Abstract
This paper proposes a new variant of bacterial foraging optimization, called Bacterial Foraging Optimization with Neighborhood Learning (BFONL). In the proposed BFO-NL, information sharing among each individual can be realized by using a von Neumann-style neighborhood topology. To demonstrate the efficiency of BFO-NL in dealing with real world problem, this paper improves the original mean-variance portfolio model into Two-Period dynamic PO model considering risky assets for trading, then uses BFO-NL to automatically find the optimal portfolios in the advanced model. With a five stock portfolio example, BFO-NL is proved to outperform original BFO in selecting optimal portfolios.
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Tan, L., Niu, B., Wang, H., Huang, H., Duan, Q. (2014). Bacterial Foraging Optimization with Neighborhood Learning for Dynamic Portfolio Selection. In: Huang, DS., Han, K., Gromiha, M. (eds) Intelligent Computing in Bioinformatics. ICIC 2014. Lecture Notes in Computer Science(), vol 8590. Springer, Cham. https://doi.org/10.1007/978-3-319-09330-7_48
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DOI: https://doi.org/10.1007/978-3-319-09330-7_48
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09329-1
Online ISBN: 978-3-319-09330-7
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