Fuzzy Inference NSGA-III Algorithm-Based Multi-Objective Optimization for Switched Reluctance Generator

Abstract

This letter presents a multi-objective optimization strategy for switched reluctance generator based on fuzzy inference non-dominated sorting genetic algorithm III, which is able to search and rank solutions in Pareto front with the preference of designer to overcome the secondary choice difficulty. Mamdani fuzzy inference system is established in line with the decision maker's way of thinking, and is introduced into non-dominated sorting genetic algorithm III, the competitive optimal solutions with relative intensity value are achieved with consideration of the designer's preferences so that the practical decision-making efficiency is improved. The effectiveness of the proposed multi-objective optimization strategy for switched reluctance generator is validated by the simulation verification.