Genetic algorithm with random and memory immigrant strategies for solving dynamic load balanced clustering problem in wireless sensor networks

Abstract

In Wireless Sensor Networks (WSNs), clustering is an effective method to distribute the load equally among all the nodes as compared to flat network architecture. Due to the dynamic nature of the network, the clustering process can be viewed as a dynamic optimization problem and the conventional computational intelligence techniques are not enough to solve these problems. The Dynamic Genetic Algorithm (DGA) addresses these problems with the help of searching optimal solutions in new environments. Therefore the dynamic load-balanced clustering process is modeled using the basic components of standard genetic algorithm and then the model is enhanced is using immigrants and memory-based schemes to elect suitable cluster heads. The metrics nodes’ residual energy level, node centrality, and mobility speed of the nodes are considered to elect the load-balanced cluster heads and the optimal number of cluster members are assigned to each cluster head using the proposed DGA schemes such as Random Immigrants Genetic Approach (RIGA), Memory Immigrants Genetic Approach (MIGA), and Memory and Random Immigrants Genetic Approach (MRIGA). The simulation results show that the proposed DGA scheme MRIGA outperforms well as compared with RIGA and MIGA in terms of various performance metrics such as the number of nodes alive, residual energy level, packet delivery ratio, end-to-end delay, and overhead for the formation of clusters.