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Research Article

A Hybrid Greedy Sine Cosine Algorithm with Differential Evolution for Global Optimization and Cylindricity Error Evaluation

ORCID Icon, , , & ORCID Icon
Pages 171-191 | Received 06 Sep 2020, Accepted 06 Nov 2020, Published online: 15 Dec 2020

References

  • Abd Elaziz, M., D. Oliva, and S. W. Xiong. 2017. An improved opposition-based sine cosine algorithm for global optimization. Expert Systems with Applications 90:484–500. doi:10.1016/j.eswa.2017.07.043.
  • Attia, A. F., R. A. El Sehiemy, and H. M. Hasanien. 2018. Optimal power flow solution in power systems using a novel sine-cosine algorithm. International Journal of Electrical Power & Energy Systems 99:331–43.
  • Chegini, S. N., A. Bagheri, and F. Najafi. 2018. PSOSCALF: A new hybrid PSO based on sine cosine algorithm and Levy flight for solving optimization problems. Applied Soft Computing 73:697–726.
  • Das, S., A. Bhattacharya, and A. K. Chakraborty. 2018. Solution of short-term hydrothermal scheduling using sine cosine algorithm. Soft Computing 22:6409–27.
  • Dorigo, M., and G. Di Caro. 1999. Ant colony optimization: A new meta-heuristic. Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), Washington, DC, USA, 1470–77. IEEE.
  • Geem, Z. W., J. H. Kim, and G. V. Loganathan. 2001. A new heuristic optimization algorithm: Harmony search. Simulation 76 (2):60–68.
  • Gupta, S., and K. Deep. 2019. A hybrid self-adaptive sine cosine algorithm with opposition based learning. Expert Systems with Applications 119:210–30.
  • Issa, M., A. E. Hassanien, D. Oliva, A. Helmi, I. Ziedan, and A. Alzohairy. 2018. ASCA-PSO: Adaptive sine cosine optimization algorithm integrated with particle swarm for pairwise local sequence alignment. Expert Systems with Applications 99:56–70.
  • Jensi, R., and G. W. Jiji. 2016. An enhanced particle swarm optimization with levy flight for global optimization. Applied Soft Computing 43:248–61.
  • Karaboga, D., and B. Basturk. 2007. A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. Journal of Global Optimization 39:459–71.
  • Kennedy, J., and R. Eberhart. 1995. Particle swarm optimization. Proceedings of ICNN’95 - International Conference on Neural Networks, Perth, WA, Australia, 1942–48. IEEE.
  • Lai, H. Y., W. Y. Jywe, C. K. Chen, and C. H. Liu. 2000. Precision modeling of form errors for cylindricity evaluation using genetic algorithms. Precision Engineering 24 (4):310–19.
  • Lei, X. Q., H. W. Song, Y. J. Xue, J. S. Li, J. Zhou, and M. D. Duan. 2011. Method for cylindricity error evaluation using geometry optimization searching algorithm. Measurement 44 (9):1556–63.
  • Li, J. S., X. Q. Lei, Y. J. Xue, and M. D. Duan. 2009. Evaluation algorithm of cylindricity error based on coordinate transformation. China Mechanical Engineering 20 (16):1983–87.
  • Li, S., H. J. Fang, and X. Y. Liu. 2018. Parameter optimization of support vector regression based on sine cosine algorithm. Expert Systems with Applications 91:63–77.
  • Liang, J. J., B. Y. Qu, and P. N. Suganthan. 2013. Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Singapore: Zhengzhou University, Zhengzhou, China and Nanyang Technological University.
  • Mirjalili, S. 2015. The ant lion optimizer. Advances in Engineering Software 83:80–98.
  • Mirjalili, S. 2016. SCA: A sine cosine algorithm for solving optimization problems. Knowledge-Based Systems 96:120–33.
  • Mirjalili, S., A. H. Gandomi, S. Z. Mirjalili, S. Saremi, H. Faris, and S. M. Mirjalili. 2017. Salp swarm algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software 114:163–91.
  • Mirjalili, S., and A. Lewis. 2016. The whale optimization algorithm. Advances in Engineering Software 95:51–67.
  • Mirjalili, S., S. M. Mirjalili, and A. Lewis. 2014. Grey wolf optimizer. Advances in Engineering Software 69:46–61.
  • Nenavath, H., and R. K. Jatoth. 2018. Hybridizing sine cosine algorithm with differential evolution for global optimization and object tracking. Applied Soft Computing 62:1019–43.
  • Nenavath, H., D. R. Kumar Jatoth, and D. S. Das. 2018. A synergy of the sine-cosine algorithm and particle swarm optimizer for improved global optimization and object tracking. Swarm and Evolutionary Computation 43:1–30.
  • Passino, K. M. 2002. Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems Magazine 22 (3):52–67.
  • Rashedi, E., H. Nezamabadi-pour, and S. Saryazdi. 2009. GSA: A gravitational search algorithm. Information Sciences 179 (13):2232–48.
  • Rizk-Allah, R. M. 2018. Hybridizing sine cosine algorithm with multi-orthogonal search strategy for engineering design problems. Journal of Computational Design and Engineering 5 (2):249–73.
  • Simon, D. 2008. Biogeography-based optimization. IEEE Transactions on Evolutionary Computation 12 (6):702–13.
  • Sindhu, R., R. Ngadiran, Y. M. Yacob, N. A. H. Zahri, and M. Hariharan. 2017. Sine-cosine algorithm for feature selection with elitism strategy and new updating mechanism. Neural Computing and Applications 28:2947–58.
  • Srinivas, M., and L. M. Patnaik. 1994. Genetic algorithms: A survey. Computer 27 (6):17–26.
  • Storn, R., and K. Price. 1997. Differential evolution – A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11:341–59.
  • Tanabe, R., and A. Fukunaga. 2013. Success-history based parameter adaptation for differential evolution. Proceedings of 2013 IEEE Congress on Evolutionary Computation, Cancún, México, 71–78. IEEE.
  • Tizhoosh, H. R. 2005. Opposition-based learning: A new scheme for machine intelligence. Proceedings of International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC’06), Vienna, Austria, 695–701. IEEE.
  • Wen, X. L., J. C. Huang, D. H. Sheng, and F. L. Wang. 2010. Conicity and cylindricity error evaluation using particle swarm optimization. Precision Engineering 34 (2):338–44.
  • Wolpert, D. H., and W. G. Macready. 1997. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1 (1):67–82.
  • Yang, X. S. 2008. Introduction to mathematical optimization: From linear programming to metaheuristics. Cambridge: Cambridge International Science Publishing.
  • Yang, Y., M. Li, C. Wang, and Q. Y. Wei. 2018. Cylindricity error evaluation based on an improved harmony search algorithm. Scientific Programming 2:1–13.
  • Zhao, Y. B., X. L. Wen, and Y. X. Xu. 2015. Cylindricity error inspection and evaluation based on CMM and QPA. China Mechanical Engineering 26 (18):2432–36.

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