RFID network planning using a multi-swarm optimizer

In this paper, we develop an optimization model for planning the positions of readers in the RFID network based on a novel multi-swarm particle swarm optimizer called PS2O. The main idea of PS2O is to extend the single population PSO to the interacting multi-swarms model by constructing hierarchical interaction topology and enhanced dynamical update equations. This algorithm, which is conceptually simple and easy to implement, has considerable potential for solving complex optimization problems. With five mathematical benchmark functions, PS2O is proved to have significantly better performance than five successful variants of PSO. PS2O is then used for solving the real-world RFID network planning problem. Simulation results show that the proposed algorithm proves to be superior for planning RFID networks than canonical PSO, multi-swarm cooperative PSO (MCPSO), and two evolutionary algorithms, namely genetic algorithm with elitism (EGA) and self-adaptive evolution strategies (SA-ES), in terms of optimization accuracy and computation robustness.