(КПІ ім. Ігоря Сікорського, 2022) Bodyanskiy, Ye.; Shafronenko, А.; Pliss, І.
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.