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On the development of cat swarm metaheuristic using distributed learning strategies and the applications

Usha Manasi Mohapatra (Department of Computer Science and Information Technology, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, India)
Babita Majhi (Department of Computer Science and Information Technology, G.G. Vishwavidyalaya, Central University, Koni Bilaspur, India)
Alok Kumar Jagadev (School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India)

International Journal of Intelligent Computing and Cybernetics

ISSN: 1756-378X

Article publication date: 15 May 2019

Issue publication date: 15 May 2019

86

Abstract

Purpose

The purpose of this paper is to propose distributed learning-based three different metaheuristic algorithms for the identification of nonlinear systems. The proposed algorithms are experimented in this study to address problems for which input data are available at different geographic locations. In addition, the models are tested for nonlinear systems with different noise conditions. In a nutshell, the suggested model aims to handle voluminous data with low communication overhead compared to traditional centralized processing methodologies.

Design/methodology/approach

Population-based evolutionary algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and cat swarm optimization (CSO) are implemented in a distributed form to address the system identification problem having distributed input data. Out of different distributed approaches mentioned in the literature, the study has considered incremental and diffusion strategies.

Findings

Performances of the proposed distributed learning-based algorithms are compared for different noise conditions. The experimental results indicate that CSO performs better compared to GA and PSO at all noise strengths with respect to accuracy and error convergence rate, but incremental CSO is slightly superior to diffusion CSO.

Originality/value

This paper employs evolutionary algorithms using distributed learning strategies and applies these algorithms for the identification of unknown systems. Very few existing studies have been reported in which these distributed learning strategies are experimented for the parameter estimation task.

Keywords

Citation

Mohapatra, U.M., Majhi, B. and Jagadev, A.K. (2019), "On the development of cat swarm metaheuristic using distributed learning strategies and the applications", International Journal of Intelligent Computing and Cybernetics, Vol. 12 No. 2, pp. 224-244. https://doi.org/10.1108/IJICC-10-2018-0146

Publisher

:

Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited

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