A Novel Mutation-based Differential Evolution Algorithm for Solving Real-World Optimization Problems

Abstract

In today's environment, evolutionary algorithms play a key role in solving optimization problems in mathematical models and applications. This article introduces a multi-objective differential evolution algorithm that extends the single-objective differential evolution (DE) algorithm to solve optimization problems in mathematical models and applications. Many existing algorithms face issues with diversity loss and convergence rate. To address these problems, this article proposes a novel mutation operator called ubiquitination mutation for DE, which has been added to the DE method. The proposed approach was tested on three real-world optimization problems: the next release problem, the multiline distance minimization problem, and data clustering. Results indicate that the proposed mutation operator outperformed state-of-the-art algorithms. In addition, the proposed approach provided better solutions in both single- and multi-objective platforms for various real-world problems.