Farklı Donör-Alıcı Konfigürasyonları ile İmmün Plazma Algoritmasının Performans Analizi

Öz

İmmün Plazma Algoritması (IPA), immün plazma transfer tedavisinden ilham alan yeni bir meta-sezgisel algoritmadır. Karmaşık optimizasyon problemlerini çözmek için birçok meta-sezgisel algoritma kullanılır, ancak performansları çoğunlukla 30 boyutlu problemlerde denetlenmiştir. Günümüzde 50'den fazla boyutlu yüksek boyutlu optimizasyon problemlerinin çözülmesini gerektiren çok daha karmaşık sistemlerle uğraşıyoruz, ancak bu meta-sezgisel algoritmaların yüksek boyutlu problemler için performansı çoğunlukla incelenmemiştir. Bu problemin üstesinden gelmek için bu çalışmada IPA'nın yüksek boyutlu problemlerin çözümündeki performansı araştırılmıştır. Bu durumda, 100 boyutlu beş iyi bilinen kıyaslama optimizasyon problemini (Sphere, Quartic, Rastrigin, Ackley ve Griewank fonksiyonları) çözmek için IPA kullanılmıştır. Devamında, bazı son teknoloji meta-sezgisel algoritmalarla karşılaştırılmıştır. Deneysel sonuçlar, IPA'nın en iyi amaç değerlerini bulmada, bu algoritmalardan daha iyi performans gösterdiğini ve test edilen optimizasyon problemlerinin çoğu için en iyi standart sapmaya ve en iyi ortalama değere sahip olduğunu göstermektedir.

Anahtar Kelimeler

• Yüksek boyutlu Problemler
• Optimizasyon
• İmmün Plazma Algoritması

Performance Analysis of Immune Plasma Algorithm with Different Donor-Receiver Configurations

Abstract

Immune Plasma Algorithm (IPA) is a novel meta-heuristic algorithm inspired by immune plasma transfer treatment. Many meta-heuristic algorithms are used for solving complex optimization problems, but their performance is mostly inspected on problems with 30 dimensions. Nowadays we are dealing with far more complex systems that require solving high-dimensional optimization problems with over 50 dimensions whereas performance of meta-heuristic algorithms for high-dimensional problems is mostly unexamined. So to overcome this problem, in this study, performance of IPA on solving high-dimensional problems is investigated. In this case, it is used to solve five well-known benchmark optimization problems with 100 dimensions. In this work, Immune Plasma Algorithm (IPA) is used for solving Sphere, Quartic, Rastrigin, Ackley and Griewank functions. It is compared with some other state-of-the-art meta-heuristic algorithms. Experimental results demonstrate that IPA outperforms these algorithms in finding best objective values, and has best standard deviation, and best mean value for most of the tested optimization problems.

Keywords

• High-dimensional Problems
• Optimization
• Immune Plasma Algorithm

References

1. Jia, D., Zheng, G., Qu, B., and Khan, M. K., “A hybrid particle swarm optimization algorithm for high-dimensional problems.” Computers and Industrial Engineering, 61(4), pp. 1117-1122, 2011.
2. Li, Z., Wang, W., Yan, Y., and Li, Z., “PS–ABC: A hybrid algorithm based on particle swarm and artificial bee colony for high-dimensional optimization problems.” Expert Systems with Applications, 42(22), pp. 8881-8895. 2015
3. Lai, Z., Feng, X., and Yu, H. “An Improved Animal Migration Optimization Algorithm Based on Interactive Learning Behavior for High Dimensional Optimization Problem.” In 2019 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS), pp. 110-115, May 2019, IEEE.
4. Nautiyal, B., Prakash, R., Vimal, V., Liang, G., and Chen, H. “Improved Salp Swarm Algorithm with mutation schemes for solving global optimization and engineering problems.” Engineering with Computers, pp. 1-23. 2021.
5. I. Boussaid, J. Lepagnot, P. Siarry, “A survey on optimization metaheuristics”, Inf. Sci., vol. 237, pp. 82-117, 2013.
6. X.-S. Yang, “Nature-inspired optimization algorithms: Challenges and open problems”, J. Comput. Sci., vol. 46, 2020.
7. Aslan, S., and Demirci, S., “Immune Plasma Algorithm: A Novel Meta-Heuristic for Optimization Problems.” IEEE Access, 8, pp.220227-220245, 2020.
8. U.S. Department of Health and Human Services, “Understanding the immune system how it works”, Nat. Inst. Allergy Infectious Diseases, Washington, DC, USA, Tech. Rep. 07-5423:1-63, 2007.

9. C. Shen, Z. Wang, F. Zhao, Y. Yang, J. Li, J. Yuan, F. Wang, D. Li, M. Yang, L. Xing, “Treatment of 5 critically ill patients with COVID-19 with convalescent plasma”, Jama, vol. 323, no. 16, pp. 1582-589, 2020.
10. Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., and Mirjalili, S. M. “Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems”. Advances in Engineering Software, 114, pp. 163-191. 2017.
11. Yang, X. S. “Firefly algorithm, Levy flights and global optimization.” In Research and development in intelligent systems XXVI, pp. 209-218. 2010. Springer, London.
12. Mirjalili, S., Mirjalili, S. M., and Lewis, A. “Grey wolf optimizer.” Advances in engineering software, 69, pp. 46-61. (2014).
13. Mirjalili, S. “Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm.” Knowledge-based systems, 89, pp. 228-249. (2015).
14. Mirjalili, S. (2016). SCA: a sine cosine algorithm for solving optimization problems. Knowledge-based systems, 96, pp. 120-133.