Problem-specific encoding and genetic operation for a multi-objective deployment and power assignment problem in wireless sensor networks

Authors:
Andreas Konstantinidis;Kun Yang;Qingfu Zhang
Affiliations:
School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK;School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK;School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
Venue:
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Year:
2009

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Abstract

Wireless Sensor Networks Deployment and Power Assignment Problems (DPAPs) for maximizing the network coverage and lifetime respectively, have received increasing attention recently. Classical approaches optimize these two objectives individually, or by combining them together in a single objective, or by constraining one and optimizing the other. In this paper, the two problems are formulated as a multi-objective DPAP and tackled simultaneously. Problem-specific encoding representation and genetic operators are designed for the DPAP and a Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) is specialized. The multi-objective DPAP is decomposed into many scalar subproblems which are solved simultaneously by using neighborhood information and network knowledge. Simulation results have shown the effectiveness of the proposed evolutionary components by providing a high quality set of alternative solutions without any prior knowledge on the objectives preference, and the superiority of our problem-specific MOEA/D approach against a state of the art MOEA.