List of Articles Particle Swarm Optimization

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  1 - Optimal LO Selection in E-Learning Environment Using PSO Algorithm
  gholamali montazer
  One of the key issues in e-learning is to identify needs, educational behavior and learning speed of the learners and design a suitable curriculum commensurate to their abilities. This goal is achieved by identifying the learners’ different dimension of personality and More

  One of the key issues in e-learning is to identify needs, educational behavior and learning speed of the learners and design a suitable curriculum commensurate to their abilities. This goal is achieved by identifying the learners’ different dimension of personality and ability and assigning suitable learning material to them according these features. In this paper, an intelligent tutoring system is proposed which optimizes the LO selection in e-learning environment. In order to evaluate the proposed method, the designed system has been used in a web-based instruction system in different conditions and the results of the "Academically success", "Satisfactory learning achievement" and "Time of the learners’ attendance" have been analyzed. The obtained results show a significant efficiency compared to other applied methods. Manuscript profile
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  2 - Using a Hybrid PSO-GA Method for Capacitor Placement in Distribution Systems
  mohammadmahdi Varahram amir mohammadi
  In this paper, we have proposed a new algorithm which combines PSO and GA in such a way that the new algorithm is more effective and efficient.The particle swarm optimization (PSO) algorithm has shown rapid convergence during the initial stages of a global search but ar More

  In this paper, we have proposed a new algorithm which combines PSO and GA in such a way that the new algorithm is more effective and efficient.The particle swarm optimization (PSO) algorithm has shown rapid convergence during the initial stages of a global search but around global optimum, the search process will become very slow. On the other hand, genetic algorithm is very sensitive to the initial population. In fact, the random nature of the GA operators makes the algorithm sensitive to the initial population. This dependence to the initial population is in such a manner that the algorithm may not converge if the initial population is not well selected. This new algorithm can perform faster and does not depend on initial population and can find optimal solutions with acceptable accuracy. Optimal capacitor placement and sizing have been found using this hybrid PSO-GA algorithm. We have also found the optimal place and size of capacitors using GA and PSO separately and compared the results. Manuscript profile
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  3 - Multimodal Biometric Recognition Using Particle Swarm Optimization-Based Selected Features
  Sara Motamed Ali Broumandnia Azam sadat  Nourbakhsh
  10.7508/jist.2013.02.002
  Feature selection is one of the best optimization problems in human recognition, which reduces the number of features, removes noise and redundant data in images, and results in high rate of recognition. This step affects on the performance of a human recognition system More

  Feature selection is one of the best optimization problems in human recognition, which reduces the number of features, removes noise and redundant data in images, and results in high rate of recognition. This step affects on the performance of a human recognition system. This paper presents a multimodal biometric verification system based on two features of palm and ear which has emerged as one of the most extensively studied research topics that spans multiple disciplines such as pattern recognition, signal processing and computer vision. Also, we present a novel Feature selection algorithm based on Particle Swarm Optimization (PSO). PSO is a computational paradigm based on the idea of collaborative behavior inspired by the social behavior of bird flocking or fish schooling. In this method, we used from two Feature selection techniques: the Discrete Cosine Transforms (DCT) and the Discrete Wavelet Transform (DWT). The identification process can be divided into the following phases: capturing the image; pre-processing; extracting and normalizing the palm and ear images; feature extraction; matching and fusion; and finally, a decision based on PSO and GA classifiers. The system was tested on a database of 60 people (240 palm and 180 ear images). Experimental results show that the PSO-based feature selection algorithm was found to generate excellent recognition results with the minimal set of selected features. Manuscript profile
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  4 - A Two-Stage Multi-Objective Enhancement for Fused Magnetic Resonance Image and Computed Tomography Brain Images
  Leena Chandrashekar A Sreedevi Asundi
  10.29252/jist.8.30.93
  Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are the imaging techniques for detection of Glioblastoma. However, a single imaging modality is never adequate to validate the presence of the tumor. Moreover, each of the imaging techniques represents a diff More

  Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are the imaging techniques for detection of Glioblastoma. However, a single imaging modality is never adequate to validate the presence of the tumor. Moreover, each of the imaging techniques represents a different characteristic of the brain. Therefore, experts have to analyze each of the images independently. This requires more expertise by doctors and delays the detection and diagnosis time. Multimodal Image Fusion is a process of generating image of high visual quality, by fusing different images. However, it introduces blocking effect, noise and artifacts in the fused image. Most of the enhancement techniques deal with contrast enhancement, however enhancing the image quality in terms of edges, entropy, peak signal to noise ratio is also significant. Contrast Limited Adaptive Histogram Equalization (CLAHE) is a widely used enhancement technique. The major drawback of the technique is that it only enhances the pixel intensities and also requires selection of operational parameters like clip limit, block size and distribution function. Particle Swarm Optimization (PSO) is an optimization technique used to choose the CLAHE parameters, based on a multi objective fitness function representing entropy and edge information of the image. The proposed technique provides improvement in visual quality of the Laplacian Pyramid fused MRI and CT images. Manuscript profile
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  5 - Evaluation of Pattern Recognition Techniques in Response to Cardiac Resynchronization Therapy (CRT)
  Mohammad Nejadeh Peyman Bayat Jalal Kheirkhah Hassan Moladoust
  10.29252/jist.8.31.197
  Cardiac resynchronization therapy (CRT) improves cardiac function in patients with heart failure (HF), and the result of this treatment is decrease in death rate and improving quality of life for patients. This research is aimed at predicting CRT response for the progno More

  Cardiac resynchronization therapy (CRT) improves cardiac function in patients with heart failure (HF), and the result of this treatment is decrease in death rate and improving quality of life for patients. This research is aimed at predicting CRT response for the prognosis of patients with heart failure under CRT. According to international instructions, in the case of approval of QRS prolongation and decrease in ejection fraction (EF), the patient is recognized as a candidate of implanting recognition device. However, regarding many intervening and effective factors, decision making can be done based on more variables. Computer-based decision-making systems especially machine learning (ML) are considered as a promising method regarding their significant background in medical prediction. Collective intelligence approaches such as particles swarm optimization (PSO) algorithm are used for determining the priorities of medical decision-making variables. This investigation was done on 209 patients and the data was collected over 12 months. In HESHMAT CRT center, 17.7% of patients did not respond to treatment. Recognizing the dominant parameters through combining machine recognition and physician’s viewpoint, and introducing back-propagation of error neural network algorithm in order to decrease classification error are the most important achievements of this research. In this research, an analytical set of individual, clinical, and laboratory variables, echocardiography, and electrocardiography (ECG) are proposed with patients’ response to CRT. Prediction of the response after CRT becomes possible by the support of a set of tools, algorithms, and variables. Manuscript profile
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  6 - A Hybrid Approach based on PSO and Boosting Technique for Data Modeling in Sensor Networks
  hadi shakibian Jalaledin Nasiri
  10.52547/jist.19789.10.40.259
  An efficient data aggregation approach in wireless sensor networks (WSNs) is to abstract the network data into a model. In this regard, regression modeling has been addressed in many studies recently. If the limited characteristics of the sensor nodes are omitted from c More

  An efficient data aggregation approach in wireless sensor networks (WSNs) is to abstract the network data into a model. In this regard, regression modeling has been addressed in many studies recently. If the limited characteristics of the sensor nodes are omitted from consideration, a common regression technique could be employed after transmitting all the network data from the sensor nodes to the fusion center. However, it is not practical nor efferent. To overcome this issue, several distributed methods have been proposed in WSNs where the regression problem has been formulated as an optimization based data modeling problem. Although they are more energy efficient than the centralized method, the latency and prediction accuracy needs to be improved even further. In this paper, a new approach is proposed based on the particle swarm optimization (PSO) algorithm. Assuming a clustered network, firstly, the PSO algorithm is employed asynchronously to learn the network model of each cluster. In this step, every cluster model is learnt based on the size and data pattern of the cluster. Afterwards, the boosting technique is applied to achieve a better accuracy. The experimental results show that the proposed asynchronous distributed PSO brings up to 48% reduction in energy consumption. Moreover, the boosted model improves the prediction accuracy about 9% on the average. Manuscript profile
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  7 - Multi-Objective Particle Swarm Classifier
  Seyed-Hamid Zahiri
  A multi-objective particle swarm optimization (MOPSO) algorithm has been used to design a classifier which is able to optimize some important pattern recognition indices concurrently. These are Reliability, Score of recognition, and the number of hyperplanes. The propos More

  A multi-objective particle swarm optimization (MOPSO) algorithm has been used to design a classifier which is able to optimize some important pattern recognition indices concurrently. These are Reliability, Score of recognition, and the number of hyperplanes. The proposed classifier can efficiently approximate the decision hyperplanes for separating the different classes in the feature space and dose not have any over-fitting and over-learning problems. Other swarm intelligence based classifiers do not have the capability of simultaneous optimizing aforesaid indices and they also may suffer the over-fitting problem. The experimental results show that the proposed multi-objective classifier can estimate the optimum sets of hyperplanes by approximating the Pareto-front and provide the favorite user's setup for selecting aforesaid indices. Manuscript profile
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  8 - An Intelligent BGSA Based Method for Feature Selection in a Persian Handwritten Digits Recognition System
  N. Ghanbari S. M. Razavi S. H. Nabavi Karizi
  In this paper, an intelligent feature selection method for recognition of Persian handwritten digits is presented. The fitness function associated with the error in the Persian handwritten digits recognition system is minimized, by selecting the appropriate features, us More

  In this paper, an intelligent feature selection method for recognition of Persian handwritten digits is presented. The fitness function associated with the error in the Persian handwritten digits recognition system is minimized, by selecting the appropriate features, using binary gravitational search algorithm. Implementation results show that the use of intelligent methods is well able to choose the most effective features for this recognition system. The results of the proposed method in comparison with other similar methods based on genetic algorithm and binary particle method of optimizing indicates the effective performance of the proposed method. Manuscript profile
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  9 - Placement of AVRs and Reconfiguration of Distribution Networks Simultaneously and Robust Considering Load Uncertainty
  M. R.  Shakarami Y. Mohammadi Pour
  : In this paper, optimal locating for AVRs and reconfiguration of distribution networks were assessed simultaneously as an optimization problem. A new objective function was introducing which incorporated several electrical indices including real power losses, reactive More

  : In this paper, optimal locating for AVRs and reconfiguration of distribution networks were assessed simultaneously as an optimization problem. A new objective function was introducing which incorporated several electrical indices including real power losses, reactive power losses, reliability, voltage profile, voltage stability, and load capacity of lines (MVA). Various load levels were incorporated into the objective function to make sure that switch status in reconfiguration and AVR taps and locations would be robust against load variations. This paper also introduced a new method for calculating the load levels with respect to load uncertainty. It also considered all loads based on a voltage-dependent model. Several scenarios are defined to thoroughly assess the proposed approach. Integer particle swarm optimization algorithm (IPSO) was used to solve the mentioned optimization problem. The results obtained by the simulation of 33-bus and 69-bus standard IEEE .radial power distribution networks demonstrated the effectiveness of the proposed approach Manuscript profile
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  10 - Optimal Design of Six-Phase Radial Flux Permanent Magnet Synchronous Generator for Small Scale Wind Turbine Applications
  M. E. Moazzen S. A. Gholamian  
  This paper presents optimal design of a six-phase permanent magnet synchronous generator (PMSG) for use in direct drive wind turbines. High Dimensions and manufacturing cost and low efficiency are the disadvantages of generators connected to wind turbines without gearbo More

  This paper presents optimal design of a six-phase permanent magnet synchronous generator (PMSG) for use in direct drive wind turbines. High Dimensions and manufacturing cost and low efficiency are the disadvantages of generators connected to wind turbines without gearbox because of their low nominal speed. Therefore, the main purpose of this paper is to optimize the design of the PMSG based on the reduction of losses and the construction cost of the generator. For this purpose, the relations governing the design of the radial flux PMSG have been introduced and then a design algorithm has been extracted. Subsequently, by defining a multi-objective optimization problem and using the particle swarm optimization (PSO) algorithm, the optimum design variables are determined in a suitable range and the minimum losses and construction cost of the generator are obtained. The optimal design has been verified by using finite element analysis. Manuscript profile
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  11 - Multi-Label Feature Selection Using a Hybrid Approach Based on the Particle Swarm Optimization Algorithm
  َAzar Rafiei Parham Moradi Abdolbaghi Ghaderzadeh
  Multi-label classification is one of the important issues in machine learning. The efficiency of multi-label classification algorithms decreases drastically with increasing problem dimensions. Feature selection is one of the main solutions for dimension reduction in mul More

  Multi-label classification is one of the important issues in machine learning. The efficiency of multi-label classification algorithms decreases drastically with increasing problem dimensions. Feature selection is one of the main solutions for dimension reduction in multi-label problems. Multi-label feature selection is one of the NP solutions, and so far, a number of solutions based on collective intelligence and evolutionary algorithms have been proposed for it. Increasing the dimensions of the problem leads to an increase in the search space and consequently to a decrease in efficiency and also a decrease in the speed of convergence of these algorithms. In this paper, a hybrid collective intelligence solution based on a binary particle swarm optimization algorithm and local search strategy for multi-label feature selection is presented. To increase the speed of convergence, in the local search strategy, the features are divided into two categories based on the degree of extension and the degree of connection with the output of the problem. The first category consists of features that are very similar to the problem class and less similar to other features, and the second category is similar features and less related. Therefore, a local operator is added to the particle swarm optimization algorithm, which leads to the reduction of irrelevant features and extensions of each solution. Applying this operator leads to an increase in the convergence speed of the proposed algorithm compared to other algorithms presented in this field. The performance of the proposed method has been compared with the most well-known feature selection methods on different datasets. The results of the experiments showed that the proposed method has a good performance in terms of accuracy. Manuscript profile
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  12 - Improving resource allocation in mobile edge computing using gray wolf and particle swarm optimization algorithms
  seyed ebrahim dashti saeid shabooei
  Mobile edge computing improves the experience of end users to achieve appropriate services and service quality. In this paper, the problem of improving resource allocation when offloading tasks based on mobile devices to edge servers in computing systems was investigate More

  Mobile edge computing improves the experience of end users to achieve appropriate services and service quality. In this paper, the problem of improving resource allocation when offloading tasks based on mobile devices to edge servers in computing systems was investigated. Some tasks are processed locally and some are offloaded to edge servers. The main issue is that the offloaded tasks for virtual machines in computing networks are properly scheduled to minimize computing time, service cost, computing network waste, and the maximum connection of a task with the network. In this paper, it was introduced using the hybrid algorithm of particle swarm and gray wolf to manage resource allocation and task scheduling to achieve an optimal result in edge computing networks. The comparison results show the improvement of waiting time and cost in the proposed approach. The results show that, on average, the proposed model has performed better by reducing the work time by 10% and increasing the use of resources by 16%. Manuscript profile
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  13 - Improving Resource Allocation in Mobile Edge Computing Using Particle Swarm and Gray Wolf Optimization Algorithms
  seyed ebrahim dashti saeid shabooei
  Mobile edge computing improves the experience of end users to achieve appropriate services and service quality. In this paper, the problem of improving resource allocation, when offloading tasks, based on mobile devices to edge servers in computing systems is investigat More

  Mobile edge computing improves the experience of end users to achieve appropriate services and service quality. In this paper, the problem of improving resource allocation, when offloading tasks, based on mobile devices to edge servers in computing systems is investigated. Some tasks are uploaded and processed locally and some to edge servers. The main issue is that the offloaded tasks for virtual machines in computing networks are properly scheduled to minimize computing time, service cost, computing network waste, and the maximum connection of a task with the network. In this paper, a multi-objective hybrid algorithm of particle swarm and gray wolf was introduced to manage resource allocation and task scheduling to achieve an optimal result in edge computing networks. Local search in the particle swarm algorithm has good results in the problem, but it will cause the loss of global optima, so in this problem, in order to improve the model, the gray wolf algorithm was used as the main basis of the proposed algorithm, in the wolf algorithm Gray, due to the graphical approach to the problem, the set of global searches will reach the optimal solution, so by combining these functions, we tried to improve the operational conditions of the two algorithms for the desired goals of the problem. In order to create a network in this research, the network creation parameters in the basic article were used and the LCG data set was used in the simulation. The simulation environment in this research is the sim cloud environment. The comparison results show the improvement of waiting time and cost in the proposed approach. The results show that, on average, the proposed model has performed better by reducing the work time by 10% and increasing the use of resources by 16%. Manuscript profile