A Novel Harmony Search Based Method for Noise Minimization on EEG Signals

Big data is a topic that is increasing in popularity day by day, and new techniques are being developed for the optimization processes performed on it. Harmony Search (HS) algorithm, inspired by music and harmonies, is an intuitive algorithm and has been used for the optimization of many problems. In this study, a new technique called source-linked HS algorithm (slinkHSA) focusing on big data optimization problems is presented. Experimental results were obtained with the slinkHS algorithm, results were compared with other popular metaheuristic algorithms and unmodified HS algorithm. The obtained results showed that the technique applied in the slinkHS algorithm adapted to the problem better, in this way better results could be obtained than other algorithms compared.