Handling multiple objectives with particle swarm optimization

TLDR

The authors propose integrating Pareto dominance into particle swarm optimization to enable the heuristic to solve problems with multiple objectives. Their method employs an external repository of particles to guide swarm movement and introduces a mutation operator to enhance exploration, and is evaluated on standard multiobjective test functions and metrics. Experimental results demonstrate that the approach is highly competitive and offers a viable alternative for multiobjective optimization.

Abstract

This paper presents an approach in which Pareto dominance is incorporated into particle swarm optimization (PSO) in order to allow this heuristic to handle problems with several objective functions. Unlike other current proposals to extend PSO to solve multiobjective optimization problems, our algorithm uses a secondary (i.e., external) repository of particles that is later used by other particles to guide their own flight. We also incorporate a special mutation operator that enriches the exploratory capabilities of our algorithm. The proposed approach is validated using several test functions and metrics taken from the standard literature on evolutionary multiobjective optimization. Results indicate that the approach is highly competitive and that can be considered a viable alternative to solve multiobjective optimization problems.