A Hybrid Non-dominated Sorting Genetic Algorithm with Local Search for Portfolio Selection Problem with Cardinality Constraints

Yuri Laio Teixeira Veras Silva; Nathállya Etyenne Figueira Silva

doi:10.5585/2023.22046

Autores

Yuri Laio Teixeira Veras Silva Universidade Federal de Campina Grande http://orcid.org/0000-0003-0683-6194
Nathállya Etyenne Figueira Silva Universidade Federal da Paraíba http://orcid.org/0000-0002-4686-9946

DOI:

https://doi.org/10.5585/2023.22046

Palavras-chave:

Portfolio selection problem, Cardinality constraints, Genetic algorithm, Multiobjective Optimization

Resumo

ARTIGO ACEITO

The Cardinality-Constrained Portfolio Selection Problem (CCPSP) consists of allocating resources to a limited number of assets. In its classical form, it is represented as a multi-objective problem, which considers the expected return and the assumed risk in the portfolio. This problem is one of the most relevant subjects in finance and economics nowadays. In recent years, the consideration of cardinality constraints, which limit the number of assets in the portfolio, has received increased attention from researchers, mainly due to its importance in real-world decisions. In this context, this paper proposes a new hybrid heuristic approach, based on a Non-dominated Sorting Genetic Algorithm with Local Search structures, to solve PSP with cardinality constraints, aiming to overcome the challenge of achieving efficient solutions to the problem. The results demonstrated that the proposed algorithm achieved good quality results, outperforming other methods in the literature in several classic instances.

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Biografia do Autor

Yuri Laio Teixeira Veras Silva, Universidade Federal de Campina Grande

Professor Adjunto na Unidade de Engenharia de Produção da Universidade Federal de Campina Grande. Tem experiência nas grandes áreas de Engenharia de Produção, especialmente em pesquisa operacional e simulação, gestão da produção, análise de investimentos, logística e cadeia de suprimentos, com foco na implementação de ferramentas de apoio à tomada de decisão, fundamentadas principalmente em abordagens de programação inteira mista, não-linear, modelos estocásticos, meta-heurísticas, inteligência artificial e abordagens de simulação computacional.

Nathállya Etyenne Figueira Silva, Universidade Federal da Paraíba

Professora no Centro Universitário de João Pessoa, lotada no curso de Administração. Doutoranda em Administração pelo Programa de Pós-Graduação em Administração da Universidade Federal da Paraíba. Desenvolve pesquisas principalmente nas áreas de finanças, educação financeira e responsabilidade social corporativa.

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