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

• Published in: International journal of intelligent computing and cybernetics Vol. 12; no. 2; pp. 224 - 244
• Main Authors: Mohapatra, Usha Manasi, Majhi, Babita, Jagadev, Alok Kumar
• Format: Journal Article
• Language: English
• Published: Bingley Emerald Publishing Limited 15.05.2019; Emerald Group Publishing Limited
• Subjects: Communication; Diffusion rate; Economic models; Evolutionary algorithms; Genetic algorithms; Geographical locations; Heuristic methods; Identification; Machine learning; Neural networks; Noise; Nonlinear systems; Online instruction; Optimization algorithms; Optimization techniques; Parameter estimation; Particle swarm optimization; Researchers; Sensors; Signal processing; System identification; Traveling salesman problem; Wireless networks; Wireless sensor network; Incremental learning strategy; Distributed learning-based cat swarm optimization; System identification; Diffusion learning strategy
• ISSN: 1756-378X; 1756-3798
• DOI: 10.1108/IJICC-10-2018-0146

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.