Genetic Algorithm Based Energy Aware Clustering Algorithm for Underwater Wireless Sensor Network

Genetic algorithms (GAs) offer a powerful and effective method for solving optimization problems due to their ability to search and explore large spaces of possible solutions. A GA-based energy aware clustering algorithm is proposed in [1] to improve the energy efficiency of wireless sensor networks by optimizing the assignment of nodes to clusters. The proposed algorithm minimizes the energy consumption of the system while guaranteeing a certain Quality of Service (QoS). It works by encoding the clustering process in a chromosome consisting of the node assignments for each target cluster, and then uses attributed-based crossover and mutation operators to generate new chromosome individuals. The algorithm employs an innovative fitness function that not only takes into account the energy costs associated with each target cluster, but also takes into account any QoS constraints imposed by the users. Experiments show that the proposed algorithm is able to significantly reduce the energy consumption of the network with minimal QoS degradation.