List of Articles بهینه‌سازی

• Open Access Article
  • Abstract Page
  • Full-Text
  
  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
• Open Access Article
  • Abstract Page
  • Full-Text
  
  2 - Using a Hybrid PSO-GA Method for Capacitor Placement in Distribution Systems
  mohammadmahdi Varahram amir mohammadi
  20.1001.1.27170411.1393.6.20.1.9
  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
• Open Access Article
  • Abstract Page
  • Full-Text
  
  3 - Using a multi-objective optimization algorithm for tasks allocate in the cloud-based systems to reduce energy consumption
  sara tabaghchimilan nima jafari novimipour
  20.1001.1.27170414.1399.12.43.5.6
  Nowadays, new technologies have increased the demand for business in the web environment.Increasing demand will increase the variety and number of services. As a result, the creation of large-scale computing data centers has high operating costs and consumes huge amount More

  Nowadays, new technologies have increased the demand for business in the web environment.Increasing demand will increase the variety and number of services. As a result, the creation of large-scale computing data centers has high operating costs and consumes huge amounts of electrical power. On the other hand, inadequate and inadequate cooling systems not only cause excessive heating of resources and shorten the life of the machines. It also produces carbon that plays an important role in the weather. Therefore, they should reduce the total energy consumption of these systems with proper methods. In this research, an efficient energy management approach is provided in virtual cloud data centers, which reduces energy consumption and operational costs, and brings about an increase in the quality of services. It aims to provide a resource allocation strategy for cloud systems with the goal of reducing energy, cost of implementation and examining its use in cloud computing. The results of the simulation show that the proposed method in comaprision to NPA, DVFS, ST and MM methods can reduce the average energy consumption up to 0.626 kWh, also the need to immigration and SLA violation declined up to 186 and 30.91% respectively. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  4 - Joint Power Allocation Optimization of cooperative communication systems with Non-Orthogonal Multiple Access
  Hamid AmiriAra mohamadbagher masrur mohamadreza zahabi
  20.1001.1.27170411.1399.12.45.8.8
  In this paper, a downlink network with two users as transmitter and relay, respectively, and a central station as a receiver is considered. The aim is to determine the optimal coefficient of non-orthogonal signal symbols and the optimal power allocation in the source-re More

  In this paper, a downlink network with two users as transmitter and relay, respectively, and a central station as a receiver is considered. The aim is to determine the optimal coefficient of non-orthogonal signal symbols and the optimal power allocation in the source-relay in order to maximize the average total rate in a cooperative communication system using the non-orthogonal multiple access technique. To achieve these goals, the average total rate of the cooperative communication system with non-orthogonal multiple access with decode and forward relay in the independent Rayleigh channel was calculated. Then, in the first step, the optimization problem of the non-orthogonal symbols coefficient is mathematically expressed for each power allocation and a closed form solution is proposed. In the second step, the power allocation optimization for the source-relay was introduced and solved. Finally, the joint optimization problem of the non-orthogonal symbols coefficient and power allocation is investigated and an algorithm proposed for the joint optimization problem. The proposed algorithm shows that the joint optimization of the non-orthogonal symbols coefficient and power allocation achieve a higher average rate than the separate optimization of each of these parameters. Also, simulations and numerical results are presented to confirm the theoretical equation, where the simulations show the 3 dB gain for the optimized system using the proposed algorithm compared to the non-optimized system. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  5 - A Task Mapping and Scheduling Algorithm based on Genetic Algorithm for Embedded System Design
  mohadese nikseresht Mohsen Raji
  20.1001.1.27170414.1400.13.47.12.8
  Embedded system designers face numerous design requirements and objectives (such as runtime, power consumption and reliability). Since meeting one of these requirements mostly contradicts other design requirements, it seem to be inevitable to apply multi-objective appr More

  Embedded system designers face numerous design requirements and objectives (such as runtime, power consumption and reliability). Since meeting one of these requirements mostly contradicts other design requirements, it seem to be inevitable to apply multi-objective approaches in various stages of designing embedded systems, including task scheduling step. In this paper, a multi-objective task mapping and scheduling in the design stage of the embedded system is presented. In this method, tasks are represented by task graphs assuming that the hardware architecture platform is given and determined. In order to manage the dependencies between tasks in the task graph, a segmentation method is used, in which the tasks that can be executed simultaneously are specified in a segment and is considered in the scheduling process. In the proposed method, the task mapping and scheduling problem is modeled as a genetic algorithm-based multi-objective optimization problem considering execution time, energy consumption, and reliability. In comparison to similar previous works, the proposed scheduling method respectively provides 21.4%, 19.2%, and 20% improvement in execution time, energy consumption, and reliability. Applying a multi-objective helps the designer to pick out the best outcome according to different considerations. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  6 - Stock market prediction using optimized grasshopper optimization algorithm and time series algorithms
  Vahid Safari dehnavi masoud shafiee
  20.1001.1.27170411.1401.14.53.8.8
  Stock 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 More

  Stock 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
  • Abstract Page
  • Full-Text
  
  7 - 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