Genetic and backtracking search optimisation algorithms applied to localisation problems. (2015)

• Record Type: Journal Article
• Authors: Sá, Alan Oliveira de; Nedjah, Nadia; Mourelle, Luiza de Macedo
• Is Part Of: International journal of innovative computing and applications. Volume 6:Number 3/4(2015)
• Page Start: 223
• Page End: 228
• Publication Date: 2015
• Subjects: genetic algorithms -- backtracking search optimisation -- localisation -- wireless sensor networks -- WSNs -- swarm robotics -- robot swarms -- distance measurements -- anchor nodes -- robot location -- node location; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

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.