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

Kris Boudt, Chunlin Wan

Research output: Contribution to JournalArticleAcademicpeer-review

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
JournalOptimization Letters
DOIs
Publication statusAccepted/In press - 2019

Fingerprint

Constrained Optimization
Sparsity
Particle Swarm Optimization
Cardinality
Cardinality Constraints
Particle Swarm Optimization Algorithm
Numerical Experiment
Heuristics
Tend
Binary
Optimization Problem

Keywords

  • Binary particle swarm optimization
  • Cardinality mapping
  • Portfolio optimization

Cite this

@article{02de0b73f1724a0cb47e77666f007952,
title = "The effect of velocity sparsity on the performance of cardinality constrained particle swarm optimization",
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.",
keywords = "Binary particle swarm optimization, Cardinality mapping, Portfolio optimization",
author = "Kris Boudt and Chunlin Wan",
year = "2019",
doi = "10.1007/s11590-019-01398-w",
language = "English",
journal = "Optimization Letters",
issn = "1862-4472",
publisher = "Springer Verlag",

}

The effect of velocity sparsity on the performance of cardinality constrained particle swarm optimization. / Boudt, Kris; Wan, Chunlin.

In: Optimization Letters, 2019.

Research output: Contribution to JournalArticleAcademicpeer-review

TY - JOUR

T1 - The effect of velocity sparsity on the performance of cardinality constrained particle swarm optimization

AU - Boudt, Kris

AU - Wan, Chunlin

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Binary particle swarm optimization

KW - Cardinality mapping

KW - Portfolio optimization

UR - http://www.scopus.com/inward/record.url?scp=85061637885&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85061637885&partnerID=8YFLogxK

U2 - 10.1007/s11590-019-01398-w

DO - 10.1007/s11590-019-01398-w

M3 - Article

JO - Optimization Letters

JF - Optimization Letters

SN - 1862-4472

ER -