The energy-efficiency coverage of wireless sensor network is measure by the network cover rate and the node redundancy rate. To solve this multi-objective optimization problem, a multi-objective quantum-inspired cultural algorithm is proposed, which adopts the dual structure to effectively utilize the implicit knowledge extracted from the non-dominating individuals set to promote more efficient search. It has three highlights. One is the rectangle's height of each allele is calculated by non-dominated sort among individuals. The second is the crowding degree that records the density of non-dominated individuals in the topological cell measure the uniformity of the Pareto-optimal set instead of the crowding distance. The third is the update operation of quantum individuals and the selection operator are directed by the knowledge. Simulation results indicate that the layout of wireless sensor network obtained by this algorithm have larger network cover rate and less node redundancy rate.
['Yi-nan Guo', 'Dandan Liu', 'Meirong Chen', 'Yun Liu']
An energy-efficient coverage optimization method for the wireless sensor networks based on multi-objective quantum-inspired cultural algorithm