We propose a Particle Swarm Optimization (PSO) based scheme for the solution of a mixed-integer nonsmooth portfolio selection problem. To this end, we first reformulate the portfolio selection problem as an unconstrained optimization problem by adopting an exact penalty method. Then, we use PSO to manage both the optimization of the objective function and the minimization of all the constraints violations. In this context we introduce and test a novel approach that adaptively updates the penalty parameters. Also, we introduce a technique for the refinement of the solutions provided by the PSO to cope with the mixed-integer framework.

A PSO-based framework for nonsmooth portfolio selection problems

Marco Corazza
;
Giacolo di Tollo;Giovanni Fasano
;
Raffaele Pesenti
2019-01-01

Abstract

We propose a Particle Swarm Optimization (PSO) based scheme for the solution of a mixed-integer nonsmooth portfolio selection problem. To this end, we first reformulate the portfolio selection problem as an unconstrained optimization problem by adopting an exact penalty method. Then, we use PSO to manage both the optimization of the objective function and the minimization of all the constraints violations. In this context we introduce and test a novel approach that adaptively updates the penalty parameters. Also, we introduce a technique for the refinement of the solutions provided by the PSO to cope with the mixed-integer framework.
Neural Advances in Processing Nonlinear Dynamic Signals
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/3697140
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