Nebojsa Bacanin

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.

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.

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.

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.

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.