A Modified Sunflower Optimization Algorithm for Wireless Sensor Networks

Abstract

Maximizing the lifetime of the wireless sensor networks (WSNs) is one of the biggest challenges due to the difficulty of changing their batteries when they run out of energy. Low Energy Adaptive Clustering Hierarchy (LEACH) is one of the most famous protocols which have applied to solve this problem. The main drawback of LEACH is that it may choose a cluster head that has less energy. Therefore, it will die in a short time and the network lifetime will finish rapidly. Many researchers have applied swarm intelligence algorithm to solve this problem however most of these algorithms trapped in local minima and suffer from premature convergence. In this paper, we combine the sunflower optimization algorithm (SFO) with the lèvy flight to maximize the WSNs lifetime. Such a combination can help the SFO algorithm to avoid trapping in local minima due to the random walk of the lèvy flight. The proposed algorithm is called a modified sunflower optimization algorithm (MSFO). To verify the superiority of the MSFO we compare it with five algorithms in literature for different numbers of nodes and cluster heads. The results show that the lifetime of the WSNs which is using the proposed MSFO is longer than their lifetime when they applied the other algorithms.