Improving Vector Evaluated Particle Swarm Optimisation Using Multiple Nondominated Leaders

Abstract

The vector evaluated particle swarm optimisation (VEPSO) algorithm was previously improved by incorporating nondominated solutions for solving multiobjective optimisation problems. However, the obtained solutions did not converge close to the Pareto front and also did not distribute evenly over the Pareto front. Therefore, in this study, the concept of multiple nondominated leaders is incorporated to further improve the VEPSO algorithm. Hence, multiple nondominated solutions that are best at a respective objective function are used to guide particles in finding optimal solutions. The improved VEPSO is measured by the number of nondominated solutions found, generational distance, spread, and hypervolume. The results from the conducted experiments show that the proposed VEPSO significantly improved the existing VEPSO algorithms.

Improved Location Estimation in Wireless Sensor Networks Using a Vector-Based Swarm Optimized Connected Dominating Set

“…In this paper, we introduce an approach by constructing a connected dominating set (CDS) based virtual backbone only with anchor nodes optimized in terms of load balance using a vector-based swarm optimization (VBSO) mechanism [10]. This optimized CDS is then used for location estimation in WSNs.…”