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Open Access Article
1 - Comparing A Hybridization of Fuzzy Inference System and Particle Swarm Optimization Algorithm with Deep Learning to Predict Stock Prices
Majid Abdolrazzagh-Nezhad mahdi kheradPredicting stock prices by data analysts have created a great business opportunity for a wide range of investors in the stock markets. But the fact is difficulte, because there are many affective economic factors in the stock markets that they are too dynamic and compl MorePredicting stock prices by data analysts have created a great business opportunity for a wide range of investors in the stock markets. But the fact is difficulte, because there are many affective economic factors in the stock markets that they are too dynamic and complex. In this paper, two models are designed and implemented to identify the complex relationship between 10 economic factors on the stock prices of companies operating in the Tehran stock market. First, a Mamdani Fuzzy Inference System (MFIS) is designed that the fuzzy rules set of its inference engine is found by the Particle Swarm Optimization Algorithm (PSO). Then a Deep Learning model consisting of 26 neurons is designed wiht 5 hidden layers. The designed models are implemented to predict the stock prices of nine companies operating on the Tehran Stock Exchange. The experimental results show that the designed deep learning model can obtain better results than the hybridization of MFIS-PSO, the neural network and SVM, although the interperative ability of the obtained patterns, more consistent behavior with much less variance, as well as higher convergence speed than other models can be mentioned as significant competitive advantages of the MFIS-PSO model Manuscript profile -
Open Access Article
2 - Speech Intelligibility Improvement in Noisy Environments for Near-End Listening Enhancement
Peyman Goli Mohammad Reza Karami-MollaeiA new speech intelligibility improvement method for near-end listening enhancement in noisy environments is proposed. This method improves speech intelligibility by optimizing energy correlation of one-third octave bands of clean speech and enhanced noisy speech without MoreA new speech intelligibility improvement method for near-end listening enhancement in noisy environments is proposed. This method improves speech intelligibility by optimizing energy correlation of one-third octave bands of clean speech and enhanced noisy speech without power increasing. The energy correlation is determined as a cost function based on frequency band gains of the clean speech. Interior-point algorithm which is an iterative procedure for the nonlinear optimization is used to determine the optimal points of the cost function because of nonlinearity and complexity of the energy correlation function. Two objective intelligibility measures, speech intelligibility index and short-time objective intelligibility measure, are employed to evaluate the noisy enhanced speech intelligibility. Furthermore, the speech intelligibility scores are compared with unprocessed speech and a baseline method under various noisy conditions. The results show large intelligibility improvements with the proposed method over the unprocessed noisy speech. Manuscript profile -
Open Access Article
3 - 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
4 - Data Aggregation Tree Structure in Wireless Sensor Networks Using Cuckoo Optimization Algorithm
Elham Mohsenifard Behnam TalebiWireless sensor networks (WSNs) consist of numerous tiny sensors which can be regarded as a robust tool for collecting and aggregating data in different data environments. The energy of these small sensors is supplied by a battery with limited power which cannot be rech MoreWireless sensor networks (WSNs) consist of numerous tiny sensors which can be regarded as a robust tool for collecting and aggregating data in different data environments. The energy of these small sensors is supplied by a battery with limited power which cannot be recharged. Certain approaches are needed so that the power of the sensors can be efficiently and optimally utilized. One of the notable approaches for reducing energy consumption in WSNs is to decrease the number of packets to be transmitted in the network. Using data aggregation method, the mass of data which should be transmitted can be remarkably reduced. One of the related methods in this approach is the data aggregation tree. However, it should be noted that finding the optimization tree for data aggregation in networks with one working-station is an NP-Hard problem. In this paper, using cuckoo optimization algorithm (COA), a data aggregation tree was proposed which can optimize energy consumption in the network. The proposed method in this study was compared with genetic algorithm (GA), Power Efficient Data gathering and Aggregation Protocol- Power Aware (PEDAPPA) and energy efficient spanning tree (EESR). The results of simulations which were conducted in matlab indicated that the proposed method had better performance than GA, PEDAPPA and EESR algorithm in terms of energy consumption. Consequently, the proposed method was able to enhance network lifetime. Manuscript profile -
Open Access Article
5 - 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
6 - An Automatic Thresholding Approach to Gravitation-Based Edge Detection in Grey-Scale Images
Hamed Agahi Kimia RezaeiThis paper presents an optimal auto-thresholding approach for the gravitational edge detection method in grey-scale images. The goal of this approach is to enhance the performance measures of the edge detector in clean and noisy conditions. To this aim, an optimal thres MoreThis paper presents an optimal auto-thresholding approach for the gravitational edge detection method in grey-scale images. The goal of this approach is to enhance the performance measures of the edge detector in clean and noisy conditions. To this aim, an optimal threshold is automatically found, according to which the proposed method dichotomizes the pixels to the edges and non-edges. First, some pre-processing operations are applied to the image. Then, the vector sum of the gravitational forces applied to each pixel by its neighbors is computed according to the universal law of gravitation. Afterwards, the force magnitude is mapped to a new characteristic called the force feature. Following this, the histogram representation of this feature is determined, for which an optimal threshold is aimed to be discovered. Three thresholding techniques are proposed, two of which contain iterative processes. The parameters of the formulation used in these techniques are adjusted by means of the metaheuristic grasshopper optimization algorithm. To evaluate the proposed system, two standard databases were used and multiple qualitative and quantitative measures were utilized. The results confirmed that the methodology of our work outperformed some conventional and recent detectors, achieving the average precision of 0.894 on the BSDS500 dataset. Moreover, the outputs had high similarity to the ideal edge maps. Manuscript profile -
Open Access Article
7 - Reducing Energy Consumption in Sensor-Based Internet of Things Networks Based on Multi-Objective Optimization Algorithms
Mohammad sedighimanesh Hessam Zandhessami Mahmood Alborzi Mohammadsadegh KhayyatianEnergy is an important parameter in establishing various communications types in the sensor-based IoT. Sensors usually possess low-energy and non-rechargeable batteries since these sensors are often applied in places and applications that cannot be recharged. The mos MoreEnergy is an important parameter in establishing various communications types in the sensor-based IoT. Sensors usually possess low-energy and non-rechargeable batteries since these sensors are often applied in places and applications that cannot be recharged. The most important objective of the present study is to minimize the energy consumption of sensors and increase the IoT network's lifetime by applying multi-objective optimization algorithms when selecting cluster heads and routing between cluster heads for transferring data to the base station. In the present article, after distributing the sensor nodes in the network, the type-2 fuzzy algorithm has been employed to select the cluster heads and also the genetic algorithm has been used to create a tree between the cluster heads and base station. After selecting the cluster heads, the normal nodes become cluster members and send their data to the cluster head. After collecting and aggregating the data by the cluster heads, the data is transferred to the base station from the path specified by the genetic algorithm. The proposed algorithm was implemented with MATLAB simulator and compared with LEACH, MB-CBCCP, and DCABGA protocols, the simulation results indicate the better performance of the proposed algorithm in different environments compared to the mentioned protocols. Due to the limited energy in the sensor-based IoT and the fact that they cannot be recharged in most applications, the use of multi-objective optimization algorithms in the design and implementation of routing and clustering algorithms has a significant impact on the increase in the lifetime of these networks. Manuscript profile -
Open Access Article
8 - A New High-Capacity Audio Watermarking Based on Wavelet Transform using the Golden Ratio and TLBO Algorithm
Ali Zeidi joudaki Marjan Abdeyazdan Mohammad Mosleh Mohammad KheyrandishDigital watermarking is one of the best solutions for copyright infringement, copying, data verification, and illegal distribution of digital media. Recently, the protection of digital audio signals has received much attention as one of the fascinating topics for resear MoreDigital watermarking is one of the best solutions for copyright infringement, copying, data verification, and illegal distribution of digital media. Recently, the protection of digital audio signals has received much attention as one of the fascinating topics for researchers and scholars. In this paper, we presented a new high-capacity, clear, and robust audio signaling scheme based on the DWT conversion synergy and golden ratio advantages using the TLBO algorithm. We used the TLBO algorithm to determine the effective frame length and embedded range, and the golden ratio to determine the appropriate embedded locations for each frame. First, the main audio signal was broken down into several sub-bands using a DWT in a specific frequency range. Since the human auditory system is not sensitive to changes in high-frequency bands, to increase the clarity and capacity of these sub-bands to embed bits we used the watermark signal. Moreover, to increase the resistance to common attacks, we framed the high-frequency bandwidth and then used the average of the frames as a key value. Our main idea was to embed an 8-bit signal simultaneously in the host signal. Experimental results showed that the proposed method is free from significant noticeable distortion (SNR about 29.68dB) and increases the resistance to common signal processing attacks such as high pass filter, echo, resampling, MPEG (MP3), etc. Manuscript profile -
Open Access Article
9 - ARASP: An ASIP Processor for Automated Reversible Logic Synthesis
Zeinab Kalantari Marzieh Gerami Mohammad eshghiReversible logic has been emerged as a promising computing paradigm to design low power circuits in recent years. The synthesis of reversible circuits is very different from that of non-reversible circuits. Many researchers are studying methods for synthesizing reversib MoreReversible logic has been emerged as a promising computing paradigm to design low power circuits in recent years. The synthesis of reversible circuits is very different from that of non-reversible circuits. Many researchers are studying methods for synthesizing reversible combinational logic. Some automated reversible logic synthesis methods use optimization algorithms Optimization algorithms are used in some automated reversible logic synthesis techniques. In these methods, the process of finding a circuit for a given function is a very time-consuming task, so it’s better to design a processor which speeds up the process of synthesis. Application specific instruction set processors (ASIP) can benefit the advantages of both custom ASIC chips and general DSP chips. In this paper, a new architecture for automatic reversible logic synthesis based on an Application Specific Instruction set Processors is presented. The essential purpose of the design was to provide the programmability with the specific necessary instructions for automated synthesis reversible. Our proposed processor that we referred to as ARASP is a 16-bit processor with a total of 47 instructions, which some specific instruction has been set for automated synthesis reversible circuits. ARASP is specialized for automated synthesis of reversible circuits using Genetic optimization algorithms. All major components of the design are comprehensively discussed within the processor core. The set of instructions is provided in the Register Transform Language completely. Afterward, the VHDL code is used to test the proposed architecture. Manuscript profile -
Open Access Article
10 - Wind Farm Layout Optimization with Emphasis on the Wake Effect
A. Farajipoor F. Faghihi R. SharifiConstruction of wind farms rise for wind energy capture as a renewable energy around the world. The purpose of wind farm layout optimization, absorb maximum energy from wind farms. In this paper, a new hybrid algorithm is presented to maximize the expected energy output MoreConstruction of wind farms rise for wind energy capture as a renewable energy around the world. The purpose of wind farm layout optimization, absorb maximum energy from wind farms. In this paper, a new hybrid algorithm is presented to maximize the expected energy output. Considerations of algorithm wake loss, which is based on wind turbine location and wind direction. The proposed model is illustrated with a scenario of the wind speed and its direction distribution of windy sites and is compared with ant colony algorithm and evolutionary strategy algorithm in six steps layout. The results show that the combination of ant colony algorithm and genetic algorithm performs better than existing strategies based on maximum values of the expected energy output and wake loss. Manuscript profile -
Open Access Article
11 - 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
12 - Improved BIRCH Clustering by Chemical Reaction Optimization Algorithm to Health Fraud Detection
M. Abdolrazzagh-Nezhad M. KheradWith regard to the scale of the financial transactions and the extent of the healthcare industry, it is one of the ideal systems for fraud. Therefore, suitable identifying fraud data is still one of the challenges facing the healthcare providers, although there are seve MoreWith regard to the scale of the financial transactions and the extent of the healthcare industry, it is one of the ideal systems for fraud. Therefore, suitable identifying fraud data is still one of the challenges facing the healthcare providers, although there are several fraud detection algorithms. In the paper, the BIRCH clustering algorithm, as one hierarchical clustering algorithm, is hybridized with a chemical reaction optimization algorithm (CRO). The BIRCH with linear time complexity is able for clustering large scale data and identifying their noises and the CRO, as one of new meta-heuristic algorithm inspired by the chemical reactions in the real world, explores the search space with a dynamic population size based on four reactions such as on-wall ineffective collision, decomposition, inter-molecular ineffective collision and synthesis. Due to the improved BIRCH-CRO removes the internal clustering process of the classic BIRCH and determines the optimal values of its main parameters, it causes that the computational time decreases and accuracy and precision of detecting fraud data increase since its experimental results is compared with the exist unsupervised algorithms. Also, the proposed fraud detection algorithm has the ability to perform on online data and large scale data, and given the obtained results, it provides a proper performance. Manuscript profile -
Open Access Article
13 - Quality of Service Aware Service Composition Method Using Biogeography-Based Optimization (BBO) Algorithm
S. Saligheh B. ArastehFast development in the utilization of cloud computing leads to publishing more cloud services on the cloud environment. The single and simple services cannot satisfy the users’ real-world complex requirements. To create a complex service, it is necessary to select and MoreFast development in the utilization of cloud computing leads to publishing more cloud services on the cloud environment. The single and simple services cannot satisfy the users’ real-world complex requirements. To create a complex service, it is necessary to select and compose a set of simple services. Therefore, it is essential to embed a service composition system in cloud computing environment. Service composition is one of the important NP-hard problems in the service-oriented computings. In this paper, a biogeography-based optimization algorithm is used to create the optimal composite-services. The proposed method was simulated and executed on five different scenarios with different number of tasks and candidate services. The throughput of the proposed method, genetic algorithm and particle swarm optimization algorithm are respectively 0.9997, 0.9975 and 0.9994; furthermore, the reliability of these methods are respectively 0.9993, 0.9980 and 0.9982. The results of simulations indicate that the proposed method outperforms the previous methods in the same conditions in terms of throughput, successability, reliability, response time, and stability. Manuscript profile -
Open Access Article
14 - Performance Improvement of Polynomial Neural Network Classifier using Whale Optimization Algorithm
Mahsa Memari A. Harifi a. KhaliliPolynomial neural network (PNN) is a supervised learning algorithm which is one of the most popular models used in real applications. The architectural complexity of polynomial neural network in terms of both number of partial descriptions (PDs) and number of layers, le MorePolynomial neural network (PNN) is a supervised learning algorithm which is one of the most popular models used in real applications. The architectural complexity of polynomial neural network in terms of both number of partial descriptions (PDs) and number of layers, leads to more computation time and more storage space requirement. In general, it can be said that the architecture of the polynomial neural networks is very complex and it requires large memory and computation time. In this research, a novel approach has been proposed to improve the classification performance of a polynomial neural network using the Whale Optimization Algorithm (PNN-WOA). In this approach, the PDs are generated at the first layer based on the combination of two features. The second layer nodes consists of PDs generated in the first layer, input variables and bias. Finally, the polynomial neural network output is obtained by sum of weighted values of the second layer outputs. Using the Whale Optimization Algorithm (WOA), the best vector of weighting coefficients will be obtained in such a way that the PNN network reach to the highest classification accuracy. Eleven different dataset from UCI database has been used as input data of proposed PNN-WOA and the results has been presented. The proposed method outperforms state-of-the-art approaches such as PNN-RCGA, PNN-MOPPSO, RCPNN-PSO and S-TWSVM in most cases. For datasets, an improvement of accuracy between 0.18% and 10.33% can be seen. Also, the results of the Friedman test indicate the statistical superiority of the proposed PNN-WOA model compared to other methods with p value of 0.039. Manuscript profile -
Open Access Article
15 - A Novel Elite-Oriented Meta-Heuristic Algorithm: Qashqai Optimization Algorithm (QOA)
Mehdi Khadem Abbas Toloie Eshlaghy Kiamars Fathi HafshejaniOptimization problems are becoming more complicated, and their resource requirements are rising. Real-life optimization problems are often NP-hard and time or memory consuming. Nature has always been an excellent pattern for humans to pull out the best mechanisms and th MoreOptimization problems are becoming more complicated, and their resource requirements are rising. Real-life optimization problems are often NP-hard and time or memory consuming. Nature has always been an excellent pattern for humans to pull out the best mechanisms and the best engineering to solve their problems. The concept of optimization seen in several natural processes, such as species evolution, swarm intelligence, social group behavior, the immune system, mating strategies, reproduction and foraging, and animals’ cooperative hunting behavior. This paper proposes a new Meta-Heuristic algorithm for solving NP-hard nonlinear optimization problems inspired by the intelligence, socially, and collaborative behavior of the Qashqai nomad’s migration who have adjusted for many years. In the design of this algorithm uses population-based features, experts’ opinions, and more to improve its performance in achieving the optimal global solution. The performance of this algorithm tested using the well-known optimization test functions and factory facility layout problems. It found that in many cases, the performance of the proposed algorithm was better than other known meta-heuristic algorithms in terms of convergence speed and quality of solutions. The name of this algorithm chooses in honor of the Qashqai nomads, the famous tribes of southwest Iran, the Qashqai algorithm. Manuscript profile -
Open Access Article
16 - Algorithmic Optimization of Midrise Residential Building Plans Based on Space Syntax Theory
sahba hasibi Ali Andaji GarmarodiThe space syntax and geometric layout, influenced by a wide array of explicit and implicit parameters and lead to multiple solutions for design problems, are one of the primary steps for architectural designs. Two of the main challenges faced in this subject are: utiliz MoreThe space syntax and geometric layout, influenced by a wide array of explicit and implicit parameters and lead to multiple solutions for design problems, are one of the primary steps for architectural designs. Two of the main challenges faced in this subject are: utilizing the computational power of computers to predict the space syntax of architectural plans and defining the problem in an Algorithmic language. The current study aims to present an algorithm for reaching a space syntax followed by the users' needs and preferences to form a meaningful connection between the houses and their residents and facilitate the user's participation in the design of midrise residential buildings. In the current study, multi-objective optimization was used to achieve space syntax designs based on multiple parameters. In order to optimize, a set of 200 manually layout design plans were used as input for the algorithm; the algorithm then generates plans for the midrise residential buildings based on criteria such as open and closed space area, requirements for orientations of various spaces, number of rooms. Then, numerous solutions are reviewed and compared, and the most suitable plan is chosen based on the results. Finally, the entire process of algorithm was tested for some case studies, and the results show a great capacity of the proposed method in providing space syntax plans with speed and variety. Manuscript profile -
Open Access Article
17 - Providing a Face Recognition System with an Optimal Selection of Features Based on the Cuckoo Optimization Algorithm
Farnaz Hoseini Hamed SepehrzadehFace recognition is a pattern recognition process that is specifically performed on faces. Face recognition has many applications in identifying credit cards, security systems, and other cases. Creating a face recognition system with high accuracy is a big challenge tha MoreFace recognition is a pattern recognition process that is specifically performed on faces. Face recognition has many applications in identifying credit cards, security systems, and other cases. Creating a face recognition system with high accuracy is a big challenge that has been the focus of various researchers in recent years. The feature extraction process and classification are two important issues in diagnosis systems that can play a significant role in increasing the accuracy of diagnosis. Considering this issue, in this study, taking into account the combined features and optimizing the cuckoo algorithm, a method to improve the accuracy of face recognition is proposed. In the presented method, seven features are extracted from the images in the database, and then by obtaining the feature vector, the steps related to feature selection are performed using the cuckoo algorithm. The proposed method has been implemented with MATLAB software and compared with other methods. The evaluation results show that the proposed method was able to perform the detection on the images of ORL and FDBB databases with 93.00% and 95.12% accuracy, respectively. The result obtained for this evaluation criterion has a higher value than other compared methods. Manuscript profile -
Open Access Article
18 - 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
19 - 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
20 - Multi-Objective Economic-Environment Scheduling of Microgrids in the Presence of Hybrid Electric Vehicles and Demand Response to Smooth the Distribution Nodal Prices
ali mirzaei NAVID TAGHIZADEGAN KALANTARI Sajad Najafi RavadaneghToday, with the growing demand for hybrid electric vehicles in microgrids, electricity supply, environmental issues, and rescheduling are among the challenges of microgrids that must be solved and suitable solutions provided. To overcome these challenges, this paper int MoreToday, with the growing demand for hybrid electric vehicles in microgrids, electricity supply, environmental issues, and rescheduling are among the challenges of microgrids that must be solved and suitable solutions provided. To overcome these challenges, this paper introduces a new multi-objective optimization model, which in the first objective, minimizes the total operation cost of the microgrid, and in the second objective, improves the reliability index by reducing the amount of energy not supplied. Due to these two objectives, a multi-objective evolutionary seagull optimization algorithm is used to find the optimal global solutions. In this regard, hybrid electric vehicles and demand response programs are used to smooth out distribution nodal prices and reduce CO2 emissions. The 69-bus distribution network has been used to evaluate the efficiency of the proposed method. Manuscript profile -
Open Access Article
21 - Stock market prediction using optimized grasshopper optimization algorithm and time series algorithms
Vahid Safari dehnavi masoud shafieeStock market prediction serves as an attractive and challenging field for researchers in financial markets. Many of the models used in stock market prediction are not able to predict accurately or these models require a large amount of input data, which increases the vo MoreStock market prediction serves as an attractive and challenging field for researchers in financial markets. Many of the models used in stock market prediction are not able to predict accurately or these models require a large amount of input data, which increases the volume of networks and learning complexity, all of which ultimately reduce the accuracy of forecasting. This article proposes a method for forecasting the stock market that can effectively predict the stock market. In this paper, the past market price is used to reduce the volume of input data and this data is placed in a regressor model. Manuscript profile -
Open Access Article
22 - A novel metaheuristic algorithm and its discrete form for influence maximizing in complex networks
Vahideh Sahargahi Vahid Majidnezhad Saeed Taghavi Afshord Bagher JafariIn light of the No Free Lunch (NFL) theorem, which establishes the inherent limitations of meta-heuristic algorithms in universally efficient problem solving, the ongoing quest for enhanced diversity and efficiency prompts the introduction of novel algorithms each year. MoreIn light of the No Free Lunch (NFL) theorem, which establishes the inherent limitations of meta-heuristic algorithms in universally efficient problem solving, the ongoing quest for enhanced diversity and efficiency prompts the introduction of novel algorithms each year. This research presents the IWOGSA meta-heuristic algorithm, a pioneering solution tailored for addressing continuous optimization challenges. IWOGSA ingeniously amalgamates principles from both the invasive weed optimization algorithm and the gravitational search algorithm, capitalizing on their synergies. The algorithm's key innovation lies in its dual-pronged sample generation strategy: a subset of samples follows a normal distribution, while others emulate the planetary motion-inspired velocities and accelerations from the gravitational search algorithm. Furthermore, a selective transfer of certain samples from distinct classes contributes to the evolution of succeeding generations. Expanding upon this foundation, a discrete variant of IWOGSA, termed DIWOGSA, emerges to tackle discrete optimization problems. The efficacy of DIWOGSA is demonstrated through its application to the intricate influence maximization problem. DIWOGSA distinguishes itself with an astute population initialization strategy and the integration of a local search operator to expedite convergence. Empirical validation encompasses a rigorous assessment of IWOGSA against established benchmark functions, composite functions, and real-world engineering structural design problems. Remarkably, the IWOGSA algorithm asserts its superiority, eclipsing both contemporary and traditional methods. This ascendancy is statistically affirmed through the utilization of the Friedman test rank, positioning IWOGSA as the premier choice. Also, DIWOGSA algorithm is evaluated by considering different networks for influence maximization problem, and it shows acceptable results in terms of influence and computational time in comparison to conventional algorithms. Manuscript profile -
Open Access Article
23 - Improving energy consumption in the Internet of Things using the Krill Herd optimization algorithm and mobile sink
Shayesteh TabatabaeiInternet of Things (IoT) technology involves a large number of sensor nodes that generate large amounts of data. Optimal energy consumption of sensor nodes is a major challenge in this type of network. Clustering sensor nodes into separate categories and exchanging info MoreInternet of Things (IoT) technology involves a large number of sensor nodes that generate large amounts of data. Optimal energy consumption of sensor nodes is a major challenge in this type of network. Clustering sensor nodes into separate categories and exchanging information through headers is one way to improve energy consumption. This paper introduces a new clustering-based routing protocol called KHCMSBA. The proposed protocol biologically uses fast and efficient search features inspired by the Krill Herd optimization algorithm based on krill feeding behavior to cluster the sensor nodes. The proposed protocol also uses a mobile well to prevent the hot spot problem. The clustering process at the base station is performed by a centralized control algorithm that is aware of the energy levels and position of the sensor nodes. Unlike protocols in other research, KHCMSBA considers a realistic energy model in the grid that is tested in the Opnet simulator and the results are compared with AFSRP (Artifical Fish Swarm Routing ProtocolThe simulation results show better performance of the proposed method in terms of energy consumption by 12.71%, throughput rate by 14.22%, end-to-end delay by 76.07%, signal-to-noise ratio by 82.82%. 46% compared to the AFSRP protocol Manuscript profile -
Open Access Article
24 - Fake Websites Detection Improvement Using Multi-Layer Artificial Neural Network Classifier with Ant Lion Optimizer Algorithm
Farhang Padidaran Moghaddam Mahshid Sadeghi B.In phishing attacks, a fake site is forged from the main site, which looks very similar to the original one. To direct users to these sites, Phishers or online thieves usually put fake links in emails and send them to their victims, and try to deceive users with social MoreIn phishing attacks, a fake site is forged from the main site, which looks very similar to the original one. To direct users to these sites, Phishers or online thieves usually put fake links in emails and send them to their victims, and try to deceive users with social engineering methods and persuade them to click on fake links. Phishing attacks have significant financial losses, and most attacks focus on banks and financial gateways. Machine learning methods are an effective way to detect phishing attacks, but this is subject to selecting the optimal feature. Feature selection allows only important features to be considered as learning input and reduces the detection error of phishing attacks. In the proposed method, a multilayer artificial neural network classifier is used to reduce the detection error of phishing attacks, the feature selection phase is performed by the ant lion optimization (ALO) algorithm. Evaluations and experiments on the Rami dataset, which is related to phishing, show that the proposed method has an accuracy of about 98.53% and has less error than the multilayer artificial neural network. The proposed method is more accurate in detecting phishing attacks than BPNN, SVM, NB, C4.5, RF, and kNN learning methods with feature selection mechanism by PSO algorithm. Manuscript profile -
Open Access Article
25 - Improving IoT resource management using fog calculations and ant lion optimization algorithm
payam shams Seyedeh Leili Mirtaheri reza shahbazian ehsan arianyanIn this paper, a model based on meta-heuristic algorithms for optimal allocation of IoT resources based on fog calculations is proposed. In the proposed model, the user request is first given to the system as a workflow; For each request, the resource requirements (proc MoreIn this paper, a model based on meta-heuristic algorithms for optimal allocation of IoT resources based on fog calculations is proposed. In the proposed model, the user request is first given to the system as a workflow; For each request, the resource requirements (processing power, storage memory, and bandwidth) are first extracted. This component determines the requested traffic status of the application in terms of real-time. If the application is not real-time and is somewhat resistant to latency, the request will be referred to the cloud environment, but if the application needs to respond promptly and is sensitive to latency, it will be dealt with as a fog calculation. It will be written to one of the Cloudletes. In this step, in order to select the best solution in allocating resources to serve the users of the IoT environment, the ant milk optimization algorithm was used. The proposed method is simulated in MATLAB software environment and to evaluate its performance, five indicators of fog cells energy consumption, response time, fog cell imbalance, latency and bandwidth have been used. The results show that the proposed method reduces the energy consumption, latency rate in fog cells, bandwidth consumption rate, load balance rate and response time compared to the base design (ROUTER) 22, 18, 12, 22 and 47, respectively. Percentage has improved. Manuscript profile -
Open Access Article
26 - 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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