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

• Published in: Soft computing (Berlin, Germany) Vol. 25; no. 12; pp. 7695 - 7706
• Main Authors: He, Guang, Lu, Xiao-li
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2021; Springer Nature B.V
• Subjects: 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
• DOI: 10.1007/s00500-021-05688-3

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.