Evolutionary algorithms for cluster heads election in wireless sensor networks: Performance comparison

Clustering sensor nodes into groups is an efficient topology control approach for achieving long-term operation of Wireless Sensor Networks (WSNs). The performance of clustering is greatly influenced by the selection of Cluster Heads (CHs), which are in charge of creating clusters and controlling member nodes. Finding the optimal set of CHs is known to be non-deterministic polynomial (NP)-hard problem for a WSN. Evolutionary computation approaches can be applied to find fast and efficient solutions to such problems. In this paper, the problem of CHs election is formulated as a single-objective optimization problem, aiming to obtain clusters that maximize the network energy efficiency and link quality. The formulated problem has been solved using three Evolutionary approaches: Genetic Algorithms (GA), Differential Evolution (DE) and Particle Swarm Optimization (PSO). Their performance has been compared in terms of the achieved fitness value. In addition, the performance of the proposed protocol is evaluated and compared to well-known cluster-based sensor network protocols.