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Unexplained Fluctuations in Particle Swarm Optimisation Performance with Increasing Problem Dimensionality

Published: 24 July 2023 Publication History

Abstract

We study the behaviour of particle swarm optimisation (PSO) with increasing problem dimension for the Alpine 1 function as an exploratory and preliminary case study. Performance trends are analysed and the tuned population size for PSO across dimensions is considered. While performance generally decreases monotonically with scale, there is an unexpected improvement in performance part way along the trend. This also appears to coincide with a counterintuitive transition from large to small populations being preferred, and underlines the challenge, and importance of, selecting the right algorithm and configuration for the problem at each increase in dimensionality.

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cover image ACM Conferences
GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation
July 2023
2519 pages
ISBN:9798400701207
DOI:10.1145/3583133
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

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Association for Computing Machinery

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Publication History

Published: 24 July 2023

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  1. particle swarm optimization (PSO)
  2. numerical optimization
  3. large-scale optimization

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