A Novel Multi-Objective Velocity-Free Boolean Particle Swarm Optimization

TL;DR

This paper introduces MBOnvPSO, a simplified multi-objective boolean particle swarm optimization algorithm that enhances exploration and diversity, achieving high-quality Pareto fronts in large discrete search spaces.

Contribution

The paper presents the first multi-objective boolean PSO algorithm without velocity updates, incorporating noise and diversity mechanisms for improved performance.

Findings

Achieved high-quality Pareto fronts on benchmark functions.

Performed well in search spaces with up to 600 dimensions.

Outperformed benchmarked alternatives in multi-objective 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.