A New Variant of the Multiverse Optimizer Using Multiple Chaotic Maps and Fuzzy Logic for Optimization in CEC-2017 Benchmark Suite

In this work, we are presenting a new variant of the Fuzzy-Chaotic Multiverse Optimizer Algorithm (FCMVO), which includes the use of type-1 Fuzzy logic and chaos theory to improve the original Multiverse Optimizer Algorithm (MVO). This new variant uses more than one chaotic map to improve the overall behavior of the algorithm. A comparison with the Fuzzy-MVO and original MVO was made in benchmark function optimization. Using multiple chaotic maps to replace the generation of random numbers for the original MVO, we are studying the behavior obtained in the algorithm, so we can develop better solutions than the FCMVO algorithm. In our tests we are using the CEC-2017 Single Objective Real-Parameter Numerical Optimization benchmark suite, which is composed of 30 functions, comparing the original MVO and some variants of the algorithm. The main objective of our work is to compare this new variant of the algorithm with the use of multiple chaotic maps in the search for the best solutions in benchmark optimization before real-world testing, such as in fuzzy-controller optimization, is performed.