An Efficient Hybrid Metaheuristic Algorithm ReDe-NM for Solving Industrial Optimization Problems

Abstract

To address the pressing demand for effective solutions to complex optimization challenges, researchers have increasingly focused on pioneering novel methodologies. In this study, a novel hybrid optimization approach termed 'Roulette Wheel Selection and Nelder-Mead-based Improved Differential Evolution' (ReDE-NM) is introduced to optimize real-world industrial problems. Initially, the proposed method is validated by applying it to tackle two mechanical engineering design problems. Subsequently, we compare the optimization results obtained with ReDE-NM against those achieved by state-of-the-art methods featured in the existing literature. Our findings reveal that the proposed method demonstrates rapid convergence and outperforms most benchmark problems in terms of solution quality and computational efficiency. Moreover, ReDE-NM is applied to fine-tune crucial cutting parameters during the turning operation of X210Cr12 steel, leveraging a multilayer-coated carbide insert (GC-4215). The fitness functions are derived for tangential cutting force, and surface roughness, with mathematical expressions for these objectives generated using the Response Surface Methodology (RSM).