Improved WSN Coverage Model and Related Fly Visual Evolutionary Neural Network
The random deployment of sensor nodes in WSN easily causes the problem of the low network coverage and network con-nectivity,which directly influences the WSN's service quality.It is still a scientific and technological challenge to construct sensor node deployment planning models and probe into the related approaches.Therefore,this work develops an improved WSN coverage optimization model and the related fly visual evolutionary neural network optimization approach.In the design of the model,the con-nectivity index is used to ensure the connectivity of the network,while the regular triangle method is employed to construct a constraint condition so that the uniform deployment of nodes in the region can be achieved.Afterwards,a single-objective constrained optimiza-tion model is derived based on the weighed coefficient approach.In the design of the algorithm,an improved fly visual evolutionary neural network optimization algorithm is proposed to handle constrained optimization problems and WSN coverage problems.Therein,an improved fly visual neural network able to handle constraints is developed to output global and local learning rates,based on the two mechanisms of visual attention and visual information-processing of the fly visual system;a state update strategy is constructed to up-date current states,after the basic dung beetle optimization approach is improved by means of the Levy flight strategy and a dynamical-ly adjustable nonlinear boundary selection factor.The comparative experiments have validated that,not only the acquired optimization approach is strongly competitive,but also the integration of the visual information processing mechanism and metaheuristics is of great potential to solving constrained optimization problems.
NETL
NSTL
万方数据
