On a vector space representation in genetic algorithms for sensor scheduling in wireless sensor networks.
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Date
2014
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Abstract
Recent works raised the hypothesis that the assignment of a geometry to the decision
variable space of a combinatorial problem could be useful both for providingmeaningful
descriptions of the fitness landscape and for supporting the systematic construction
of evolutionary operators (the geometric operators) that make a consistent usage of
the space geometric properties in the search for problem optima. This paper introduces
some new geometric operators that constitute the realization of searches along the combinatorial
space versions of the geometric entities descent directions and subspaces.
The new geometric operators are stated in the specific context of the wireless sensor network
dynamic coverage and connectivity problem (WSN-DCCP). A genetic algorithm
(GA) is developed for the WSN-DCCP using the proposed operators, being compared
with a formulation based on integer linear programming (ILP) which is solved with
exact methods. That ILP formulation adopts a proxy objective function based on the
minimization of energy consumption in the network, in order to approximate the objective
of network lifetime maximization, and a greedy approach for dealing with the
system’s dynamics. To the authors’ knowledge, the proposed GA is the first algorithm
to outperform the lifetime of networks as synthesized by the ILP formulation, also
running in much smaller computational times for large instances.
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Keywords
Wireless sensor networks, Dynamic optimization, Genetic algorithms
Citation
MARTINS, F. V. C. et al. On a vector space representation in genetic algorithms for sensor scheduling in wireless sensor networks. Evolutionary Computation, v. 22, p. 361-403, 2014. Disponível em: <http://www.mitpressjournals.org/doi/abs/10.1162/EVCO_a_00112?url_ver=Z39.88-2003&rfr_id=ori%3Arid%3Acrossref.org&rfr_dat=cr_pub%3Dpubmed&>. Acesso em: 28 jul. 2017.