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Firefly algorithm for cardinality constrained mean-variance portfolio optimization problem with entropy diversity constraint.

Bacanin N, Tuba M - ScientificWorldJournal (2014)

Bottom Line: This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms.No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature.Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Computer Science, Megatrend University Belgrade, 11070 Belgrade, Serbia.

ABSTRACT
Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results.

Show MeSH
Arrangement algorithm.
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Related In: Results  -  Collection


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alg2: Arrangement algorithm.

Mentions: In the arrangement algorithm, i is the current solution that consists of Q the distinct set of Ki* assets in the ith solution, zi,j is the decision variable of asset j, and xi,j is the weight proportion for asset j. Arrangement algorithm pseudocode is shown as Algorithm 2.


Firefly algorithm for cardinality constrained mean-variance portfolio optimization problem with entropy diversity constraint.

Bacanin N, Tuba M - ScientificWorldJournal (2014)

Arrangement algorithm.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4060745&req=5

alg2: Arrangement algorithm.
Mentions: In the arrangement algorithm, i is the current solution that consists of Q the distinct set of Ki* assets in the ith solution, zi,j is the decision variable of asset j, and xi,j is the weight proportion for asset j. Arrangement algorithm pseudocode is shown as Algorithm 2.

Bottom Line: This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms.No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature.Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Computer Science, Megatrend University Belgrade, 11070 Belgrade, Serbia.

ABSTRACT
Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results.

Show MeSH