An Improved Particle Swarm Optimization for the Constrained Portfolio Selection Problem

2009 ◽  
Author(s):  
Jianwei Gao ◽  
Zhonghua Chu
Keyword(s):  
Particle Swarm Optimization ◽  
Portfolio Selection ◽  
Particle Swarm ◽  
Selection Problem ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Portfolio Selection Problem

scholarly journals A novel hybrid PSO-based metaheuristic for costly portfolio selection problems

2021 ◽  
Author(s):  
Marco Corazza ◽  
Giacomo di Tollo ◽  
Giovanni Fasano ◽  
Raffaele Pesenti
Keyword(s):  
Particle Swarm Optimization ◽  
Portfolio Selection ◽  
Particle Swarm ◽  
Selection Problem ◽  
Computational Time ◽  
Swarm Optimization ◽  
Portfolio Selection Problem ◽  
Hybrid Metaheuristic ◽  
Unconstrained Model ◽  
Unconstrained Problem

AbstractIn this paper we propose a hybrid metaheuristic based on Particle Swarm Optimization, which we tailor on a portfolio selection problem. To motivate and apply our hybrid metaheuristic, we reformulate the portfolio selection problem as an unconstrained problem, by means of penalty functions in the framework of the exact penalty methods. Our metaheuristic is hybrid as it adaptively updates the penalty parameters of the unconstrained model during the optimization process. In addition, it iteratively refines its solutions to reduce possible infeasibilities. We report also a numerical case study. Our hybrid metaheuristic appears to perform better than the corresponding Particle Swarm Optimization solver with constant penalty parameters. It performs similarly to two corresponding Particle Swarm Optimization solvers with penalty parameters respectively determined by a REVAC-based tuning procedure and an irace-based one, but on average it just needs less than 4% of the computational time requested by the latter procedures.

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