Optimization of Wireless Sensor Networks Based on Differential Evolution Algorithm

To study the optimization problem of wireless sensor network (WSN) based on differential evolution, the single objective differential evolution algorithm is applied and combined with the advantages and disadvantages crossover strategy. Firstly, the path optimization problem in WSN is analyzed, and the optimization model is established. Then, the differential evolution algorithm is used as the search tool to solve the minimum energy consumption in the path optimization model, that is, the optimal path problem. Finally, the comparison experiment is carried out on the classical algorithm genetic algorithm (GA), particle swarm optimization (PSO) and standard differential evolution (DE) algorithm. The results show that the performance of differential evolution algorithm based on crossover strategy is superior to or not worse than that of several contrast algorithms. It can be seen that the differential evolution algorithm based on advantage and disadvantage crossover strategy has good effectiveness.