An analytical comparison of the performance between Firefly algorithm and Differential Evolution algorithm in wireless sensor networks

Abstract

Wireless Sensing Network is a major and interesting technology that is used in various applications such as monitoring inaccessible conditions in a particular area. Each node consists of a battery, transmitter, receiver, and processor. The battery cannot be replaced or recharged each time. Therefore, extending network life by reducing power consumption of the entire sensor nodes and load balancing are major challenges in WSN research. In this work, the performance of two algorithms, Firefly Algorithm (FA) and Differential Evolution (DE), was investigated. The performance of each of them was tested on randomly deployed sensor networks. The results show that the FA algorithm is better able to select the vertices of clusters in the network than the DE algorithm. Where the optimal selection of cluster heads leads to preserving live nodes in the network as much as possible and thus prolonging the network's work. FA is a suitable optimization tool in part because of the effect of the gravity function, which is unique to firefly behavior.