Multiverse Optimizer Variants with Chaotic Maps and Fuzzy Logic for Multiple Benchmark Optimization.

This work proposes multiple variants of the Multiverse Optimizer Algorithm (MVO) with the use of Chaotic Maps and Fuzzy Logic. These variants come from the Fuzzy-Chaotic Multiverse Optimizer Algorithm (FCMVO), and the purpose is to analyze the behavior of the algorithm in some benchmark mathematical functions, to compare in which cases can perform best between the variants presented. The use of chaotic maps resides from some of the most used in the literature on optimization algorithms, and the main use is to adjust some of the parameters of behavior in the MVO algorithm; and other implementation is the use of Fuzzy Logic for dynamical adaptation of two parameters in the algorithm. In this study, we are presenting an extended comparison between the variants obtained from diverse chaotic maps, that in previous studies, we delimited to six variants which we called Elitist FCMVO. This comparison is done by evaluating the resulting variants from a set of three chaotic maps and comparing also with two different fuzzy inference systems (Mamdani and Sugeno types). The main objective is to present the obtained variants and compare which are the best chaotic maps in the FCMVO algorithm, to improve further in other study cases. [ABSTRACT FROM AUTHOR]