The effect of velocity sparsity on the performance of cardinality constrained particle swarm optimization

Kris Boudt, Chunlin Wan*

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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Abstract

The Particle Swarm Optimization (PSO) algorithm is a flexible heuristic optimizer that can be used for solving cardinality constrained binary optimization problems. In such problems, only K elements of the N-dimensional solution vector can be non-zero. The typical solution is to use a mapping function to enforce the cardinality constraint on the trial PSO solution. In this paper, we show that when K is small compared to N, the use of the mapped solution in the velocity vector tends to lead to early stagnation. As a solution, we recommend to use the untransformed solution as a direction in the velocity vector. We use numerical experiments to document the gains in performance when K is small compared to N.

Original languageEnglish
Pages (from-to)747-758
Number of pages12
JournalOptimization Letters
Volume14
Issue number3
Early online date13 Feb 2019
DOIs
Publication statusPublished - Apr 2020

Keywords

  • Binary particle swarm optimization
  • Cardinality mapping
  • Portfolio optimization

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