A new health-based metaheuristic algorithm: cholesterol algorithm

Authors

  • Serap Ulusam Seçkiner Gaziantep University
  • Şeyma Yilkici Yüzügüldü Gaziantep University

DOI:

https://doi.org/10.12928/ijio.v4i2.7651

Keywords:

Cholesterol Algorithm, Optimization, Meta-heuristics, Continuous functions

Abstract

This paper seeks to explore the effectiveness of a new health-based metaheuristic algorithm inspired by the cholesterol metabolism of the human body. In the study, the main idea is the focus on the performance of the cholesterol algorithm on unconstrained continuous optimization problems. The performances of the proposed cholesterol algorithm are evaluated based on 23 comparison tests and results were compared with Particle Swarm Optimization, Genetic Algorithm, Grey Wolf Optimization, Whale Optimization Algorithm, Harris Hawks Optimization, Differential Evolution, FireFly Algorithm, Cuckoo Search, Multi-Verse Optimizer, and JAYA algorithms. Results showed that this novel cholesterol algorithm implementation can compete effectively with the best-known solution to test functions.

References

K. Sörensen and F. Glover, "Metaheuristics," Encycl. Oper. Res. Manage. Sci., vol. 62, pp. 960-970, 2013. doi: https://doi.org/10.1007/978-1-4419-1153-7_1167.

J. H. Holland, "Adaptation in natural and artificial systems : an introductory analysis with applications to biology, control, and artificial intelligence," in MIT Press, 1975, doi: https://doi.org/10.7551/mitpress/1090.001.0001

R. Storn and K. Price, “Differential Evolution - A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces,” J. Glob. Optim., Vol 11, pp 341-359, 1997, doi: https://doi.org/10.1023/A:1008202821328

J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proc. ICNN'95 - Int. Conf. on Neural Networks, 1995, pp. 1942-1948. doi: https://doi.org/10.1109/ICNN.1995.488968.

X. S. Yang and S. Deb, "Cuckoo search via Lévy flights," in Proc. 2009 World Congress on Nature & Biologically Inspired Computing (NABIC), Coimbatore, India, 2009, pp. 210-214. doi: https://doi.org/10.1109/NABIC.2009.5393690.

X. S. Yang, "Firefly algorithms for multimodal optimization," in Nature Inspired Cooperative Strategies for Optimization (NISCO), L. C. Jain and P. N. Suganthan, Eds., Springer, Berlin, Heidelberg, 2009, pp. 169-178. doi: 10.1007/978-3-642-04944-6_14.

S. Mirjalili and A. Lewis, "The Whale Optimization Algorithm," Adv. Eng. Softw., vol. 95, pp. 51-67, 2016. doi: https://doi.org/10.1016/j.advengsoft.2016.01.008.

A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, and H. Chen, "Harris hawks optimization: Algorithm and applications," Future Gener. Comput. Syst., vol. 97, pp. 849-872, 2019. doi: https://doi.org/10.1016/j.future.2019.02.028.

S. Mirjalili, S. M. Mirjalili, and A. Lewis, "Grey Wolf Optimizer," Adv. Eng. Softw., vol. 69, pp. 46-61, 2014. doi: https://doi.org/10.1016/j.advengsoft.2013.12.007.

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. doi: https://doi.org/10.1007/s00521-015-1870-7.

R. Venkata Rao, "Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems," Int. J. Ind. Eng. Comput., 2016. doi: https://doi.org/10.5267/j.ijiec.2015.8.004.

C. M. M. Lawes, S. V. Hoorn, M. R. Law, and A. Rodgers, "Chapter 7 High Cholesterol," in Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors, M. Ezzati, A. D. Lopez, A. Rodgers, and C. J. Murray, Eds., vol. 1, Geneva: World Health Organization, 2004. [Online]. Available: https://hsepedia.com/wp-content/uploads/2018/04/High-Cholesterol.pdf

K. Bloch, "Chapter 12 Cholesterol: Evolution of structure and function," New Compr. Biochem., vol. 20, no. C, pp. 363–381, 1991. doi: https://doi.org/10.1016/S0167-7306(08)60340-3

R. L. Jackson, J. D. Morrisett, and A. M. Gotto, "Lipoprotein structure and metabolism," Physiol. Rev., vol. 56, no. 2, pp. 259-318, 1976. doi: https://doi.org/10.1152/physrev.1976.56.2.259

K. K. Birtcher and C. M. Ballantyne, “Measurement of Cholesterol,” Circulation, vol. 110, no. 11, pp. 296–297, 2004, doi: https://doi.org/10.1161/01.cir.0000141564.89465.4e.

P. Barter et al., "HDL Cholesterol, Very Low Levels of LDL Cholesterol, and Cardiovascular Events," N. Engl. J. Med., 2007. doi: https://doi.org/10.1056/NEJMoa064278.

R. A. Khurma, I. Aljarah, A. Sharieh, and S. Mirjalili, "EvoloPy-FS: An Open-Source Nature-Inspired Optimization Framework in Python for Feature Selection," in Evolutionary Machine Learning Techniques, S. Mirjalili, H. Faris, I. Aljarah (eds.), Algorithms for Intelligent Systems. Springer, Singapore, 2020. doi: https://doi.org/10.1007/978-981-32-9990-0_8.

H. Faris, I. Aljarah, S. Mirjalili, P. A. Castillo, and J. J. Merelo, "EvoloPy: An open-source nature-inspired optimization framework in python," in Proc. 8th Int. Joint Conf. Comp. Intelligence - Volume 0IJCCI, pp. 171-177, Porto, Portugal, 2016. doi: https://doi.org/10.5220/0006048201710177.

J. Kennedy and R. C. Eberhart, "Particle swarm optimization," in Proc. IEEE Int. Conf. Neural Networks, pp. 1942-1948, 1995. doi: https://doi.org/10.1109/ICNN.1995.488968.

S. Mirjalili, S. M. Mirjalili, and A. Lewis, "Grey Wolf Optimizer," Adv. Eng. Softw., vol. 69, pp. 46-61, 2014, doi: https://doi.org/10.1016/j.advengsoft.2013.12.007

S. Mirjalili and A. Lewis, "The Whale Optimization Algorithm," Adv. Eng. Softw., vol. 95, pp. 51-67, 2016, doi: https://doi.org/10.1016/j.advengsoft.2016.01.008

A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, and H. Chen, "Harris hawks optimization: Algorithm and applications," Future Gener. Comput. Syst., vol. 97, pp. 849-872, 2019, doi: https://doi.org/10.1016/j.future.2019.02.028

R. Storn and K. Price, "Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces," J. Glob. Optim., vol. 11, no. 4, pp. 341-359, 1997. doi: https://doi.org/10.1023/A:1008202821328.

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Published

2023-09-11

How to Cite

Seçkiner , S. U. ., & Yilkici Yüzügüldü, Şeyma. (2023). A new health-based metaheuristic algorithm: cholesterol algorithm. International Journal of Industrial Optimization, 4(2), 115–130. https://doi.org/10.12928/ijio.v4i2.7651

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Articles