Abstract
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).
Highlights
The localization problem has attracted many researchers’ attention recently
First modification gives more chance to the best solution to update itself by increasing the number of scout bees, the second modification improves the convergence by the experience of the global best solution reached so far, the third modification helps in exploitation by retaining the acquired experience of the swarm about the search area, while the fourth modification is taking advantage in exploration of the search space
The inputs to the NN are the seven features of wireless signal strengths measured from the diverse routers
Summary
The localization problem has attracted many researchers’ attention recently. Location-based services, e.g. location-based recommender systems, are one of the major motivations behind this problem. First modification gives more chance to the best solution to update itself by increasing the number of scout bees, the second modification improves the convergence by the experience of the global best solution reached so far, the third modification helps in exploitation by retaining the acquired experience of the swarm about the search area, while the fourth modification is taking advantage in exploration of the search space Another study [13] balances the diversity and convergence capability of the ABC by applying three modifications on ABC as the following, incorporating ABC by Lévy Flight Local Search after scout phase in order to enhance the capability of ABC’s exploitation, incorporating opposition based learning to avoid the lull situation of the algorithm and increase the convergence speed, and the last modification is by changing the food sources search equation to that one used in Gbest-guided ABC [13]. We will train the NN for indoor localization classification using Genetic Algorithm (GA), Ant Colony Optimization (ACO), Evolutionary Strategy (ES), Biogeography-Based Optimization (BBO), Probability Based Incremental Learning (PBIL), Particle Swarm Optimization (PSO), ABC and ABCLF and perform a comparison using a dataset found in [1]
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