TY  - JOUR
T1  - Indoor Localization Using Artificial Bee Colony with Levy Flight
TT  - Indoor Localization Using Artificial Bee Colony with Levy Flight
AU  - Saleh, Radhwan Ali Abdulghani
AU  - Akay, Rüştü
PY  - 2019
DA  - October
DO  - 10.31590/ejosat.637712
JF  - Avrupa Bilim ve Teknoloji Dergisi
JO  - EJOSAT
PB  - Osman SAĞDIÇ
WT  - DergiPark
SN  - 2148-2683
SP  - 152
EP  - 156
LA  - en
AB  - 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).
KW  - Indoor localization
KW  - artificial bee colony
KW  - levy flight
KW  - neural network
KW  - metaheuristics
KW  - multi-layer perceptron
N2  - Using Wi-Fi signal strengthfor detecting objects in an indoor environment has different types ofapplications, such as, locating perpetrator in finite areas, and detecting thenumber of users on an access point. In this work, we propose a hybridoptimization algorithm to train training Multi-Layer Perceptron Neural Network thatcould be distributed in monitoring and tracking devices used for determiningthe location of users based on the Wi-Fi signal strength which their personaldevices receive. This hybrid algorithm combines Artificial Bee Colony (ABC) andLevy Flight (LF) algorithm, called ABCLF. ABCLF increases the exploration andexploitation capabilities of ABC so that it can be used efficiency for thepurpose of training Multi-Layer Perceptron “MLP” Neural Network. The proposedABCLF algorithm guarantees the enhancing of accuracy with the increasing in iterations because it hasthe powerfull of the frame work of Artificial Bee Colony algorithm “threephases whith different strategies of searching” and the powerfull of the LevyFlight local search algorithm which has been used in both onlooker bee phase with a short step size walk to guarantee the enhancing in theexploitation and in scout bee phase with a long step size walk to guarantee the enhancing inthe exploration. The results of ourstudy show that the classification accuracy of the trained neural network usingABCLF is better than the other evolutionary algorithms used in this study forthe same purpose like ABC, Genetic Algorithm (GA), Biogeography-BasedOptimization (BBO), Probability Based Incremental Learning (PBIL) and ParticleSwarm Optimization (PSO).
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UR  - https://doi.org/10.31590/ejosat.637712
L1  - https://dergipark.org.tr/tr/download/article-file/838904
ER  -