TY - JOUR
T1 - The effect of velocity sparsity on the performance of cardinality constrained particle swarm optimization
AU - Boudt, Kris
AU - Wan, Chunlin
PY - 2020/4
Y1 - 2020/4
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 - https://doi.org/10.1007/s11590-019-01398-w
UR - http://www.scopus.com/inward/record.url?scp=85061637885&partnerID=8YFLogxK
U2 - 10.1007/s11590-019-01398-w
DO - 10.1007/s11590-019-01398-w
M3 - Article
VL - 14
SP - 747
EP - 758
JO - Optimization Letters
JF - Optimization Letters
SN - 1862-4472
IS - 3
ER -