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

Kris Boudt (UGent) and Chunlin Wan
(2020) OPTIMIZATION LETTERS. 14. p.747-758
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Abstract
The Particle Swarm Optimization (PSO) algorithm is a flexible heuristic 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

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Please use this url to cite or link to this publication:

MLA
Boudt, Kris, and Chunlin Wan. “The Effect of Velocity Sparsity on the Performance of Cardinality Constrained Particle Swarm Optimization.” OPTIMIZATION LETTERS, vol. 14, 2020, pp. 747–58.
APA
Boudt, K., & Wan, C. (2020). The effect of velocity sparsity on the performance of cardinality constrained particle swarm optimization. OPTIMIZATION LETTERS, 14, 747–758.
Chicago author-date
Boudt, Kris, and Chunlin Wan. 2020. “The Effect of Velocity Sparsity on the Performance of Cardinality Constrained Particle Swarm Optimization.” OPTIMIZATION LETTERS 14: 747–58.
Chicago author-date (all authors)
Boudt, Kris, and Chunlin Wan. 2020. “The Effect of Velocity Sparsity on the Performance of Cardinality Constrained Particle Swarm Optimization.” OPTIMIZATION LETTERS 14: 747–758.
Vancouver
1.
Boudt K, Wan C. The effect of velocity sparsity on the performance of cardinality constrained particle swarm optimization. OPTIMIZATION LETTERS. 2020;14:747–58.
IEEE
[1]
K. Boudt and C. Wan, “The effect of velocity sparsity on the performance of cardinality constrained particle swarm optimization,” OPTIMIZATION LETTERS, vol. 14, pp. 747–758, 2020.
@article{8601327,
  abstract     = {The Particle Swarm Optimization (PSO) algorithm is a flexible heuristic 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. },
  author       = {Boudt, Kris and Wan, Chunlin},
  issn         = {1862-4472},
  journal      = {OPTIMIZATION LETTERS},
  keywords     = {Binary Particle Swarm Optimization,Cardinality Mapping,Portfolio Optimization},
  language     = {eng},
  pages        = {747--758},
  title        = {The effect of velocity sparsity on the performance of cardinality constrained particle swarm optimization},
  url          = {http://dx.doi.org/10.1007/s11590-019-01398-w},
  volume       = {14},
  year         = {2020},
}

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