Khosrow Amirizadeh | University Science Malaysia (original) (raw)
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Papers by Khosrow Amirizadeh
Hendrickson, 1985
An academic directory and search engine.
INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY
Accelerated multi-armed bandit (MAB) model in Reinforcement-Learning for on-line sequential selec... more Accelerated multi-armed bandit (MAB) model in Reinforcement-Learning for on-line sequential selection problems is presented. This iterative model utilizes an automatic step size calculation that improves the performance of MAB algorithm under different conditions such as, variable variance of reward and larger set of usable actions. As result of these modifications, number of optimal selections will be maximized and stability of the algorithm under mentioned conditions may be amplified. This adaptive model with automatic step size computation may attractive for on-line applications in which, variance of observations vary with time and re-tuning their step size are unavoidable where, this re-tuning is not a simple task. The proposed model governed by upper confidence bound (UCB) approach in iterative form with automatic step size computation. It called adaptive UCB (AUCB) that may use in industrial robotics, autonomous control and intelligent selection or prediction tasks in the ec...
International journal of innovation and scientific research, 2016
Recently Wireless Sensor Networks (WSNs) has turned into a popular matter of research area, becau... more Recently Wireless Sensor Networks (WSNs) has turned into a popular matter of research area, because of its flexibility and dynamic nature. It is proven that the clustering technique, as a multi objective optimization, is the most effective solution to have minimum energy consumption. The goal of the clustering technique is to divide network sensors into clusters each of which has a cluster-head (CH) responsible to collect, aggregate and send sensed data to the base station (BS). The recent researches shown that such multi objective optimization in WSNs can be solved through well adapted an evolutionary algorithm. In this paper an improved k-mean clustering model powered by Particle Swarm Optimization (PSO) algorithm is presented. This is called Link-aware PSO, LSPO. The proposed model utilizes two-phase optimization by applying different fitness function. At the first phase, it selects Primary Cluster-heads based on improved Intra-Cluster Distance metric as fitness function in PSO a...
Recently Wireless Sensor Networks (WSNs) has turned into a popular matter of research area, becau... more Recently Wireless Sensor Networks (WSNs) has turned into a popular matter of research area, because of its flexibility and dynamic nature. It is proven that the clustering technique, as a multi objective optimization, is the most effective solution to have minimum energy consumption. The goal of the clustering technique is to divide network sensors into clusters each of which has a cluster-head (CH) responsible to collect, aggregate and send sensed data to the base station (BS). The recent researches shown that such multi objective optimization in WSNs can be solved through well adapted an evolutionary algorithm. In this paper an improved k-mean clustering model powered by Particle Swarm Optimization (PSO) algorithm is presented. This is called Link-aware PSO, LSPO. The proposed model utilizes two-phase optimization by applying different fitness function. At the first phase, it selects Primary Cluster-heads based on improved Intra-Cluster Distance metric as fitness function in PSO algorithm to give primary CHs. In the second phase each primary CHs selected are evaluated by link quality and energy metrics to select the best ones as final CHs. Simulation results showed that the proposed algorithm outperforms LEACH and PSO-C algorithms in term of performance, prolonging network lifetime and energy saving.
Accelerated multi-armed bandit (MAB) model in Reinforcement-Learning for on-line sequential selec... more Accelerated multi-armed bandit (MAB) model in Reinforcement-Learning for on-line sequential selection problems is presented. This iterative model utilizes an automatic step size calculation that improves the performance of MAB algorithm under different conditions such as, variable variance of reward and larger set of usable actions. As result of these modifications, number of optimal selections will be maximized and stability of the algorithm under mentioned conditions may be amplified. This adaptive model with automatic step size computation may attractive for on-line applications in which, variance of observations vary with time and re-tuning their step size are unavoidable where, this re-tuning is not a simple task. The proposed model governed by upper confidence bound (UCB) approach in iterative form with automatic step size computation. It called adaptive UCB (AUCB) that may use in industrial robotics, autonomous control and intelligent selection or prediction tasks in the economical engineering applications under lack of information.
— Current algorithms for solving multi-armed bandit (MAB) problem in stationary observations ofte... more — Current algorithms for solving multi-armed bandit (MAB) problem in stationary observations often perform well. Although this performance may be acceptable under carefully tuned accurate parameter settings, most of them degrade under non stationary observations. Among these-greedy iterative approach is still attractive and is more applicable to real-world tasks owing to its simple implementation. One main concern among the iterative models is the parameter dependency and more specifically the dependency on step size. This study proposes an enhanced-greedy iterative model, termed as adaptive step size model (ASM), for solving multi-armed bandit (MAB) problem. This model is inspired by the steepest descent optimization approach that automatically computes the optimal step size of the algorithm. In addition, it also introduces a dynamic exploration parameter that is progressively ineffective with increasing process intelligence. This model is empirically evaluated and compared, under stationary as well as non stationary situations, with previously proposed popular algorithms: traditional-greedy, Softmax,-decreasing and UCB-Tuned approaches. ASM not only addresses the concerns on parameter dependency but also performs either comparably or better than all other algorithms. With the proposed enhancements ASM learning time is greatly reduced and this feature of ASM is attractive to a wide range of online decision making applications such as autonomous agents, game theory, adaptive control, industrial robots and prediction tasks in management and economics.
Journal of Software, 2015
Hendrickson, 1985
An academic directory and search engine.
INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY
Accelerated multi-armed bandit (MAB) model in Reinforcement-Learning for on-line sequential selec... more Accelerated multi-armed bandit (MAB) model in Reinforcement-Learning for on-line sequential selection problems is presented. This iterative model utilizes an automatic step size calculation that improves the performance of MAB algorithm under different conditions such as, variable variance of reward and larger set of usable actions. As result of these modifications, number of optimal selections will be maximized and stability of the algorithm under mentioned conditions may be amplified. This adaptive model with automatic step size computation may attractive for on-line applications in which, variance of observations vary with time and re-tuning their step size are unavoidable where, this re-tuning is not a simple task. The proposed model governed by upper confidence bound (UCB) approach in iterative form with automatic step size computation. It called adaptive UCB (AUCB) that may use in industrial robotics, autonomous control and intelligent selection or prediction tasks in the ec...
International journal of innovation and scientific research, 2016
Recently Wireless Sensor Networks (WSNs) has turned into a popular matter of research area, becau... more Recently Wireless Sensor Networks (WSNs) has turned into a popular matter of research area, because of its flexibility and dynamic nature. It is proven that the clustering technique, as a multi objective optimization, is the most effective solution to have minimum energy consumption. The goal of the clustering technique is to divide network sensors into clusters each of which has a cluster-head (CH) responsible to collect, aggregate and send sensed data to the base station (BS). The recent researches shown that such multi objective optimization in WSNs can be solved through well adapted an evolutionary algorithm. In this paper an improved k-mean clustering model powered by Particle Swarm Optimization (PSO) algorithm is presented. This is called Link-aware PSO, LSPO. The proposed model utilizes two-phase optimization by applying different fitness function. At the first phase, it selects Primary Cluster-heads based on improved Intra-Cluster Distance metric as fitness function in PSO a...
Recently Wireless Sensor Networks (WSNs) has turned into a popular matter of research area, becau... more Recently Wireless Sensor Networks (WSNs) has turned into a popular matter of research area, because of its flexibility and dynamic nature. It is proven that the clustering technique, as a multi objective optimization, is the most effective solution to have minimum energy consumption. The goal of the clustering technique is to divide network sensors into clusters each of which has a cluster-head (CH) responsible to collect, aggregate and send sensed data to the base station (BS). The recent researches shown that such multi objective optimization in WSNs can be solved through well adapted an evolutionary algorithm. In this paper an improved k-mean clustering model powered by Particle Swarm Optimization (PSO) algorithm is presented. This is called Link-aware PSO, LSPO. The proposed model utilizes two-phase optimization by applying different fitness function. At the first phase, it selects Primary Cluster-heads based on improved Intra-Cluster Distance metric as fitness function in PSO algorithm to give primary CHs. In the second phase each primary CHs selected are evaluated by link quality and energy metrics to select the best ones as final CHs. Simulation results showed that the proposed algorithm outperforms LEACH and PSO-C algorithms in term of performance, prolonging network lifetime and energy saving.
Accelerated multi-armed bandit (MAB) model in Reinforcement-Learning for on-line sequential selec... more Accelerated multi-armed bandit (MAB) model in Reinforcement-Learning for on-line sequential selection problems is presented. This iterative model utilizes an automatic step size calculation that improves the performance of MAB algorithm under different conditions such as, variable variance of reward and larger set of usable actions. As result of these modifications, number of optimal selections will be maximized and stability of the algorithm under mentioned conditions may be amplified. This adaptive model with automatic step size computation may attractive for on-line applications in which, variance of observations vary with time and re-tuning their step size are unavoidable where, this re-tuning is not a simple task. The proposed model governed by upper confidence bound (UCB) approach in iterative form with automatic step size computation. It called adaptive UCB (AUCB) that may use in industrial robotics, autonomous control and intelligent selection or prediction tasks in the economical engineering applications under lack of information.
— Current algorithms for solving multi-armed bandit (MAB) problem in stationary observations ofte... more — Current algorithms for solving multi-armed bandit (MAB) problem in stationary observations often perform well. Although this performance may be acceptable under carefully tuned accurate parameter settings, most of them degrade under non stationary observations. Among these-greedy iterative approach is still attractive and is more applicable to real-world tasks owing to its simple implementation. One main concern among the iterative models is the parameter dependency and more specifically the dependency on step size. This study proposes an enhanced-greedy iterative model, termed as adaptive step size model (ASM), for solving multi-armed bandit (MAB) problem. This model is inspired by the steepest descent optimization approach that automatically computes the optimal step size of the algorithm. In addition, it also introduces a dynamic exploration parameter that is progressively ineffective with increasing process intelligence. This model is empirically evaluated and compared, under stationary as well as non stationary situations, with previously proposed popular algorithms: traditional-greedy, Softmax,-decreasing and UCB-Tuned approaches. ASM not only addresses the concerns on parameter dependency but also performs either comparably or better than all other algorithms. With the proposed enhancements ASM learning time is greatly reduced and this feature of ASM is attractive to a wide range of online decision making applications such as autonomous agents, game theory, adaptive control, industrial robots and prediction tasks in management and economics.
Journal of Software, 2015