Scalability of a novel shifting balance theory-based optimization algorithm: A comparative study on a cluster-based wireless sensor network Conference Paper


Author(s): Yang, Erfu; Barton, Nicholas H; Arslan, Tughrul; Erdogan, Ahmet T
Title: Scalability of a novel shifting balance theory-based optimization algorithm: A comparative study on a cluster-based wireless sensor network
Title Series: LNCS
Affiliation
Abstract: Scalability is one of the most important issues for optimization algorithms used in wireless sensor networks (WSNs) since there are often many parameters to be optimized at the same time. In this case it is very hard to ensure that an optimization algorithm can be smoothly scaled up from a low-dimensional optimization problem to the one with a high dimensionality. This paper addresses the scalability issue of a novel optimization algorithm inspired by the Shifting Balance Theory (SBT) of evolution in population genetics. Toward this end, a cluster-based WSN is employed in this paper as a benchmark to perform a comparative study. The total energy consumption is minimized under the required quality of service by jointly optimizing the transmission power and rate for each sensor node. The results obtained by the SBT-based algorithm are compared with the Metropolis algorithm (MA) and currently popular particle swarm optimizer (PSO) to assess the scaling performance of the three algorithms against the same WSN optimization problem.
Conference Title: IECS: International Conference on Evolvable Systems
Volume: 5216
Conference Dates: September 21-24, 2008
Conference Location: Prague, Czech Republic
ISBN: 978-3-540-85856-0
Publisher: Springer  
Location: Berlin, Heidelberg
Date Published: 2008-09-08
Start Page: 249
End Page: 260
DOI: 10.1007/978-3-540-85857-7_22
Open access: no
IST Austria Authors
  1. Nick Barton
    252 Barton
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