Indoor Localization Using Artificial Bee Colony with Levy Flight

Using Wi-Fi signal strength for detecting objects in an indoor environment has different types of applications, such as, locating perpetrator in finite areas, and detecting the number of users on an access point. In this work, we propose a hybrid optimization algorithm to train training Multi-Layer Perceptron Neural Network that could be distributed in monitoring and tracking devices used for determining the location of users based on the Wi-Fi signal strength which their personal devices receive. This hybrid algorithm combines Artificial Bee Colony (ABC) and Levy Flight (LF) algorithm, called ABCLF. ABCLF increases the exploration and exploitation capabilities of ABC so that it can be used efficiency for the purpose of training Multi-Layer Perceptron “MLP” Neural Network. The proposed ABCLF algorithm guarantees the enhancing of accuracy with the increasing in iterations because it has the powerfull of the frame work of Artificial Bee Colony algorithm “three phases whith different strategies of searching” and the powerfull of the Levy Flight local search algorithm which has been used in both onlooker bee phase with a short step size walk to guarantee the enhancing in the exploitation and in scout bee phase with a long step size walk to guarantee the enhancing in the exploration . The results of our study show that the classification accuracy of the trained neural network using ABCLF is better than the other evolutionary algorithms used in this study for the same purpose like ABC, Genetic Algorithm (GA), Biogeography-Based Optimization (BBO), Probability Based Incremental Learning (PBIL) and Particle Swarm Optimization (PSO).