Optimization Strategy of Wireless Charger Node Deployment Based on Improved Cuckoo Search Algorithm

Abstract

In wireless rechargeable sensor network, the deployment of charger node directly affects the overall charging utility of sensor network. Aiming at this problem, this paper abstracts the charger deployment problem as a multi-objective optimization problem that maximizes the received power of sensor nodes and minimizes the number of charger nodes. First, a network model that maximizes the sensor node received power and minimizes the number of charger nodes is constructed. Second, an Improved Cuckoo Search (ICS) algorithm is proposed. This algorithm is based on the traditional Cuckoo Search algorithm (CS) to redefine its step factor, and then use the mutation factor to change the nesting position of the host bird to update the bird ’s nest position, and then use ICS to find the ones that maximize the received power of the sensor node and minimize the number of charger nodes optimal solution. Compared with the traditional cuckoo search algorithm and multi-objective particle swarm optimization algorithm, the simulation results show that the algorithm can effectively increase the receiving power of sensor nodes, reduce the number of charger nodes and find the optimal solution to meet the conditions, so as to maximize the network charging utility.

Research on DV-Hop localization algorithm of sensor nodes optimized by cuckoo search algorithm based on quantum behavior

“…At the same time, the best relay node is selected as the data forwarding node to achieve multi-hop data transmission. The algorithm greatly reduces the network energy consumption, achieves the effect of improving the overall network lifetime, and broadens the application field of wireless sensor networks.In order to solve the problem of uneven distribution of random deployment in traditional sensor networks, literature [7] proposed to use cuckoo search algorithm to optimize node deployment. Firstly, three improved algorithms, Momentum-CS, RMSprop-CS and Adam-CS, were proposed to optimize and adjust the step control quantity and elimination probability in CS algorithm.…”

Improved Simultaneous Localization and Mapping Estimation using Crow Search Algorithm Based Particle Filter

“…Huang et al [15], and Chen et al [16], cuckoo and bat algorithms were introduced to have better performance of the particle filtering algorithm. Gao et al [17] used the firefly algorithm transfer formula to recombine the particle sample.…”