M
Milan Tuba
Researcher at Singidunum University
Publications - 216
Citations - 5051
Milan Tuba is an academic researcher from Singidunum University. The author has contributed to research in topics: Metaheuristic & Swarm intelligence. The author has an hindex of 33, co-authored 210 publications receiving 3842 citations. Previous affiliations of Milan Tuba include State University of Novi Pazar & Ben-Gurion University of the Negev.
Papers
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Journal ArticleDOI
Improved bat algorithm applied to multilevel image thresholding.
Adis Alihodzic,Milan Tuba +1 more
TL;DR: The new proposed improved bat algorithm proved to be better than five other state-of-the-art algorithms, improving quality of results in all cases and significantly improving convergence speed.
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An upgraded artificial bee colony (ABC) algorithm for constrained optimization problems
Ivona Brajevic,Milan Tuba +1 more
TL;DR: This paper introduces an upgraded artificial bee colony (UABC) algorithm for constrained optimization problems that enhances fine-tuning characteristics of the modification rate parameter and employs modified scout bee phase of the ABC algorithm.
Modified cuckoo search algorithm for unconstrained optimization problems
TL;DR: A modified version of the cuckoo search algorithm where the step size is determined from the sorted rather than only permuted fitness matrix is implemented.
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An ant colony optimization algorithm with improved pheromone correction strategy for the minimum weight vertex cover problem
Raka Jovanovic,Milan Tuba +1 more
TL;DR: This article proposes a pheromone correction heuristic strategy that uses information about the best-found solution to exclude suspicious elements from it and improves pure ant colony optimization algorithm by avoiding early trapping in local convergence.
Journal ArticleDOI
Firefly algorithm for cardinality constrained mean-variance portfolio optimization problem with entropy diversity constraint.
Nebojsa Bacanin,Milan Tuba +1 more
TL;DR: This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint and proves to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results.