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Nebojsa Bacanin
Researcher at Singidunum University
Publications - 207
Citations - 4041
Nebojsa Bacanin is an academic researcher from Singidunum University. The author has contributed to research in topics: Computer science & Metaheuristic. The author has an hindex of 25, co-authored 121 publications receiving 1740 citations. Previous affiliations of Nebojsa Bacanin include Megatrend University.
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COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach
Miodrag Zivkovic,Nebojsa Bacanin,K. Venkatachalam,Anand Nayyar,Aleksandar Djordjevic,Ivana Strumberger,Fadi Al-Turjman +6 more
TL;DR: Wang et al. as mentioned in this paper proposed a hybrid approach between machine learning, adaptive neuro-fuzzy inference system and enhanced beetle antennae search swarm intelligence metaheuristics to predict the number of the COVID-19 cases.
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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.
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Artificial Bee Colony (ABC) Algorithm for Constrained Optimization Improved with Genetic Operators
Nebojsa Bacanin,Milan Tuba +1 more
TL;DR: Modifications to the ABC algorithm for constrained optimization problems that improve performance of the algorithm are introduced based on genetic algorithm (GA) operators and are applied to the creation of new candidate solutions.
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Artificial Bee Colony Algorithm Hybridized with Firefly Algorithm for Cardinality Constrained Mean-Variance Portfolio Selection Problem
Milan Tubaand,Nebojsa Bacanin +1 more
TL;DR: Comparison with other state-of-the-art optimization metaheuristics including genetic algorithms, simulated annealing, tabu searc h and particle swarm optimization shows that the proposed algorithm is superior considering quality of the portfolio optimization results, especially mean Euclidean distance from the standard efficiency frontier.
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Improved seeker optimization algorithm hybridized with firefly algorithm for constrained optimization problems
Milan Tuba,Nebojsa Bacanin +1 more
TL;DR: This paper introduced modifications to the seeker optimization algorithm to control exploitation/exploration balance and hybridized it with elements of the firefly algorithm that improved its exploitation capabilities and outperformed other state-of-the-art swarm intelligence algorithms.