Stud Multi-Verse Algorithm

Abstract

Recently, a novel bio-inspired optimization algorithm known as Multi-Verse Optimizer (MVO) has been proposed for solving optimization problems based on the fundamental multi-verse theory including concepts such as white holes, black holes, and wormholes. The objective of this study was to present an optimization algorithm using MVO as well as the stud selection and crossover (SSC) operator, namely the Stud Multi-Verse Algorithm (Stud MVO), in order to improve the performance of the MVO algorithm. The SCC operator is originated from the Stud Genetic Algorithm (Stud GA), by which the best search agent known as the stud provides optimal information for other search agents in the population using general genetic operators. In order to evaluate the performance of the Stud MVO, twenty-three benchmark functions including unimodal, multimodal and fixed-dimension multimodal benchmark functions were used. The comparison of the results indicated that Stud MVO outperformed the MVO algorithm in twenty benchmark functions.