A new lattice based artificial bee colony algorithm for EEG noise minimization

The last decades have witnessed the changes stemming from the existence of a new term called big data. This new concept and its features have modified the descriptions of real world optimization problems and investigating the performances of the previously introduced solving techniques and developing new methods by considering the properties of big data concept have become critical. Artificial Bee Colony (ABC) algorithm inspired by foraging behaviors of the real honey bees is one of the most successful swarm intelligences based techniques. In this study, 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 signals and lattice based ABC (LBABC) was proposed. For analyzing the solving capabilities of the proposed technique, a set of experimentals has been carried out by using different problem instances. The results obtained from the experimental studies were also compared with the results of well-known 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 other techniques for the vast majority of the problem instances.