Implementation of Wireless Sensor Charging Route Planning Based on Multi-Strategies Frilled Lizard Optimization

This paper presents an improved method to solve the problems of wireless sensor charging route planning by using a K-means algorithm and an improved Frilled Lizard Optimization. The improved method uses the K-means algorithm to cluster the data of wireless sensor nodes first. Then, an improved swarm intelligence algorithm generates corresponding Hamiltonian cycles for the clustered units and finally combines each Hamiltonian cycle to generate the final path planning for wireless sensor charging. The experimental simulations have proven that the K-means clustering algorithm reduces the time and space complexity in solving the problem, and the improved swarm intelligence algorithm has a shorter running time, lower energy consumption and higher efficiency than existing algorithms.