend the network lifetime while keeping the entire area under the coverage of the network. In this problem, which is known as set cover problem, the main objective is to select a subset of sensor nodes as active nodes so that the set of active nodes covers the entire area of the network. In this paper, an scheduling algorithm is presented for solving the set cover problem using cellular learning automata. In this algorithm, each node is equipped with a learning automaton which decides for the node to be active or not locally and based on the situations of its neighbors. Simulation results in JSim simulator environment specify the efficiency of the proposed scheduling algorithm over existing algorithms such as PEAS and PECAS.
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"... Abstract—Redundant node deployment is a common strategy in wireless sensor networks. This redundancy can be due to various reasons such as high probability of failures, long lifetime expectation, etc. One major problem in wireless sensor networks is to use this redundancy in order to extend the netw ..."
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Abstract—Redundant node deployment is a common strategy in wireless sensor networks. This redundancy can be due to various reasons such as high probability of failures, long lifetime expectation, etc. One major problem in wireless sensor networks is to use this redundancy in order to extend the network lifetime while keeping the entire area under the coverage of the network. In this problem, which is known as set cover problem, the main objective is to select a subset of sensor nodes as active nodes so that the set of active nodes covers the entire area of the network. In this paper, an scheduling algorithm is presented for solving the set cover problem using cellular learning automata. In this algorithm, each node is equipped with a learning automaton which locally decides for the node to be active or not based on the situations of its neighbors. Simulation results in Jsim simulator environment specify the efficiency of the proposed scheduling algorithm over existing algorithms such as PEAS and PECAS. Index Terms—Area coverage, cellular learning automata, learning automata, scheduling algorithm, wireless sensor networks. I.
This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON CYBERNETICS 1 Irregular Cellular Learning Automata
"... Abstract—Cellular learning automaton (CLA) is a recently introduced model that combines cellular automaton (CA) and learning automaton (LA). The basic idea of CLA is to use LA to adjust the state transition probability of stochastic CA. This model has been used to solve problems in areas such as cha ..."
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Abstract—Cellular learning automaton (CLA) is a recently introduced model that combines cellular automaton (CA) and learning automaton (LA). The basic idea of CLA is to use LA to adjust the state transition probability of stochastic CA. This model has been used to solve problems in areas such as channel assignment in cellular networks, call admission control, image processing, and very large scale integration placement. In this paper, an extension of CLA called irregular CLA (ICLA) is introduced. This extension is obtained by removing the structure regularity assumption in CLA. Irregularity in the structure of ICLA is needed in some applications, such as computer networks, web mining, and grid computing. The concept of expediency has been introduced for ICLA and then, conditions under which an ICLA becomes expedient are analytically found. Index Terms—Expediency, irregular cellular learning automata (ICLA), Markov process, steadystate analysis. I.
IEEE TRANSACTIONS ON CLOUD COMPUTING, SPECIAL ISSUE 1 Decreasing Impact of SLA Violations: A Proactive Resource Allocation Approach for
"... Abstract — User satisfaction as a significant antecedent to user loyalty has been highlighted by many researchers in market based literatures. SLA violation as an important factor can decrease users ’ satisfaction level. The amount of this decrease depends on user’s characteristics. Some of these ch ..."
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Abstract — User satisfaction as a significant antecedent to user loyalty has been highlighted by many researchers in market based literatures. SLA violation as an important factor can decrease users ’ satisfaction level. The amount of this decrease depends on user’s characteristics. Some of these characteristics are related to QoS requirements and announced to service provider through SLAs. But some of them are unknown for service provider and selfish users are not interested to reveal them truly. Most the works in literature ignore considering such characteristics and treat users just based on SLA parameters. So, two users with different characteristics but similar SLAs have equal importance for the service provider. In this paper, we use two user’s hidden characteristics, named willingness to pay for service and willingness to pay for certainty, to present a new proactive resource allocation approach with aim of decreasing impact of SLA violations. New methods based on learning automaton for estimation of these characteristics are provided as well. To validate our approach we conducted some numerical simulations in critical situations. The results confirm that our approach has ability to improve users ’ satisfaction level that cause to gain in profitability.