The Fast Modification of Evolutionary Bioinspired Cat Swarm Optimization Method

Abstract

This paper discusses the optimization problem based on cat swarm optimization by introducing elements of a random search in a stochastic modification of the basic procedure into the seeking and tracing modes, that improves the speed and accuracy of determining the direction of movement in the seeking mode and improves the global properties of the procedure in the tracing mode. The proposed optimization method, being a representative of evolutionary algorithms, is intended for using hybrid systems of computational intelligence and, above all, in learning tasks of artificial neural networks, neuro-fuzzy systems, as well as in clustering and classification problems.