Clusterization of vector and matrix data arrays using the combined evolutionary method of fish schools




combined optimization, fuzzy clustering, evolutionary algorithms, density functions, Fish School


The problem of clustering data arrays described in both vector and matrix forms and based on the optimization of data distribution density functions in these arrays is considered. For the optimization of these functions, the algorithm that is a hybrid of Fish School Search, random search, and evolutionary optimization is proposed. This algorithm does not require calculating the optimized function’s derivatives and, in the general case, is designed to find optimums of multiextremal functions of the matrix argument (images). The proposed approach reduces the number of runs of the optimization procedure, finds extrema of complex functions with many extrema, and is simple in numerical implementation.

Author Biographies

Yevgeniy Bodyanskiy, Kharkiv National University of Radio Electronics, Kharkiv

Doctor of Technical Sciences, a professor at the Department of Artificial Intelligence, the scientific head of the Control Systems Research Laboratory of Kharkiv National University of Radio Electronics, Kharkiv, Ukraine.

Alina Shafronenko, Kharkiv National University of Radio Electronics, Kharkiv

Candidate of Technical Sciences (Ph.D.), an associate professor at the Department of Informatics of Kharkiv National University of Radio Electronics, Kharkiv, Ukraine.

Iryna Pliss, Kharkiv National University of Radio Electronics, Kharkiv

Senior researcher, Candidate of Technical Sciences (Ph.D.), a leading researcher of the Control Systems Research Laboratory of Kharkiv National University of Radio Electronics, Kharkiv, Ukraine.


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Methods of optimization, optimum control and theory of games