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Artificial Bee Colony Algorithm Hybridized with Firefly Algorithm for Cardinality Constrained Mean-Variance Portfolio Selection Problem |
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PP: 2831-2844 |
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Author(s) |
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Milan Tuba,
Nebojsa Bacanin,
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Abstract |
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Portfolio selection (optimization) problem is a very important and widely researched problem in the areas of finance and
economy. Literature review shows that many methods and heuristics were applied to this hard optimization problem, however, there are
only few implementations of swarm intelligence metaheuristics. This paper presents artificial bee colony (ABC) algorithm applied to
the cardinality constrained mean-variance (CCMV) portfolio optimization model. By analyzing ABC metaheuristic, some deficiencies
such as slow convergence to the optimal region, were noticed. In this paper ABC algorithm improved by hybridization with the firefly
algorithm (FA) is presented. FA’s search procedure was incorporated into the ABC algorithm to enhance the process of exploitation.
We tested our proposed algorithm on standard test data used in the literature. Comparison with other state-of-the-art optimization
metaheuristics including genetic algorithms, simulated annealing, tabu search and particle swarm optimization (PSO) shows that our
approach is superior considering quality of the portfolio optimization results, especially mean Euclidean distance from the standard
efficiency frontier. |
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