SCWOA: an efficient hybrid algorithm for parameter optimization of multi-pass milling process

References

• Agapiou JS. The optimization of machining operations based on a combined criterion, part 2: multipass operations. J Eng Ind. 1992;114:508–513.10.1115/1.2900705
   Google Scholar
• Baskar N, Asokan P, Saravanan R, et al. Selection of optimal machining parameters for multi-tool milling operations using a memetic algorithm. J Mater Process Technol. 2006;174:239–249.10.1016/j.jmatprotec.2005.09.032
   Web of Science ®Google Scholar
• Gao L, Huang J, Li X. An effective cellular particle swarm optimization for parameters optimization of a multi-pass milling process. Appl Soft Comput. 2012;12:3490–3499.10.1016/j.asoc.2012.06.007
   Web of Science ®Google Scholar
• Huang J, Gao L, Li X. An effective teaching-learning-based cuckoo search algorithm for parameter optimization problems in structure designing and machining processes. Appl Soft Comput. 2015;36:349–356.10.1016/j.asoc.2015.07.031
   Web of Science ®Google Scholar
• Khalilpourazari S, Pasandideh SHR. Multi-item EOQ model with nonlinear unit holding cost and partial backordering: moth-flame optimization algorithm. J Ind Prod Eng. 2016;34:42–51.
   Google Scholar
• Khalilpourazari S, Khalilpourazary S. A lexicographic weighted Tchebycheff approach for multi-constrained multi-objective optimization of the surface grinding process. Eng Optim. 2017;49:878–895.10.1080/0305215X.2016.1214437
   Web of Science ®Google Scholar
• Khalilpourazari S, Pasandideh SHR, Niaki STA. Optimization of multi-product economic production quantity model with partial backordering and physical constraints: SQP, SFS, SA, and WCA. Appl Soft Comput. 2016;49:770–791.10.1016/j.asoc.2016.08.054
   Web of Science ®Google Scholar
• Khalilpourazari S, Pasandideh SHR. Bi-objective optimization of multi-product EPQ model with backorders, rework process and random defective rate. In: Industrial Engineering (ICIE), 12th International Conference on; Jan 25–27; Tehran, Iran; 2016. p. 36–40.
   Google Scholar
• Khalilpourazari S, Mohammadi M. Optimization of closed-loop Supply chain network design: a Water Cycle Algorithm approach. In: Industrial Engineering (ICIE), 2016 12th International Conference on; Jan 25–27; Tehran, Iran; 2016. p. 41–45.
   Google Scholar
• Mirjalili S. SCA: A Sine Cosine Algorithm for solving optimization problems. Knowl-Based Syst. 2016;96:120–133.10.1016/j.knosys.2015.12.022
   Web of Science ®Google Scholar
• Mirjalili S, Lewis A. The whale optimization algorithm. Adv Eng Software. 2016;95:51–67.
   Web of Science ®Google Scholar
• Mellal MA, Williams EJ. Total production time minimization of a multi-pass milling process via cuckoo optimization algorithm. Int J Adv Manuf Technol. 2016;1:1–8.
   Google Scholar
• Onwubolu GC. Performance-based optimization of multi-pass face milling operations using Tribes. Int J Mach Tools Manuf. 2006;46:717–727.10.1016/j.ijmachtools.2005.07.041
   Web of Science ®Google Scholar
• Pawar PJ, Rao RV. Parameter optimization of machining processes using teaching–learning-based optimization algorithm. Int J Adv Manuf Technol. 2013;67:995–1006.10.1007/s00170-012-4524-2
   Web of Science ®Google Scholar
• Rao RV, Pawar PJ. Parameter optimization of a multi-pass milling process using non-traditional optimization algorithms. Appl Soft Comput. 2010;10:445–456.
   Web of Science ®Google Scholar
• Shunmugam MS, Bhaskara Reddy SB, Narendran TT. Selection of optimal conditions in multi-pass face-milling using a genetic algorithm. Int J Mach Tools Manuf. 2000;40:401–414.10.1016/S0890-6955(99)00063-2
   Web of Science ®Google Scholar
• Smith S, Tlusty J. An overview of modeling and simulation of the milling process. J Eng Ind. 1991;113:169–175.10.1115/1.2899674
   Web of Science ®Google Scholar
• Sönmez AI, Baykasoǧlu A, Dereli T, et al. Dynamic optimization of multipass milling operations via geometric programming. Int J Mach Tools Manuf. 1999;39:297–320.10.1016/S0890-6955(98)00027-3
   Web of Science ®Google Scholar
• Tschätsch H. “Milling”, Applied machining technology. London: Springer Science & Business Media; 2010. p. 173–225.
   Google Scholar
• Watkins WA, Schevill WE. Aerial observation of feeding behavior in four baleen whales: Eubalaena glacialis, Balaenoptera borealis, Megaptera novaean- gliae and Balaenoptera physalus. J Mammal. 1979;60:155–163.
   Web of Science ®Google Scholar
• Wang ZG, Rahman M, Wong YS, et al. Optimization of multi-pass milling using parallel genetic algorithm and parallel genetic simulated annealing. Int J Mach Tools Manuf. 2005;45:1726–1734.10.1016/j.ijmachtools.2005.03.009
   Web of Science ®Google Scholar
• Yang WA, Guo Y, Liao WH. Optimization of multi-pass face milling using a fuzzy particle swarm optimization algorithm. Int J Adv Manuf Technol. 2011;54:45–57.10.1007/s00170-010-2927-5
   Web of Science ®Google Scholar
• Yang Y, Li X, Gao L. Parameters optimization of a multi-pass milling process based on imperialist competitive algorithm. In: Computer Supported Cooperative Work in Design (CSCWD), IEEE 17th International Conference on; Jun 27–30; 2013. p. 406–410.
   Google Scholar
• Yıldız AR. A novel hybrid immune algorithm for global optimization in design and manufacturing. Rob Comput Integr Manuf. 2009;25:261–270.
   Web of Science ®Google Scholar
• Khalilpourazari S, Khalilpourazary S. Optimization of production time in the multi-pass milling process via a Robust Grey Wolf Optimizer. Neural Comput Appl. 2016:1–16.
   Google Scholar
• Wu Q, Gao L, Li X, et al. Applying an electromagnetism-like mechanism algorithm on parameter optimisation of a multi-pass milling process. Int J Prod Res. 2013;51:1777–1788.10.1080/00207543.2012.709643
   Web of Science ®Google Scholar