List of Articles particle swarm algorithm

• Open Access Article
  • Abstract Page
  • Full-Text
  
  1 - Application of clustering in AODV routing protocol for intercity networks on the highway scenario
  amin feyzi Vahid Sattari-Naeini majid mohammadi
  Intercarous networks are a subset of mobile networks in which vehicles are considered as network nodes. The main difference with case mobile networks is the rapid mobility of nodes, which causes rapid topology change in this network It becomes. Rapid changes in network More

  Intercarous networks are a subset of mobile networks in which vehicles are considered as network nodes. The main difference with case mobile networks is the rapid mobility of nodes, which causes rapid topology change in this network It becomes. Rapid changes in network topology are a major challenge for routing, for routing in these networks, routing protocols must be robust and reliable. One of the well-known routing protocols in intercity networks is the AODV routing protocol. The application of this routing protocol on intercity networks also has problems that increase the number of control messages in the network by increasing the scale of the network and the number of nodes. One way to reduce overhead in the AODV protocol is to cluster network nodes. In this paper, the modified K-Means algorithm is used to cluster the nodes and the particle swarm algorithm is used to select the cluster head. The results of the proposed method improve the normal routing load and increase the packet delivery rate compared to the AODV routing protocol. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  2 - Lifetime Improvement Using Cluster Head Selection and Base Station Localization in Wireless Sensor Networks
  maryam najimi Sajjad  Nankhoshki
  10.7508/jist.2018.02.006
  The limited energy supply of wireless sensor networks poses a great challenge for the deployment of wireless sensor nodes. In this paper, a sensor network of nodes with wireless transceiver capabilities and limited energy is considered. Clustering is one of the most eff More

  The limited energy supply of wireless sensor networks poses a great challenge for the deployment of wireless sensor nodes. In this paper, a sensor network of nodes with wireless transceiver capabilities and limited energy is considered. Clustering is one of the most efficient techniques to save more energy in these networks. Therefore, the proper selection of the cluster heads plays important role to save the energy of sensor nodes for data transmission in the network. In this paper, we propose an energy efficient data transmission by determining the proper cluster heads in wireless sensor networks. We also obtain the optimal location of the base station according to the cluster heads to prolong the network lifetime. An efficient method is considered based on particle swarm algorithm (PSO) which is a nature inspired swarm intelligence based algorithm, modelled after observing the choreography of a flock of birds, to solve a sensor network optimization problem. In the proposed energy- efficient algorithm, cluster heads distance from the base station and their residual energy of the sensors nodes are important parameters for cluster head selection and base station localization. The simulation results show that our proposed algorithm improves the network lifetime and also more alive sensors are remained in the wireless network compared to the baseline algorithms in different situations. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  3 - Permeability estimation using petrophysical logs and artificial intelligence methods: A case study in the Asmari reservoir of Ahvaz oil field
  Abouzar Mohsenipour Bahman Soleimani iman Zahmatkesh Iman  Veisi
  Permeability is one of the most important petrophysical parameters that play a key role in the discussion of production and development of hydrocarbon fields. In this study, first, the magnetic resonance log in Asmari reservoir was evaluated and permeability was calcula More

  Permeability is one of the most important petrophysical parameters that play a key role in the discussion of production and development of hydrocarbon fields. In this study, first, the magnetic resonance log in Asmari reservoir was evaluated and permeability was calculated using two conventional methods, free fluid model (Coates) and Schlumberger model or mean T2 (SDR). Then, by constructing a simple model of artificial neural network and also combining it with Imperialist competition optimization (ANN-ICA) and particle swarm (ANN-PSO) algorithms, the permeability was estimated. Finally, the results were compared by comparing the estimated COATES permeability and SDR permeability with the actual value, and the estimation accuracy was compared in terms of total squared error and correlation coefficient. The results of this study showed an increase in the accuracy of permeability estimation using a combination of optimization algorithms with artificial neural network. The results of this method can be used as a powerful method to obtain other petrophysical parameters. Manuscript profile