Multi-objective quantum cultural algorithm and its application in the wireless sensor networks' energy-efficient coverage optimization

Abstract

Whether wireless sensor network covers the target field availably is measured by the network cover rate and the node redundancy rate. To solve this multi-objective optimization problem, multi-objective quantum cultural algorithm is proposed. It effectively utilizes the implicit knowledge extracted from the non-domination individuals to promote more efficient search. Two highlights include: 1. The rectangle's height of each allele is calculated in accordant with the non-dominated sort among individuals. 2. The update operation of quantum individuals and the mutation operator are directed by the implicit knowledge. Taken a typical wireless sensor network with 25 sensor nodes as an example, simulation results indicate that the layout of wireless sensor network obtained by the proposed algorithm have larger network cover rate and lower node redundancy rate. © 2013 Springer-Verlag.