A novel hybrid PSO-based metaheuristic for costly portfolio selection problems
| Autor: | Marco Corazza, Giacomo di Tollo, Giovanni Fasano, Raffaele Pesenti |
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| Rok vydání: | 2021 |
| Předmět: |
Mathematical optimization
Portfolio selection problems Computer science General Decision Sciences 02 engineering and technology Management Science and Operations Research Exact penalty functions 020204 information systems 0202 electrical engineering electronic engineering information engineering Penalty method Metaheuristic Global optimization Selection (genetic algorithm) Settore SECS-S/06 - Metodi mat. dell'economia e Scienze Attuariali e Finanziarie Particle swarm optimization Solver REVAC Particle Swarm Optimization Theory of computation Hybrid metaheuristics Portfolio 020201 artificial intelligence & image processing irace Settore MAT/09 - Ricerca Operativa |
| Zdroj: | Annals of Operations Research. 304:109-137 |
| ISSN: | 1572-9338 0254-5330 |
| DOI: | 10.1007/s10479-021-04075-3 |
| Popis: | In 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. |
| Databáze: | OpenAIRE |
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