A Novel Multi-Objective Velocity-Free Boolean Particle Swarm Optimization
Abstract
This paper extends boolean particle swarm optimization to a multi-objective setting, to our knowledge for the first time in the literature. Our proposed new boolean algorithm, MBOnvPSO, is notably simplified by the omission of a velocity update rule and has enhanced exploration ability due to the inclusion of a 'noise' term in the position update rule that prevents particles being trapped in local optima. Our algorithm additionally makes use of an external archive to store non-dominated solutions and implements crowding distance to encourage solution diversity. In benchmark tests, MBOnvPSO produced high quality Pareto fronts, when compared to benchmarked alternatives, for all of the multi-objective test functions considered, with competitive performance in search spaces with up to 600 discrete dimensions.
Cite
@article{arxiv.2210.05882,
title = {A Novel Multi-Objective Velocity-Free Boolean Particle Swarm Optimization},
author = {Wei Quan and Denise Gorse},
journal= {arXiv preprint arXiv:2210.05882},
year = {2022}
}