Localization in Wireless Sensor Networks by Hybridization between Optimization Algorithms of Particle Swarms and Fruit Flies

The work presented in this paper falls within the general framework of wireless sensor networks. We focus on the study and implementation of new distributed algorithms that use metaheuristics biologically inspired to solve the localization problem in WSN. In this work, we proposed a hybrid optimization algorithm between two metaheuristics: the fruit fly optimization (FOA) and the particle swarm optimization (PSO) algorithms, to improve the accuracy of localization in a network of sensors with a two-dimensional area of interest. To validate the performance of our proposed approach, we carried out experiments and compared them with optimization algorithms by particle swarms and fruit flies, the results obtained showed the superiority of our proposal.