An improved QPSO algorithm and its application in fuzzy portfolio model with constraints

Aiming at the shortcomings of quantum-behaved particle swarm optimization algorithm (QPSO), an improved quantum-behaved particle swarm optimization algorithm (IQPSO) is put forward, and the improved algorithm is applied in solving a kind of fuzzy portfolio selection problems. Firstly, a kind of port...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Soft computing (Berlin, Germany) Ročník 25; číslo 12; s. 7695 - 7706
Hlavní autoři: He, Guang, Lu, Xiao-li
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2021
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Computational Intelligence
Constraint modelling
Control
Engineering
Focus
Fuzzy sets
Heuristic methods
Investments
Mathematical Logic and Foundations
Mechatronics
Optimization algorithms
Parameter estimation
Particle swarm optimization
Random variables
Robotics
Securities markets
Simulation
Tax rates
Thermal expansion
Velocity
Fuzzy return rate
Hybrid distribution
Quantum-behaved particle swarm optimization algorithm
Contraction–expansion coefficient
Background risk
ISSN:1432-7643, 1433-7479
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Aiming at the shortcomings of quantum-behaved particle swarm optimization algorithm (QPSO), an improved quantum-behaved particle swarm optimization algorithm (IQPSO) is put forward, and the improved algorithm is applied in solving a kind of fuzzy portfolio selection problems. Firstly, a kind of portfolio models with fuzzy return rates and background risk is established followed by some necessary preparations of fuzzy theory. Then, in the improved algorithm, hybrid probability distribution strategy and contraction–expansion coefficient with nonlinear structure are chosen to enhance particle’s exploration ability, and premature prevention mechanism is used to maintain population diversity. Furthermore, the experimental results on 16 benchmark functions show that IQPSO has better convergence and robustness than PSO with inertia weight, QPSO and QPSO with a hybrid probability distribution in most cases. Finally, when solving a fuzzy portfolio model, IQPSO provides comparable and superior results compared with the other metaheuristics.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-021-05688-3