Akademik Veri Yönetim Sistemi
KNOWLEDGE-BASED SYSTEMS, cilt.271, 2023 (SCI-Expanded)
The present study aims to optimize the engineering design and manufacturing problems with a novel hybrid optimizer named: AOA-NM (Arithmetic optimization-Nelder mead). To overcome the local optima trap shortcoming and improve the solution quality of a recently introduced arithmetic optimization algorithm (AOA), the Nelder-Mead local search methodology has been incorporated into the basic AOA framework. The objective of the proposed hybridization approach was to facilitate the refinement of the exploration-exploitation behaviour of the AOA search. In the numerical validation stage, numerous multidimensional benchmarks from the CEC2020 were used as challenging testing functions to investigate the suggested AOA-NM optimizer. To investigate the viability of the proposed hybridized algorithm in real-world applications, it is investigated for ten constrained engineering de-sign problems, and the performance was contrasted with other distinguished metaheuristics extracted from the literature. Additionally, a hands-on manufacturing problem of milling process parameter optimization and vehicle structure shape optimization is posed and solved at the forefront to evaluate both AOA and AOA-NM efficacy. The proficiency of the AOA-NM algorithm, in terms of both solution quality and stability, is confirmed by performed comparative analysis and found to be robust in handling challenging practical issues.(c) 2023 Published by Elsevier B.V.