Özet:

The last decades have witnessed the changes stemming from the existence of a new term called as big data. This new concept and its features have modified the descriptions of the real world optimization problems and investigating the performances of the previously introduced solving techniques and developing new methods by considering the properties of the big data concept have become critical. Artificial Bee Colony (ABC) algorithm inspired by the clever foraging behaviors of the real honey bees is one of the most successful swarm intelligence based techniques. In this study, the employed and onlooker bee phases of the ABC algorithm were remodeled for solving a recent big data optimization problem that requires noise minimization on the electroencephalography (EEG) signals and lattice based ABC (LBABC) was proposed. For analyzing the solving capabilities of the proposed technique, a set of experimental studies has been carried out by using different problem instances. The results obtained from the experimental studies first were compared with the well-known variants of the standard ABC algorithm named gbest-guided ABC (GABC), ABC/best/1, ABC/best/2, crossover ABC (CABC), converge-onlookers ABC (COABC) and quick ABC (qABC). Moreover, the results of the proposed ABC algorithm were compared with the Differential Evolution (DE) algorithm, Genetic algorithm (GA), Firefly algorithm (FA), Fireworks algorithm (FW), Phase based Optimization algorithm (PBO) and Particle Swarm Optimization (PSO) algorithm based big data optimization techniques. From the comparative studies, it was understood that the newly introduced big data optimization technique by referencing the ABC algorithm is capable of producing better or relatively similar results compared to the mentioned techniques for the vast majority of the problem instances.

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