Using Multi-Objective Particle Swarm Optimization for Energy-Efficient Clustering in Wireless Sensor Networks

Abstract

In this chapter, the authors propose a multi-objective solution to the problem by using multi-objective particle swarm optimization (MOPSO) algorithm to optimize the number of clusters in a sensor network in order to provide an energy-efficient solution. The proposed algorithm considers the ideal degree of nodes and battery power consumption of the sensor nodes. The main advantage of the proposed method is that it provides a set of solutions at a time. The results of the proposed approach were compared with two other well-known clustering techniques: WCA and CLPSO-based clustering. Extensive simulations were performed to show that the proposed approach is an effective approach for clustering in WSN environments and performs better than the other two approaches.

Brain Drain Optimization (BRADO) Algorithm to Solve Multi-Objective Expert Team Formation Problem in Social Networks

“…Following PSO's success with single-objective optimization problems [31], MOPSO, which is the multi-objective version of the PSO algorithm, was developed and has proven to be effective in numerous studies [32].…”