Effect of parameters variation on the performance of particle swarm optimization algorithm for tag coverage problem of radio frequency identification network
Optimal tag coverage is the most crucial aspect for deploying RFID (Radio Frequency Identification) system in a large scale. From the literature, optimal tag coverage can be considered as a high dimensional optimization problem and often solved using nature-inspired algorithms. In this paper, PSO (P...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Asian Research Publishing Network (ARPN)
2016
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/3621/1/AJ%202017%20%28502%29.pdf http://eprints.uthm.edu.my/3621/ |
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| Summary: | Optimal tag coverage is the most crucial aspect for deploying RFID (Radio Frequency Identification) system in a large scale. From the literature, optimal tag coverage can be considered as a high dimensional optimization problem and often solved using nature-inspired algorithms. In this paper, PSO (Particle Swarm Optimization) algorithm is used to optimize the tag coverage problem. This paper also investigates the effect of varying two parameters of PSO (swarm size and iteration number) to the performance of the algorithm. During the simulation sessions, both parameters are set at 50, 100, 150 and 200. Next, sets of comparison were made. From the experiment, the best set of results is generated when the swarm size is set at 200 and the iteration number is at 50. This is very encouraging because for the iteration number at 50, the runtime is much less (4.9s) compared to the higher iteration numbers (100, 150 and 200). The percentages for additional runtimes for iteration number set at 100, 150 and 200 are 103%, 204% and 341% respectively. |
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