Abstract
In this paper, we apply a basic Bee Colony Optimization algorithm in order to find a high-quality solution for the constrained portfolio optimization problem. Moreover, we use a basic Ant Colony Optimization algorithm and a Tabu Search metaheuristic approach as a benchmark. Our findings indicate that nature-inspired methodologies are able to find feasible solutions for dynamic optimization problems in a reasonable amount of time in contrast with the simple tabu search.
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Vassiliadis, V., Dounias, G. (2008). Nature Inspired Intelligence for the Constrained Portfolio Optimization Problem. In: Darzentas, J., Vouros, G.A., Vosinakis, S., Arnellos, A. (eds) Artificial Intelligence: Theories, Models and Applications. SETN 2008. Lecture Notes in Computer Science(), vol 5138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87881-0_45
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DOI: https://doi.org/10.1007/978-3-540-87881-0_45
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