摘要 : At present, wireless sensor networks (WSNs) are widely used due to their low-cost devices and its easy deployment Such networks have an important constraint: WSNs are particularly sensitive to energy consumption. This paper focuse... 展开 At present, wireless sensor networks (WSNs) are widely used due to their low-cost devices and its easy deployment Such networks have an important constraint: WSNs are particularly sensitive to energy consumption. This paper focuses on how to add routers to a previously established static WSN to simultaneously optimize two important features: energy consumption and covered area. This np-hard multi-objective optimization problem was solved by evolutionary computation, considering two well-known multi-objective genetic algorithms (NSGA-II and SPEA2), and a multi-objective adaptation of a novel swarm intelligence algorithm based on the behaviour of an artificial bee colony (ABC). The results obtained are analyzed in depth by means of a widely used statistical methodology that considers two multi-objective quality indicators: hypervolume and set coverage. We conclude that ABC provides better performance than standards NSGA-II and SPEA2. In addition, a comparative study is added where the gains in fitness values from including relay nodes are shown. 收起