One of the most promising algorithms for network optimization is the particle swarm optimization (PSO) and genetic algorithm (GA). The paper is about comparing these two as applied to wireless sensor networks. If a sink is placed at a longer distance from the sensors then the battery life (energy) drains faster, and it reduces the life of the network. Our analysis shows that optimized clustering technique of sensors can minimize the communication distance and can help to increase the network stability. GA and PSO can optimize the cluster formation of sensors. Simulation results have shown us that PSO performs better than GA for clustering algorithms in wireless sensor networks.
CITATION STYLE
Parwekar, P., Rodda, S., & Vani Mounika, S. (2018). Comparison between genetic algorithm and PSO for wireless sensor networks. In Smart Innovation, Systems and Technologies (Vol. 77, pp. 403–411). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-10-5544-7_39
Mendeley helps you to discover research relevant for your work.