On the Automatic Design of Multi-Objective Particle Swarm Optimizers: Experimentation and Analysis

Research in multi-objective particle swarm optimizers (MOPSOs) progresses by proposing one new MOPSO at a time. In spite of the commonalities among different MOPSOs, it is often unclear which algorithmic components are crucial for explaining the performance of a particular MOPSO design. Moreover, it is expected that different designs may perform best on different problem families and identifying a best overall MOPSO is a challenging task. We tackle this challenge here by: (1) proposing AutoMOPSO, a flexible algorithmic template for designing MOPSOs with a design space that can instantiate thousands of potential MOPSOs; and (2) searching for good-performing MOPSO designs given a family of training problems by means of an automatic configuration tool (irace). We apply this automatic design methodology to generate a MOPSO that significantly outperforms two state-of-the-art MOPSOs on four well-known bi-objective problem families. We also identify the key design choices and parameters of the winning MOPSO by means of ablation. AutoMOPSO is publicly available as part of the jMetal framework.

Keywords
Artificial Intelligence, Automatic Algorithm Design, Metaheuristics, Multi-objective Particle Swarm Optimizers (MOPSOs).

Autores:

Carlos Artemio Coello Coello.

Revista

© 2024 Springer.

DOI: https://doi.org/10.1007/s11721-023-00227-2.