Path Optimization and Object Localization Using Hybrid Particle Swarm and Ant Colony Optimization for Mobile RFID Reader
Abstract
This paper proposes a hybrid approach of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) for the mobile Radio Frequency Identification System (RFID) reader to get the shortest path for object localization. In this approach, we have adopted the ACO global pheromone updating information of ants to guide the update velocities and position for PSO based on nearest neighbor constraints. The pheromone information is used efficiently to guide the selection of each particle in a search space of its visits. The best path will be used for mobile RFID reader for objects localization in search space. Simulation results show that the method is effective, minimizing the number of visited nodes for a mobile RFID reader.
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