Authors :
Dr. Richa Saxena; Pratham Amit Jain; Preet Mukesh Mehta; Priyanshu Sunil Anandani; Pulkit Khemka; Rashi Jitendra Parchani
Volume/Issue :
Volume 11 - 2026, Issue 10 - October
Google Scholar :
https://tinyurl.com/4yc4n393
DOI :
https://doi.org/10.38124/ijisrt/26Oct896
Note : A published paper may take 4-5
working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and
ResearchGate.
Abstract :
The present study is a systematic literature review-cum-bibliometric analysis of research on heuristic approaches to portfolio optimization under risk, aimed at mapping how the field has grown, where it is published and which themes drive it. Data were retrieved from Elsevier's Scopus database using the search string "risk AND optimisation AND heuristic" and screened through the PRISMA framework, giving a final sample of 1,901 journal articles and review papers published between 2014 and 2027. Performance analysis and science mapping were carried out in Biblioshiny. Excluding the incomplete year 2027, annual output grew at 18.13% and followed an exponential trend.
Keywords :
Portfolio Optimization; Heuristic Algorithms; Metaheuristics; Risk Management; Bibliometric Analysis; Biblioshiny.
References :
- Arasteh, B. and Rashed, A. (2026) 'The RIME-Whale algorithm: A synergistic hybrid for effective global optimization problems', The Journal of Supercomputing, 82(12). Available at: https://doi.org/10.1007/s11227-026-08764-3.
- Aria, M. and Cuccurullo, C. (2017) 'bibliometrix: An R-tool for comprehensive science mapping analysis', Journal of Informetrics, 11(4), pp. 959–975. Available at: https://doi.org/10.1016/j.joi.2017.08.007.
- Blomvall, J., Ekblom, J. and Birge, J.R. (2026) 'Approximate dynamic programming for high-dimensional portfolio choice with transaction costs', Quantitative Finance. Available at: https://doi.org/10.1080/14697688.2026.2708220.
- Carvalho, S.M. de S., Peixoto, M. da S. and de Freitas Jr., C.M. (2026) 'Simulated annealing–Tabu search integration for downside-risk portfolio optimization with cardinality constraints', Journal of Heuristics, 32(3). Available at: https://doi.org/10.1007/s10732-026-09601-9.
- Cui, T., Du, N., Yang, X. and Ding, S. (2024) 'Multi-period portfolio optimization using a deep reinforcement learning hyper-heuristic approach', Technological Forecasting and Social Change, 198, 122944. Available at: https://doi.org/10.1016/j.techfore.2023.122944.
- Donthu, N., Kumar, S., Mukherjee, D., Pandey, N. and Lim, W.M. (2021) 'How to conduct a bibliometric analysis: An overview and guidelines', Journal of Business Research, 133, pp. 285–296. Available at: https://doi.org/10.1016/j.jbusres.2021.04.070.
- Emambocus, B.A.S. et al. (2026) 'A systematic review on novel population-based metaheuristic optimization algorithms: Performance, applications, challenges and future directions', Archives of Computational Methods in Engineering. Available at: https://doi.org/10.1007/s11831-026-10644-1.
- Ertenlice, O. and Kalayci, C.B. (2018) 'A survey of swarm intelligence for portfolio optimization: Algorithms and applications', Swarm and Evolutionary Computation, 39, pp. 36–52. Available at: https://doi.org/10.1016/j.swevo.2018.01.009.
- Filippi, C., Guastaroba, G. and Speranza, M.G. (2016) 'A heuristic framework for the bi-objective enhanced index tracking problem', Omega, 65, pp. 122–137. Available at: https://doi.org/10.1016/j.omega.2016.01.004.
- Gao, N., Liu, Y., He, Y., Zhang, J. and Zhang, L. (2026) 'A novel portfolio selection method via deep reinforcement learning', Systems, 14(3). Available at: https://doi.org/10.3390/systems14030292.
- Gokilavani, R., Arivukkodi, R., Devi, M., Bhardwaj, D., Manda, R. and Mohana Priya, G. (2026) 'Optimizing financial portfolio management using deep reinforcement learning and A3C', International Journal of Artificial Intelligence and Machine Learning, 6(3s), pp. 617–632. Available at: https://doi.org/10.51483/IJAIML.6.2s.2026.617-632.
- Hall, N.G., Long, D.Z., Qi, J. and Sim, M. (2015) 'Managing underperformance risk in project portfolio selection', Operations Research, 63(3), pp. 660–675. Available at: https://doi.org/10.1287/opre.2015.1382.
- Hidalgo-Marín, A.J., Nebro, A.J. and García-Nieto, J. (2026) 'Efficient selection of investment portfolios in real-world markets: A multi-objective optimization approach', Algorithms, 19(1). Available at: https://doi.org/10.3390/a19010020.
- Hussein, O., Megahed, M., Salem, S., Emad, L., Elmelegy, N., Wissa, M. and Gheith, M. (2026) 'A hybrid deep learning and meta-heuristics framework for financial portfolio optimization considering practical constraints: A case study of the Egyptian stock market', Results in Control and Optimization, 24. Available at: https://doi.org/10.1016/j.rico.2026.100784.
- Kalayci, C.B., Ertenlice, O. and Akbay, M.A. (2019) 'A comprehensive review of deterministic models and applications for mean-variance portfolio optimization', Expert Systems with Applications, 125, pp. 345–368. Available at: https://doi.org/10.1016/j.eswa.2019.02.011.
- Khan, A.T., Cao, X., Li, S., Hu, B. and Katsikis, V.N. (2021) 'Quantum beetle antennae search: A novel technique for the constrained portfolio optimization problem', Science China Information Sciences, 64(5), 152204. Available at: https://doi.org/10.1007/s11432-020-2894-9.
- Mansini, R., Ogryczak, W. and Speranza, M.G. (2014) 'Twenty years of linear programming based portfolio optimization', European Journal of Operational Research, 234(2), pp. 518–535. Available at: https://doi.org/10.1016/j.ejor.2013.08.035.
- Markowitz, H. (1952) 'Portfolio selection', The Journal of Finance, 7(1), pp. 77–91. Available at: https://doi.org/10.1111/j.1540-6261.1952.tb01525.x.
- Page, M.J., McKenzie, J.E., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D. et al. (2021) 'The PRISMA 2020 statement: An updated guideline for reporting systematic reviews', BMJ, 372, n71. Available at: https://doi.org/10.1136/bmj.n71.
- Paul, J. and Criado, A.R. (2020) 'The art of writing literature review: What do we know and what do we need to know?', International Business Review, 29(4), 101717. Available at: https://doi.org/10.1016/j.ibusrev.2020.101717.
- Saiz, M., Juan, A.A. and Panadero, J. (2026) 'Generative heuristics for hard- and soft-criteria optimization: Integrating LLMs within metaheuristics', Mathematics, 14(14). Available at: https://doi.org/10.3390/math14142547.
- Soler-Dominguez, A., Juan, A.A. and Kizys, R. (2017) 'A survey on financial applications of metaheuristics', ACM Computing Surveys, 50(1), pp. 1–23. Available at: https://doi.org/10.1145/3054133.
- Sun, J., Pu, M., Tian, D., Fan, Y., Xu, Q., Li, F. and Peng, S. (2026) 'Multi-swarm particle swarm optimization with multi-learning strategy', Algorithms, 19(6). Available at: https://doi.org/10.3390/a19060474.
- Vercher, E. and Bermúdez, J.D. (2015) 'Portfolio optimization using a credibility mean-absolute semi-deviation model', Expert Systems with Applications, 42(20), pp. 7121–7131. Available at: https://doi.org/10.1016/j.eswa.2015.05.020.
- Wei, W., Liu, F. and Mei, S. (2015) 'Energy pricing and dispatch for smart grid retailers under demand response and market price uncertainty', IEEE Transactions on Smart Grid, 6(3), pp. 1364–1374. Available at: https://doi.org/10.1109/TSG.2014.2376522.
- Zhao, H., Chen, Z., Zhan, Z., Kwong, S. and Zhang, J. (2021) 'Multiple populations co-evolutionary particle swarm optimization for multi-objective cardinality constrained portfolio optimization problem', Neurocomputing, 430, pp. 58–70. Available at: https://doi.org/10.1016/j.neucom.2020.12.022.
The present study is a systematic literature review-cum-bibliometric analysis of research on heuristic approaches to portfolio optimization under risk, aimed at mapping how the field has grown, where it is published and which themes drive it. Data were retrieved from Elsevier's Scopus database using the search string "risk AND optimisation AND heuristic" and screened through the PRISMA framework, giving a final sample of 1,901 journal articles and review papers published between 2014 and 2027. Performance analysis and science mapping were carried out in Biblioshiny. Excluding the incomplete year 2027, annual output grew at 18.13% and followed an exponential trend.
Keywords :
Portfolio Optimization; Heuristic Algorithms; Metaheuristics; Risk Management; Bibliometric Analysis; Biblioshiny.