Chaotic crossover artificial bee colony algorithm for coverage optimization of wireless sensor networks
In order to improve the coverage performance of wireless sensor networks,the artificial bee colony algorithm is used to optimize the sensor node layout strategy,and chaotic crossover of nectar source coordinate components is carried out to enhance the optimization ability of the artificial bee colony algorithm.Firstly,an appropriate number of sensor nodes is selected according to the target region,and an initial coverage model is established for the target region.Then,the artificial bee colony algorithm is used to optimize the coverage model,and the coverage rate is selected as the fitness function,and the coordinates of all sensor nodes randomly distributed are taken as the initial nectar source positions.Then,the candidate nectar source is searched by the probe bees to obtain the coordinates of the sensor nodes with higher fitness,and the candidate nectar source coordinate components are chaotically optimized.The nectar source with the highest fitness is obtained by components optimization of the following bees.Finally,the optimal nectar source coordinates are output,which are coordinate values of all sensor nodes in the target region.The experimental results show that,compared with the other three algorithms,the chaotic crossover artificial bee colony algorithm can achieve a higher coverage rate if the colony size and iteration times are set properly.
万方数据
维普
