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Open Access Article
1 - using clustering in AODV routing protocol for vehicular ad-hoc networks on highway scenario
amin feyziVehicular Ad hoc networks are a subset of mobile Ad hoc networks in which vehicles are considered as network nodes. Their major difference is rapid mobility of nodes which causes the quick change of topology in this network. Quick changes in the topology of the network MoreVehicular Ad hoc networks are a subset of mobile Ad hoc networks in which vehicles are considered as network nodes. Their major difference is rapid mobility of nodes which causes the quick change of topology in this network. Quick changes in the topology of the network are considered as a big challenge For routing in these networks, routing protocols must be robust and reliable. AODV Routing protocol is one of the known routing protocols in vehicular ad hoc networks. There are also some problems in applying this routing protocol on the vehicular ad hoc networks. The number of control massages increases with increasing the scale of the network and the number of nodes . One way to reduce the overhead in AODV routing protocol is clustering the nodes of the network. In this paper , the modified K-means algorithm has been used for clustering the nodes and particle swarm optimization has been used for selecting cluster head. The results of the proposed method improved normalized routing load and the increase of the packet delivery rate compared to AODV routing protocol. Manuscript profile -
Open Access Article
2 - Optimal LO Selection in E-Learning Environment Using PSO Algorithm
gholamali montazerOne 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 MoreOne 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 -
Open Access Article
3 - Using a Hybrid PSO-GA Method for Capacitor Placement in Distribution Systems
mohammadmahdi Varahram amir mohammadiIn 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 MoreIn 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 -
Open Access Article
4 - Multimodal Biometric Recognition Using Particle Swarm Optimization-Based Selected Features
Sara Motamed Ali Broumandnia Azam sadat NourbakhshFeature 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 MoreFeature 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 -
Open Access Article
5 - PSO-Algorithm-Assisted Multiuser Detection for Multiuser and Inter-symbol Interference Suppression in CDMA Communications
Atefeh Haji Jamali Arani paeez azmiApplying particle swarm optimization (PSO) algorithm has become a widespread heuristic technique in many fields of engineering. In this paper, we apply PSO algorithm in additive white Gaussian noise (AWGN) and multipath fading channels. In the proposed method, PSO algor MoreApplying particle swarm optimization (PSO) algorithm has become a widespread heuristic technique in many fields of engineering. In this paper, we apply PSO algorithm in additive white Gaussian noise (AWGN) and multipath fading channels. In the proposed method, PSO algorithm was applied to solve joint multiuser and inter-symbol interference (ISI) suppression problems in the code-division multiple-access (CDMA) systems over multipath Rayleigh fading channel and consequently, to reduce the computational complexity. At the first stage, to initialize the POS algorithm, conventional detector (CD) was employed. Then, time-varying acceleration coefficients (TVAC) were used in the PSO algorithm. The simulation results indicated that the performance of PSO-based multiuser detection (MUD) with TVAC is promising and it is outperforming the CD. Manuscript profile -
Open Access Article
6 - Hybrid Task Scheduling Method for Cloud Computing by Genetic and PSO Algorithms
Amin Kamalinia Ali GhaffariCloud computing makes it possible for users to use different applications through the internet without having to install them. Cloud computing is considered to be a novel technology which is aimed at handling and providing online services. For enhancing efficiency in cl MoreCloud computing makes it possible for users to use different applications through the internet without having to install them. Cloud computing is considered to be a novel technology which is aimed at handling and providing online services. For enhancing efficiency in cloud computing, appropriate task scheduling techniques are needed. Due to the limitations and heterogeneity of resources, the issue of scheduling is highly complicated. Hence, it is believed that an appropriate scheduling method can have a significant impact on reducing makespans and enhancing resource efficiency. Inasmuch as task scheduling in cloud computing is regarded as an NP complete problem; traditional heuristic algorithms used in task scheduling do not have the required efficiency in this context. With regard to the shortcomings of the traditional heuristic algorithms used in job scheduling, recently, the majority of researchers have focused on hybrid meta-heuristic methods for task scheduling. With regard to this cutting edge research domain, we used HEFT (Heterogeneous Earliest Finish Time) algorithm to propose a hybrid meta-heuristic method in this paper where genetic algorithm (GA) and particle swarm optimization (PSO) algorithms were combined with each other. The results of simulation and statistical analysis of proposed scheme indicate that the proposed algorithm, when compared with three other heuristic and a memetic algorithms, has optimized the makespan required for executing tasks. Manuscript profile -
Open Access Article
7 - A Two-Stage Multi-Objective Enhancement for Fused Magnetic Resonance Image and Computed Tomography Brain Images
Leena Chandrashekar A Sreedevi AsundiMagnetic 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 MoreMagnetic 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 -
Open Access Article
8 - Evaluation of Pattern Recognition Techniques in Response to Cardiac Resynchronization Therapy (CRT)
Mohammad Nejadeh Peyman Bayat Jalal Kheirkhah Hassan MoladoustCardiac 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 MoreCardiac 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 -
Open Access Article
9 - Improvement of Firefly Algorithm using Particle Swarm Optimization and Gravitational Search Algorithm
Mahdi TouraniEvolutionary algorithms are among the most powerful algorithms for optimization, Firefly algorithm (FA) is one of them that inspired by nature. It is an easily implementable, robust, simple and flexible technique. On the other hand, Integration of this algorithm with ot MoreEvolutionary algorithms are among the most powerful algorithms for optimization, Firefly algorithm (FA) is one of them that inspired by nature. It is an easily implementable, robust, simple and flexible technique. On the other hand, Integration of this algorithm with other algorithms, can be improved the performance of FA. Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) are suitable and effective for integration with FA. Some method and operation in GSA and PSO can help to FA for fast and smart searching. In one version of the Gravitational Search Algorithm (GSA), selecting the K-best particles with bigger mass, and examining its effect on other masses has a great help for achieving the faster and more accurate in optimal answer. As well as, in Particle Swarm Optimization (PSO), the candidate answers for solving optimization problem, are guided by local best position and global best position to achieving optimal answer. These operators and their combination with the firefly algorithm (FA) can improve the performance of the search algorithm. This paper intends to provide models for improvement firefly algorithm using GSA and PSO operation. For this purpose, 5 scenarios are defined and then, their models are simulated using MATLAB software. Finally, by reviewing the results, It is shown that the performance of introduced models are better than the standard firefly algorithm. Manuscript profile -
Open Access Article
10 - A Hybrid Approach based on PSO and Boosting Technique for Data Modeling in Sensor Networks
hadi shakibian Jalaledin NasiriAn 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 MoreAn 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 -
Open Access Article
11 - Multi-Objective Particle Swarm Classifier
Seyed-Hamid ZahiriA 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 MoreA 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 -
Open Access Article
12 - A Two-Stage Method for Classifiers Combination
S. H. Nabavi Karizi E. KabirEnsemble learning is an effective machine learning method that improves the classification performance. In this method, the outputs of multiple classifiers are combined so that the better results can be attained. As different classifiers may offer complementary informat MoreEnsemble learning is an effective machine learning method that improves the classification performance. In this method, the outputs of multiple classifiers are combined so that the better results can be attained. As different classifiers may offer complementary information about the classification, combining classifiers, in an efficient way, can achieve better results than any single classifier. Combining multiple classifiers is only effective if the individual classifiers are accurate and diverse. In this paper, we propose a two-stage method for classifiers combination. In the first stage, by mixture of experts strategy we produce different classifiers and in the second stage by using particle swarm optimization (PSO), we find the optimal weights for linear combination of them. Experimental results on different data sets show that proposed method outperforms the independent training and mixture of experts methods. Manuscript profile -
Open Access Article
13 - An Intelligent BGSA Based Method for Feature Selection in a Persian Handwritten Digits Recognition System
N. Ghanbari S. M. Razavi S. H. Nabavi KariziIn 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 MoreIn 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 -
Open Access Article
14 - Economical Optimization of Capacity and Operational Strategy for Combined Heat and Power Systems
M. HajinazariAn optimization method has been developed to determine the optimal capacities for the CHP and boiler such that thermal and electrical energy demands can be satisfied with high cost efficiency. The proposed method offers an operational strategy in order to determine the MoreAn optimization method has been developed to determine the optimal capacities for the CHP and boiler such that thermal and electrical energy demands can be satisfied with high cost efficiency. The proposed method offers an operational strategy in order to determine the optimum value for boiler and CHP capacities which maximize an objective function based on the net present value (NPV). The reduction in operational strategy expenses arising from the monetary cost of the credit attainable by air pollution reduction is also taken into account in evaluation of the objective function. The optimal value for boiler and CHP capacities and the resulting projection for the optimal value of the objective function are derived using a hybrid optimization method involving the particle swarm optimization (PSO) and the linear programming algorithms. The viability of the proposed method is demonstrated by analyzing the decision to construct a CHP system for a typical hospital. Manuscript profile -
Open Access Article
15 - Risk-based Static and Dynamics Security Assessment and Its Enhancement with Particle Swarm Optimization Generation Realloca
M. Saeedi H. SeifiSecurity assessment is traditionally checked using a deterministic criterion. Based on that, the system may be considered as secured or unsecured. If an unsecured condition is detected, preventive actions are foreseen to make it secure. Recently, risk based security as MoreSecurity assessment is traditionally checked using a deterministic criterion. Based on that, the system may be considered as secured or unsecured. If an unsecured condition is detected, preventive actions are foreseen to make it secure. Recently, risk based security assessment is used in power systems. In this paper, risk-based static and dynamic security assessment is proposed and a new transient stability index is defined. In this paper, the risk index is used as an objective function in the generation reallocation algorithm. In this algorithm, the security is maintained using the generation reallocation. The algorithm is tested on IEEE 24-bus test system and its capabilities are assessed in comparison with a traditional OPF, in which the security is maintained based on a deterministic criterion. Particle Swarm Optimization (PSO) algorithm is used as the optimization tool. Manuscript profile -
Open Access Article
16 - Designing Optimal Fuzzy Classifier Using Particle Swarm Optimization
Seyed-Hamid ZahiriAn important issue in designing a fuzzy classifier is setting its structural and mathematical fuzzy parameters (e.g., number of rules, antecedents, consequents, types and locations of membership functions). In fact, the variations of these parameters establish a wide MoreAn important issue in designing a fuzzy classifier is setting its structural and mathematical fuzzy parameters (e.g., number of rules, antecedents, consequents, types and locations of membership functions). In fact, the variations of these parameters establish a wide range high dimensional search space, which makes heuristic methods some suitable candidates to solve this problem (designing optimal fuzzy parameters). In this paper, a method is described for this purpose. In presented technique, all fuzzy parameters of a fuzzy classifier, are interpreted in structure of particles and PSO algorithm is employed to find the optimal one. Extensive experimental results on well-known benchmarks and practical pattern recognition problem (automatic target recognition) demonstrate the effectiveness of the proposed method. Manuscript profile -
Open Access Article
17 - Multi Objective Network Reconfiguration for Distribution System with Micro-Grids Power Exchange using Max-Min Fuzzy Method and Particle Swarm Optimization Algorithm
A. Fattahi Meyabadi H. SohrabianiA group of small generators and energy storages in the low or medium voltage distribution systems beside of consumers emerge to a new power system called micro grid. Micro grids are designed to have secure and economic operation isolated and connected to the network and MoreA group of small generators and energy storages in the low or medium voltage distribution systems beside of consumers emerge to a new power system called micro grid. Micro grids are designed to have secure and economic operation isolated and connected to the network and exchange electrical energy with distribution system. Hence, they may impact on planning and scheduling of distribution systems. In this case, network reconfiguration is a considerable issue after presenting of micro grids to the system. In the previous studies regarding to this issue, micro grid is considered as a distributed generation which should only produce electricity to the network. In this paper, micro grid is modeled as a power exchanger in the distribution network to study the effect of it on the network reconfiguration. For this purpose, reconfiguration is formulated as a multi objective optimization problem using max-min fuzzy method. In this problem, power loss reduction and load balancing among feeders are two independent objectives and voltage profile, lines congestion, radial network structure and load flow are equality and inequality constraints. Particle swarm algorithm is applied to solve the optimization problem and the reconfiguration over two 33 and 70 buses IEEE test network is shown. Results demonstrate that replacing traditional distribution systems by modern active networks and exchanging power with micro grids can lead to increase the reliability of system and more economic operation. Manuscript profile -
Open Access Article
18 - 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 -
Open Access Article
19 - Optimal and Simultaneously Compensation of Active, and Reactive Powers in Power System Using of Plug in Electric Vehicle
f. rashidi H. Feshki FarahaniPlug in electric vehicles besides environment pollution reduction can help power system operation. One of the most important capabilities of them is providing activeand reactive power. This paper considers grid constraints, technical concerns and market price and propos MorePlug in electric vehicles besides environment pollution reduction can help power system operation. One of the most important capabilities of them is providing activeand reactive power. This paper considers grid constraints, technical concerns and market price and proposes a framework to allocate the PEV capacity such that operational cost paid by distribution system operator (DSO) to power provider of active and reactive power is minimized. For this purpose, an objective function is defined that includes the payment for each power provider. This objective function is minimized based on particle swarm optimization subject to grid and vehicles constraints. In this framework, the PEVs compete with generator to produce active and reactive power. In order to accelerate the optimization process and prevent the algorithm from being trapped in local optima, new heuristic approaches are included to the original PSO algorithm. To evaluate the effectiveness of the propose method, it is implemented on the low voltage with 134 customer and including the other power providers and the amount of each participants production and payment cost to each component is determined. Manuscript profile -
Open Access Article
20 - Optimal Design of Six-Phase Radial Flux Permanent Magnet Synchronous Generator for Small Scale Wind Turbine Applications
M. E. Moazzen S. A. GholamianThis 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 MoreThis 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 -
Open Access Article
21 - Energy-Aware Data Gathering in Rechargeable Wireless Sensor Networks Using Particle Swarm Optimization Algorithm
Vahideh Farahani Leili Farzinvash Mina Zolfy Lighvan Rahim Abri LighvanThis paper investigates the problem of data gathering in rechargeable Wireless Sensor Networks (WSNs). The low energy harvesting rate of rechargeable nodes necessitates effective energy management in these networks. The existing schemes did not comprehensively examine t MoreThis paper investigates the problem of data gathering in rechargeable Wireless Sensor Networks (WSNs). The low energy harvesting rate of rechargeable nodes necessitates effective energy management in these networks. The existing schemes did not comprehensively examine the important aspects of energy-aware data gathering including sleep scheduling, and energy-aware clustering and routing. Additionally, most of them proposed greedy algorithms with poor performance. As a result, nodes run out of energy intermittently and temporary disconnections occur throughout the network. In this paper, we propose an energy-efficient data gathering algorithm namely Energy-aware Data Gathering in Rechargeable wireless sensor networks (EDGR). The proposed algorithm divides the original problem into three phases namely sleep scheduling, clustering, and routing, and solves them successively using particle swarm optimization algorithm. As derived from the simulation results, the EDGR algorithm improves the average and standard deviation of the energy stored in the nodes by 17% and 5.6 times, respectively, compared to the previous methods. Also, the packet loss ratio and energy consumption for delivering data to the sink of this scheme is very small and almost zero Manuscript profile -
Open Access Article
22 - Multi-Label Feature Selection Using a Hybrid Approach Based on the Particle Swarm Optimization Algorithm
َAzar Rafiei Parham Moradi Abdolbaghi GhaderzadehMulti-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 MoreMulti-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 -
Open Access Article
23 - Identification of Transfer Function Parameters of Brushless DC Motor Using Particle Swarm Algorithm
Ahmad Shirzadi Arash Dehestani Kolagar Mohammad Reza Alizadeh PahlavaniSo far, comprehensive and extensive studies have been conducted on the brushless DC motor (BLDC), and a part of these studies focuses on the estimation of the parameters of the transfer function of this motor. Estimation of BLDC motor transfer function parameters is ess MoreSo far, comprehensive and extensive studies have been conducted on the brushless DC motor (BLDC), and a part of these studies focuses on the estimation of the parameters of the transfer function of this motor. Estimation of BLDC motor transfer function parameters is essential to study motor performance and predict its behavior. Therefore, an efficient, accurate and reliable parameter estimation method is needed. In this article, the problem of estimating the parameters of the transfer function of the inverter-fed BLDC motor set has been solved using particle swarm algorithms (PSO). The results of using this algorithm have been compared with the results of other optimization algorithms. The comparison of these results has shown that the PSO algorithm is an efficient, accurate and reliable method for solving the transfer function parameter estimation problem. Manuscript profile -
Open Access Article
24 - Improving resource allocation in mobile edge computing using gray wolf and particle swarm optimization algorithms
seyed ebrahim dashti saeid shabooeiMobile 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 MoreMobile 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 -
Open Access Article
25 - Improving Resource Allocation in Mobile Edge Computing Using Particle Swarm and Gray Wolf Optimization Algorithms
seyed ebrahim dashti saeid shabooeiMobile 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 MoreMobile 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
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