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Başlık: The Harris hawks, grasshopper and multi-verse optimization algorithms for the selection of optimal machining parameters in manufacturing operations
Yazarlar: Sait, Sadiq M.
Li, Xinyu
Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makine Mühendisliği Bölümü.
0000-0003-1790-6987
Yıldız, Betül Sultan
Yıldız, Ali Rıza
AAL-9234-2020
F-7426-2011
7102365439
57094682600
Anahtar kelimeler: Harris hawks optimization algorithm
Grasshopper optimization algorithm
Multi-verse optimization algorithm
Manufacturing
Grinding
Design
Structural design optimization
Multiobjective optimization
Water cycle algorithm
Grinding process
Genetic algorithm
Gravitational search
Immune algorithm
Colony algorithm
Topology desing
Differential evolution
Ant colony optimization
Design
Genetic algorithms
Grinding (machining)
Manufacture
Particle swarm optimization (PSO)
Simulated annealing
Differential evolution algorithms
Improved differential evolutions
Manufacturing operations
Optimal machining parameters
Optimization algorithms
Optimization of processing parameters
Particle swarm optimization algorithm
Teaching-learning-based optimizations
Industrial research
Yayın Tarihi: Ağu-2019
Yayıncı: Walter de Gruyter
Atıf: Yıldız, B. S. vd. (2019). ''The Harris hawks, grasshopper and multi-verse optimization algorithms for the selection of optimal machining parameters in manufacturing operations''. Materials Testing, 61(8), 725-733.
Özet: In this research, the Harris hawks optimization algorithm (HHO), the grasshopper optimization algorithm (GOA) and the multi-verse optimization algorithm (MVO) have been used in solving manufacturing optimization problems. This paper is the first research study for the optimization of processing parameters for manufacturing processes using the HHO, the GOA, and the MVO in the literature, and in particular, for grinding operations. A well-known grinding optimization problem is solved to prove how effective the HHO, the GOA and the MVO are in solving manufacturing problems and to demonstrate superiority over other algorithms. The results of the HHO, the GOA and the MVO are compared with other methods such as the genetic algorithm, the ant colony algorithm, the scatter search, the differential evolution algorithm, the particle swarm optimization algorithm, simulated annealing, the artificial bee colony, harmony search, improved differential evolution, the hybrid particle swarm algorithm, teaching learning-based optimization algorithms, the cuckoo search, and the fractal search algorithm. The results show that the HHO, the GOA, and the MVO are efficient optimization approaches for obtaining optimal manufacturing variables in manufacturing operations.
URI: https://doi.org/10.3139/120.111377
https://www.degruyter.com/document/doi/10.1515/9783035624052-007/html
http://hdl.handle.net/11452/29621
ISSN: 0025-5300
2195-8572
Koleksiyonlarda Görünür:Scopus
Web of Science

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