{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T21:22:20Z","timestamp":1782163340888,"version":"3.54.5"},"reference-count":42,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T00:00:00Z","timestamp":1766534400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>As financial markets become increasingly complex, optimizing investment portfolios under multiple conflicting objectives has become a central challenge for decision-makers. This paper presents a comprehensive benchmarking framework for multi-objective portfolio optimization based on metaheuristics, designed to operate on real-world financial data. This framework integrates preprocessing, and optimization using four state-of-the-art algorithms: NSGA-II, MOEA\/D, SMS-EMOA, and SMPSO. Using historical data from over 11,000 assets listed on U.S. exchanges, including ARCA, NYSE, NASDAQ, OTC, AMEX, and BATS, we define a suite of benchmark scenarios with increasing dimensionality and constraint complexity. Our results highlight algorithmic strengths and limitations, reveal significant trade-offs between return and risk, and demonstrate the effectiveness of multi-objective metaheuristics in constructing diversified, high-performance investment portfolios. Each portfolio is encoded as a real-valued vector combining asset selection and allocation, enabling fine-grained diversification control. All datasets and source code are publicly available to ensure reproducibility.<\/jats:p>","DOI":"10.3390\/a19010020","type":"journal-article","created":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T11:22:14Z","timestamp":1766575334000},"page":"20","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Efficient Selection of Investment Portfolios in Real-World Markets: A Multi-Objective Optimization Approach"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-8603-0732","authenticated-orcid":false,"given":"Antonio J.","family":"Hidalgo-Mar\u00edn","sequence":"first","affiliation":[{"name":"Department de Lenguajes y Ciencias de la Computaci\u00f3n, University of M\u00e1laga, 29071 M\u00e1laga, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5580-0484","authenticated-orcid":false,"given":"Antonio J.","family":"Nebro","sequence":"additional","affiliation":[{"name":"Department de Lenguajes y Ciencias de la Computaci\u00f3n, University of M\u00e1laga, 29071 M\u00e1laga, Spain"},{"name":"ITIS Software, University of M\u00e1laga, Ada Byron Research Building, 29071 M\u00e1laga, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2985-3480","authenticated-orcid":false,"given":"Jos\u00e9","family":"Garc\u00eda-Nieto","sequence":"additional","affiliation":[{"name":"Department de Lenguajes y Ciencias de la Computaci\u00f3n, University of M\u00e1laga, 29071 M\u00e1laga, Spain"},{"name":"ITIS Software, University of M\u00e1laga, Ada Byron Research Building, 29071 M\u00e1laga, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mandal, P.K., and Thakur, M. 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