Cooperative artificial bee colony algorithm for multi-objective RFID network planning

Radio frequency identification (RFID) is rapidly growing into an important technology for object identification and tracking applications. This gives rise to the most challenging RFID network planning (RNP) problem in the large-scale RFID deployment environment. RNP has been proven to be an NP-hard problem that involves many objectives and constraints. The application of evolutionary and swarm intelligence algorithms for solving multi-objective RNP (MORNP) has gained significant attention in the literature, while these proposed methods always transform multi-objective RNP into single-objective problem by the weighted coefficient approach. In this work, we propose a cooperative multi-objective artificial colony algorithm called CMOABC to find all the Pareto optimal solutions and to achieve the optimal planning solutions by simultaneously optimizing four conflicting objectives in MORNP. The experiment presents an exhaustive comparison of the proposed CMOABC and two successful multi-objective techniques, namely the recently developed multi-objective artificial bee colony algorithm (MOABC) and nondominated sorting genetic algorithm II (NSGA-II), on instances of different nature, namely the two-objective and three-objective MORNP in the large-scale RFID scenario. Simulation results show that CMOABC proves to be superior for planning RFID networks compared to NSGA-II and MOABC in terms of optimization accuracy and computation robustness.