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

216 Downloads (Pure)

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

Funding

This paper was written while Chunlin Wan was visiting the finance department of the Solvay Business School at Vrije Universiteit Brussel. We are grateful to the Editor (Oleg Prokopyev), the associate editor, an anonymous referee, Xiang Deng, Paola Festa, Jiayin Gu, Nanjing Huang, Joao Miranda, Giang Nguyen, Wajid Raza and Marjan Wauters for helpful comments and suggestions. Financial support from the Chinese Scholarship Council and the National Natural Science Foundation of China (Grant Number 71742004) is gratefully acknowledged.

FundersFunder number
Chinese Scholarship Council
National Natural Science Foundation of China71742004
Vrije Universiteit Brussel

    Keywords

    • Binary particle swarm optimization
    • Cardinality mapping
    • Portfolio optimization

    Fingerprint

    Dive into the research topics of 'The effect of velocity sparsity on the performance of cardinality constrained particle swarm optimization'. Together they form a unique fingerprint.

    Cite this