Evolutionary Algorithms Based on Cat Swarm Optomozation in Fuzzy Clustering Tasks - Shafronenko, A. Yu.; Bodyanskiy, Ye. V.; Rudenko, D. A. (My University, 2020)
Computational intelligence methods are widely used to solve many complex problems, both purely scientific, and in the field of engineering, business, finance, medical and technical diagnostics, and other areas related to information processing, including, of course, traditional intellectual analysis: Data Mining and such new directions as Dynamic Data Mining, Data Stream Mining, Big Data Mining, Web Mining, Text Mining, etc. [1-6]. One of the main areas of computational intelligence is evolutionary algorithms, which are essentially certain mathematical models of reproduction or development of biological organisms, inspired by nature and intended, in the most general case, to find the global optimum of multi-extremal functions under uncertainty. Historically, the first evolutionary algorithms were the so-called genetic algorithms, which are based on the selection and genetics mechanisms that implement the survival of the strongest individuals in the process of evolution. The most popular evolutionary bioinspired algorithms for today are the so-called “swarm” procedures (Particle Swarm Optimization - PSO) [7], among which, as for the performance and simplicity of implementation is Cat Swarm Optimization (CSO) [8,9]. These algorithms have confirmed their effectiveness in solving a number of rather complex tasks and have already “managed” to undergo a number of modifications, among which are procedures based on harmonic search, fractional derivatives, adaptation of search parameters and, finally, “crazy cats” [ 10-16]. At the same time, these procedures are not deprived of some shortcomings that degrade the properties of the global extremum search process. The aim of the chapter is to develop a fast-acting and numerically simple method of evolutionary optimization under conditions of multi-extremal goal functions.
Keywords: Fuzzy clustering; cat swarm optimization; neural networks; evolutionary algorithm