Rat swarm optimizer adjusted by fuzzy inference system

Abstract

The rat swarm optimizer is one of the most recent metaheuristics focused on global optimization. This work proposes a fuzzy mechanism that aims to improve the convergence of this algorithm, adjusting the amplitude of the parameter that directly affects the chasing mechanism of the behavior of rats. The proposed fuzzy model uses the normalized fitness of each individual and the population diversity as input information. For evaluation criteria, the fuzzy mechanism proposed, was implemented in the optimization of third-three single objective problems. For comparison criteria, the proposed fuzzy variant is compared with other algorithms, such as GWO (Grey Wolf Optimizer), SSA (Salp Swarm Algorithm), WOA (Whale Optimization Algorithm), and also with two proposed alternative fuzzy variants. One of the simpler fuzzy variants uses only population diversity as input information, while the other uses only the normalized fitness value of each rat. The results show that the proposed fuzzy system improves the convergence of the conventional version of the rat algorithm and is also competitive with other metaheuristics. The Friedman test shows statistically the results obtained.