WSN node coverage optimization algorithm based on global and neighborhood difference DE

Abstract

Wireless sensor networks are widely used in today's fields, such as scientific research, industry and agriculture. However, due to the influence of its geographical location and the problems of low coverage and waste of resources caused by random placement, it is very important to adopt appropriate strategies to improve its coverage. To this end, an improved GND-DE (Global and Neighborhood Difference Guided DE) algorithm is proposed. This algorithm uses both the global topology structure and the neighborhood topology structure, combined with the evaluation of contemporary optimization results, and selects the results from the two topology structures. The value-dominant individual, the individual to be evolved and the two dominant individuals calculate the difference operator corresponding to the two topological structures; a diversity neighborhood topology is proposed for the creation of the neighborhood topology; at the same time, the algorithm step size factor F is adaptively adjusted and the JADE external archive mutation strategy is introduced to eliminate the possibility of algorithm search stagnation. In order to verify the effectiveness of its improved algorithm, compared with other mainstream improved algorithms on the CEC2017 test set, it shows that its optimization efficiency and convergence are better than other comparison algorithms; finally, GND-DE is applied to WSN node coverage optimization, which proves the feasibility of its optimization strategy.