Abstract
This paper employs five classical multi-objective swarm intelligence algorithms to solve portfolio optimization (PO) problem with background returns. The potential investment ratio is considered as an individual. In the experiments, we consider five different PO cases. The simulation results show that multi-objective evolutionary algorithm based on decomposition (MOEA/D) and weighted optimization framework (WOF) perform significantly better than the other four in solving the high-dimensional objective problem, and WOF obtains a more uniform solution to the high-dimensional problem.
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Acknowledgement
The work was supported by The Natural Science Foundation of Guangdong Province (No. 2020A1515010752).
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Chen, L., Wang, Y., Liu, J., Tan, L. (2023). Swarm Intelligence for Multi-objective Portfolio Optimization. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13655. Springer, Cham. https://doi.org/10.1007/978-3-031-20096-0_13
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DOI: https://doi.org/10.1007/978-3-031-20096-0_13
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