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Genetic and Backtracking Search Optimization Algorithms Applied to Localization Problems

  • Alan Oliveira de Sá
  • Nadia Nedjah
  • Luiza de Macedo Mourelle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8583)

Abstract

The localization 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 Optimization Algorithm(PSO), have been used. Accordingly, in order to obtain better solutions to the localization problem, this paper presents the results obtained with the Backtracking Search Optimization Algorithm (BSA) and compares them with those obtained with the GA.

Keywords

Genetic Algorithm Global Position System Particle Swarm Optimization Wireless Sensor Network Global Position System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Alan Oliveira de Sá
    • 1
  • Nadia Nedjah
    • 2
  • Luiza de Macedo Mourelle
    • 3
  1. 1.Center of Electronics, Communications and Information TechnologyAdmiral Wandenkolk Instruction Center, Brazilian NavyRio de JaneiroBrazil
  2. 2.Department of Electronics Engineering and Telecommunication, Engineering FacultyState University of Rio de JaneiroBrazil
  3. 3.Department of System Engineering and Computation, Engineering FacultyState University of Rio de JaneiroBrazil

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