Radhwan Ali Abdulghani SALEH, Rüştü AKAY

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, locatingperpetrator in finite areas, and detecting the number of users on an access point. In this work, we propose a hybrid optimization algorithmto train training Multi-Layer Perceptron Neural Network that could be distributed in monitoring and tracking devices used fordetermining the location of users based on the Wi-Fi signal strength which their personal devices receive. This hybrid algorithmcombines Artificial Bee Colony (ABC) and Levy Flight (LF) algorithm, called ABCLF. ABCLF increases the exploration andexploitation capabilities of ABC so that it can be used efficiency for the purpose of training Multi-Layer Perceptron “MLP” NeuralNetwork. The proposed ABCLF algorithm guarantees the enhancing of accuracy with the increasing in iterations because it has thepowerfull of the frame work of Artificial Bee Colony algorithm “three phases whith different strategies of searching” and the powerfullof the Levy Flight local search algorithm which has been used in both onlooker bee phase with a short step size walk to guarantee theenhancing in the exploitation and in scout bee phase with a long step size walk to guarantee the enhancing in the exploration. The resultsof our study show that the classification accuracy of the trained neural network using ABCLF is better than the other evolutionaryalgorithms 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).

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