Towards Aid by Generate and Solve Methodology: Application in the Problem of Coverage and Connectivity in Wireless Sensor Networks

Placido Rogerio Pinheiro, Andre Luis Vasconcelos Coelho, Alexei Barbosa Aguiar and Alvaro de Menezes Sobreira Neto

International Journal of Distributed Sensor Networks, 2012, vol. 8, issue 10, 790459

Abstract: The integrative collaboration of genetic algorithms and integer linear programming as specified by the Generate and Solve methodology tries to merge their strong points and has offered significant results when applied to wireless sensor networks domains. The Generate and Solve (GS) methodology is a hybrid approach that combines a metaheuristics component with an exact solver. GS has been recently introduced into the literature in order to solve the problem of dynamic coverage and connectivity in wireless sensor networks, showing promising results. The GS framework includes a metaheuristics engine (e.g., a genetic algorithm) that works as a generator of reduced instances of the original optimization problem, which are, in turn, formulated as mathematical programming problems and solved by an integer programming solver.

Date: 2012
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1155/2012/790459 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:8:y:2012:i:10:p:790459

DOI: 10.1155/2012/790459

Access Statistics for this article

More articles in International Journal of Distributed Sensor Networks
Bibliographic data for series maintained by SAGE Publications ().