Comparison of metaheuristic optimization algorithms for RSS-based 3-D visible light positioning systems

In this work we present a comparison between two classical metaheuristic optimization algorithms to solve two different received signal strength (RSS) based fitness function formulations for 3-D visible light positioning systems. For this purpose, simulations have been carried out to analyze the performance of genetic algorithm (GA) and particle swarm optimization (PSO) methods for mobile node’s position determination. Based on simulation results, GA overcomes PSO. GA shows to be more robust than PSO and it is capable to provide feasible optimal solutions when no map information is added as constraints to the optimization problem. When map information is used as lower and upper bound constraints, GA and PSO methods increase their accuracy and precision significantly.