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All issues Volume 87 (2019) E3S Web Conf., 87 (2019) 01006 References
Open Access
Issue
E3S Web Conf.
Volume 87, 2019
1st International Conference on Sustainable Energy and Future Electric Transportation (SeFet 2019)
Article Number 01006
Number of page(s) 9
DOI https://doi.org/10.1051/e3sconf/20198701006
Published online 22 February 2019
  1. D. Simon, “Biogeography-Based Optimization,” IEEE Trans. Evol. Comput., vol. 12, no. 6, pp. 702–713, Dec. 2008. [CrossRef] [Google Scholar]
  2. S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer,” Adv Eng Softw, vol. 69, p. 46, 2014. [CrossRef] [Google Scholar]
  3. S. Mirjalili, “The ant lion optimizer,” Adv. Eng. Softw., vol. 83, pp. 80–98, 2015. [CrossRef] [Google Scholar]
  4. S. Mirjalili, “Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm,” Knowledge-Based Syst., vol. 89, pp. 228–249, 2015. [CrossRef] [Google Scholar]
  5. S. Mirjalili, S. M. Mirjalili, and A. Hatamlou, “Multi-Verse Optimizer: a nature-inspired algorithm for global optimization,” Neural Comput. Appl., vol. 27, no. 2, pp. 495–513, 2016. [CrossRef] [Google Scholar]
  6. S. Mirjalili, “Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems,” Neural Comput. Appl., vol. 27, no. 4, pp. 1053–1073, 2016. [CrossRef] [Google Scholar]
  7. S. Mirjalili, “SCA: A Sine Cosine Algorithm for solving optimization problems,” Knowledge-Based Syst., vol. 96, pp. 120–133, 2016. [CrossRef] [Google Scholar]
  8. H. Shareef, A. A. Ibrahim, and A. H. Mutlag, “Lightning search algorithm,” Appl. Soft Comput. J., vol. 36, pp. 315–333, 2015. [CrossRef] [Google Scholar]
  9. D. Chaohua, C. Weirong, and Z. Yunfang, “Seeker optimization algorithm,” 2006 Int. Conf. Comput. Intell. Secur. ICCIAS 2006, vol. 1, pp. 225–229, 2007. [Google Scholar]
  10. M. D. Li, H. Zhao, X. W. Weng, and T. Han, “A novel nature-inspired algorithm for optimization: Virus colony search,” Adv. Eng. Softw., vol. 92, pp. 65–88, 2016. [CrossRef] [Google Scholar]
  11. S. Mirjalili and A. Lewis, “The Whale Optimization Algorithm,” Adv. Eng. Softw., vol. 95, pp. 51–67, 2016. [CrossRef] [Google Scholar]
  12. Z. Bayraktar, M. Komurcu, and D. H. Werner, “Wind Driven Optimization (WDO): A novel nature-inspired optimization algorithm and its application to electromagnetics,” 2010 IEEE Int. Symp. Antennas Propag. CNC-USNC/URSI Radio Sci. Meet. - Lead. Wave, AP-S/URSI 2010, no. 1, pp. 0–3, 2010. [Google Scholar]
  13. S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer,” Adv. Eng. Softw., vol. 69, pp. 46–61, 2014. [CrossRef] [Google Scholar]
  14. J. G. Vlachogiannis and K. Y. Lee, “Economic load dispatch - A comparative study on heuristic optimization techniques with an improved coordinated aggregation-based PSO,” IEEE Trans. Power Syst., vol. 24, no. 2, pp. 991–1001, 2009. [CrossRef] [Google Scholar]
  15. M. Herwan Sulaiman, W. Lo Ing, Z. Mustaffa, and M. Rusllim Mohamed, “GREY WOLF OPTIMIZER FOR SOLVING ECONOMIC DISPATCH PROBLEM WITH VALVE-LOADING EFFECTS,” vol. 10, no. 21, 2015. [Google Scholar]
  16. A. Bhardwaj, V. K. Kamboj, V. K. Shukla, B. Singh, and P. Khurana, “Unit commitment in electrical power system - A literature review,” in 2012 IEEE International Power Engineering and Optimization Conference, PEOCO 2012 - Conference Proceedings, 2012. [Google Scholar]
  17. S. Mirjalili, “How effective is the Grey Wolf optimizer in training multi-layer perceptrons,” Appl. Intell., vol. 43, no. 1, pp. 150–161, Jul. 2015. [CrossRef] [Google Scholar]
  18. T.-S. Pan, T.-K. Dao, T.-T. Nguyen, and S.-C. Chu, “A Communication Strategy for Paralleling Grey Wolf Optimizer,” Springer, Cham, 2016, pp. 253–262. [Google Scholar]
  19. M. A. Tawhid and A. F. Ali, “A Hybrid grey wolf optimizer and genetic algorithm for minimizing potential energy function,” Memetic Comput., vol. 9, no. 4, pp. 347–359, Dec. 2017. [CrossRef] [Google Scholar]
  20. D. Jitkongchuen, “A hybrid differential evolution with grey wolf optimizer for continuous global optimization,” in 2015 7th International Conference on Information Technology and Electrical Engineering (ICITEE), 2015, pp. 51–54. [Google Scholar]
  21. S. Zhang, Q. Luo, and Y. Zhou, “Hybrid Grey Wolf Optimizer Using Elite Opposition-Based Learning Strategy and Simplex Method,” Int. J. Comput. Intell. Appl., vol. 16, no. 2, p. 1750012, Jun. 2017. [CrossRef] [Google Scholar]
  22. N. Mittal, U. Singh, and B. S. Sohi, “Modified Grey Wolf Optimizer for Global Engineering Optimization,” Appl. Comput. Intell. Soft Comput., vol. 2016, pp. 1–16, May 2016. [CrossRef] [Google Scholar]
  23. N. Singh and S. B. Singh, “A Modified Mean Gray Wolf Optimization Approach for Benchmark and Biomedical Problems.,” Evol. Bioinform. Online, vol. 13, p. 1176934317729413, 2017. [PubMed] [Google Scholar]
  24. N. Singh and S. B. Singh, “Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance,” J. Appl. Math., vol. 2017, pp. 1–15, Nov. 2017. [CrossRef] [Google Scholar]
  25. S. Gupta and K. Deep, “A novel Random Walk Grey Wolf Optimizer,” Swarm Evol. Comput., Jan. 2018. [Google Scholar]
  26. A. H. Gandomi, Y. X-s, and A. A. C. search algorithm:, “a metaheuristic approach to solve structural optimization problems,” Eng Comput, vol. 29, no. 17, p. 35, 2013. [Google Scholar]
  27. A. Sadollah, A. Bahreininejad, H. Eskandar, and H. M. M. blast algorithm:, “a new population based algorithm for solving constrained engineering optimization problems,” Appl Soft Comput, vol. 13, no. 2592, p. 612, 2013. [CrossRef] [Google Scholar]
  28. G. Ah., Interior search algorithm (ISA): a novel approach for global optimization. ISA Trans, 2014. [Google Scholar]
  29. W. S-j and C. P-t., “Genetic algorithms for nonlinear mixed discrete-integer optimization problems via meta-genetic parameter optimization,” Eng Optim+ A, vol. 3524, no. 137, p. 59, 1995. [Google Scholar]
  30. T. K. Sharma, M. Pant, and V. Singh, “Improved local search in artificial bee colony using golden section search. arXiv,” 2012. [Google Scholar]
  31. K. Deb and M. A. Goyal, “combined genetic adaptive search (GeneAS) for engineering design,” Comput Sci Inf., vol. 26, no. 30, p. 45, 1996. [Google Scholar]
  32. B. Kannan and K. Sn., No Title. An augmented Lagrange multiplier based method for mixed integer discrete continuous optimization. [Google Scholar]
  33. Seyedali Mirjalili, The Ant Lion Optimizer, Advances in Engineering Software,Volume 83,2015, Pages 80-98, ISSN 0965-9978, https://doi.org/10.1016/j.advengsoft.2015.01.010. [CrossRef] [Google Scholar]

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