Abstract
Portfolio optimization is a complex real-world problem where assets are selected such that profit is maximized while risk is simultaneously minimized. In recent years, nature-inspired algorithms have become a popular choice for efficiently identifying optimal portfolios. This paper introduces such an algorithm that, unlike previous algorithms, uses a set-based approach to reduce the dimensionality of the problem and to determine the appropriate budget al.location for each asset. The results show that the proposed approach is capable of obtaining good quality solutions, while being relatively fast.
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The authors acknowledge the Centre for High Performance Computing (CHPC), South Africa, for providing computational resources to this research project.
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Erwin, K., Engelbrecht, A.P. (2020). Set-Based Particle Swarm Optimization for Portfolio Optimization. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2020. Lecture Notes in Computer Science(), vol 12421. Springer, Cham. https://doi.org/10.1007/978-3-030-60376-2_28
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