Comparative Analysis of Parametric Optimization Techniques: Fuzzy DSS for Medical Diagnostics

Abstract

This paper addresses the issues of the research and comparative analysis of the advanced bio-inspired techniques for parametric optimization of fuzzy systems (FS). Several modifications of particle swarm optimization (PSO) and grey wolf optimization (GWO) multi-agent algorithms, adapted for optimization of FS parameters, are compared with the conventional search methods. The studies and comparative analysis are conducted on a specific example, namely, at parametric optimization of the fuzzy decision support system (DSS) of Takagi-Sugeno type for the medical diagnostics. The obtained simulation results confirm the high efficiency of the presented bio-inspired optimization techniques, taking into account the achieved performance of the DSS and the spent computing costs.