Title: Genetic and backtracking search optimisation algorithms applied to localisation problems

Authors: Alan Oliveira de Sá; Nadia Nedjah; Luiza de Macedo Mourelle

Addresses: Center of Electronics, Communications and Information Technology, Admiral Wandenkolk Instruction Center, Brazilian Navy, Rio de Janeiro, Brazil ' Department of Electronics Engineering and Telecommunication, Engineering Faculty, State University of Rio de Janeiro, Brazil ' Department of System Engineering and Computation, Engineering Faculty, State University of Rio de Janeiro, Brazil

Abstract: The localisation problem arises from the need of the elements of a swarm of robots, or of a wireless sensor network (WSN), to determine its position without the use of external references, such as the global positioning system (GPS), for example. In this problem, the location is based on calculations that use distance measurements to anchor nodes that have known positions. In the search for efficient algorithms to calculate the location, some algorithms inspired by nature, such as genetic algorithm (GA) and particle swarm optimisation (PSO) algorithm, have been used. Accordingly, in order to obtain better solutions to the localisation problem, this paper presents the results obtained with the backtracking search optimisation algorithm (BSA) and compares them with those obtained with the GA.

Keywords: genetic algorithms; backtracking search optimisation; localisation; wireless sensor networks; WSNs; swarm robotics; robot swarms; distance measurements; anchor nodes; robot location; node location.

DOI: 10.1504/IJICA.2015.072973

International Journal of Innovative Computing and Applications, 2015 Vol.6 No.3/4, pp.223 - 228

Received: 29 Oct 2014
Accepted: 29 Oct 2014

Published online: 11 Nov 2015 *

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