Crayfish Optimization Algorithm

Öz

This study aims to improve the performance of the Crayfish Optimization Algorithm (COA), a swarm intelligence algorithm recently introduced in the literature, on various test functions with fixed and variable dimensions. Optimization can be defined as making a system as efficient as possible at the least cost, within certain constraints. Numerous optimization algorithms have been designed in the literature to obtain the best solutions for specific problems. The most critical aspects in solving these problems are modeling the problem correctly, determining the parameters and constraints, and selecting an appropriate meta-heuristic algorithm for solving the objective function. Not every algorithm is suitable for every problem structure. While some algorithms solve fixed-dimension test functions better, others may perform better on variable-dimension test functions. In this study, the COA algorithm's performance was evaluated on 10 test functions previously used in the literature, consisting of three fixed-dimension functions (Schaffer Function, Himmelblau Function, Kowalik Function) and seven variable-dimension functions, including one unimodal (Elliptic Function) and six multimodal functions (Non-Continuous Rastrigin Function, Alpine Function, Levy Function, Weierstrass Function, Michalewicz Function, Dixon & Price Function). The solution values obtained for each of the selected functions were compared with the solutions obtained using the Harris Hawks Optimizer (HHO), the Charged System Search Algorithm (CSS), and the Backtracking Search Optimization Algorithm (BSA).

Anahtar Kelimeler

• Crayfish Optimization algorithm (COA)
• Harris Hawks Optimizer (HHO)
• Charged System Search Algorithm (CSS)
• Backtracking Search Optimization (BSA)
• Fixed and variable size unimodal and multimodal test functions

Kaynakça

1. B., Öztürk, L., Uğur, F., Erzincanlı, and Ö. Küçük, “Optimization of Polyethylene Inserts Design Geometry of Total Knee Prosthesis,” International Scientific and Vocational Studies Journal, vol. 2, no. 2, pp. 31-39, 2018.
2. İ. B., Koç, A. A., Janadi, and V. Ateş, “Interlock Optimization of an Accelerator using Genetic Algorithm,” International Scientific and Vocational Studies Journal, vol. 1, no. 1, 30-41, 2017.
3. Y., Altınok, M., Lüy, N. A., Metin, S., Görgülü Balcı, and F., Acar, “Sustainable Grids: Smart Meter Solutions for Efficient Energy Measurement,” International Scientific and Vocational Studies Journal, vol. 8, no. 1, pp. 49-64, 2024. DOI: 10.47897/bilmes.1485662.
4. K., Eryılmaz, and A., Elen, “Changes in Traffic Density and Vehicle Usage Habits During and After the Pandemic in Balıkesir Province,” International Scientific and Vocational Studies Journal, vol. 8, no. 2, pp. 235-252, 2024. DOI: 10.47897/bilmes.1602255.
5. S., Özyön, C., Yaşar, and H., Temurtaş, “Kaos Tabanlı Yerçekimsel Arama Algoritmaları (CbGSA-X) için Test Fonksiyonları,” Düzce Üniversitesi Bilim ve Teknoloji Dergisi, vol. 8, no. 3, pp. 1771-1793, 2020.
6. F. Cantaş, S. Özyön, and C. Yaşar, “Runge Kutta Optimization for Fixed Size Multimodal Test Functions,” International Scientific and Vocational Studies Journal, vol. 6, no. 2, pp. 144-155, 2022. DOI: 10.47897/bilmes.1219033.
7. H., Jia, H., Rao, C., Wen, and S., Mirjalili, “Crayfish Optimization Algorithm,” Artificial Intelligence Review, vol. 56 (Suppl 2), pp. 1919-1979, 2023. DOI: 10.1007/s10462-023-10567-4.
8. S., Özyön, C., Yaşar, and H., Temurtaş, “Particle Swarm Optimization Algorithm Applied to Environmental Economic Power Dispatch Problems Consisting of Thermal Units,” In 6th International Advanced Technologies Symposium (IATS 2011), Electrical & Electronics Technologies Papers, vol. 4, EAE-39, pp. 175-180, Elâzığ, Turkey, May 16-18, 2011.

9. D., Aydın, G., Yavuz, S., Özyön, C., Yaşar, and T., Stützle, “Artificial Bee Colony Framework to Non-convex Economic Dispatch Problem with Valve Point Effects,” In Genetic and Evolutionary Computation Conference Companion (GECCO’17), pp. 1311-1318, Berlin, Germany, July 15–17, 2017. ISBN: 978-1-4503-4939-0.
10. C., Yaşar, and S., Özyön, “A Modified Incremental Gravitational Search Algorithm for Short-term Hydrothermal Scheduling with Variable Head,” Engineering Applications of Artificial Intelligence, vol. 95, pp. 1-17. article 103845, 2020. DOI: 10.1016/j.engappai.2020.103845.
11. A., Kaveh, and A., Dadras, “A Novel Meta-heuristic Optimization Algorithm: Thermal Exchange Optimization,” Advances in Engineering Software, vol. 110, pp. 69-84, 2017. DOI: 10.1016/j.advengsoft.2017.03.014.
12. S., Özyön, B., Durmuş, G., Kuvat, and G., Özcan, “Yüksek Boyutlu Problemlerin Optimizasyonunda Parametre Seçiminin Genetik Algoritma Performansına Etkileri,” In International Multidisciplinary Congress of Eurasia (IMCOFE’15), pp. 842-859, Üsküp, Macedonia, September 1-5, 2015. ISBN: 978-9944-0637-1-5.
13. P., Civicioglu, “Backtracking Search Optimization Algorithm for Numerical Optimization Problems,” Applied Mathematics and Computation, vol. 219, no. 15, pp. 8121-8144, 2013. DOI: 10.1016/j.amc.2013.02.017.
14. S., Özyön, Differential Evolution Algorithm with Incremental Social Learning,” Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, vol. 7, no. 1, pp. 133-163, 2020. DOI: 10.35193/bseufbd.666626.
15. S., Özyön, C., Yaşar, and H., Temurtaş, “Harmoni Arama Algoritmasının Çevresel Ekonomik Güç Dağıtım Problemlerine Uygulanması,” Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, vol. 26, no. 2, pp. 65-76, 2011.
16. R. V., Rao, V. J., Savsani, and D. P., Vakharia, “Teaching-learning-based Optimization: A Novel Method for Constrained Mechanical Design Optimization Problems,” Computer-Aided Design, vol. 43, no. 3, pp. 303-315, 2011. DOI: 10.1016/j.cad.2010.12.015.
17. A. A., Heidari, S., Mirjalili, H., Faris, I., Aljarah, M., Mafarja, and H., Chen, “Harris Hawks Optimization: Algorithm and Applications,” Future Generation Computer Systems, vol. 97, pp. 849-872, 2019. DOI: 10.1016/j.future.2019.02.028.
18. O., Akdağ, A., Ateş, and C., Yeroğlu, “Harris Şahini Optimizasyon Algoritması ile Aktif Güç Kayıplarının Minimizasyonu,” Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, vol. 22, no. 65, pp. 481-490, 2020. DOI: 10.21205/deufmd.2020226516.
19. A., Kaveh, and S. Talatahari, “A Novel Heuristic Optimization Method: Charged System Search,” Acta Mechanica, vol. 213, no. 3, pp. 267-289, 2010. DOI: 10.1007/s00707-009-0270-4.
20. F. E., Durak, B., Hiçdurmaz, and S., Özyön, “The Design of Bessel Type High-pass Active Filter with Charged System Search Algorithm,” International Scientific and Vocational Journal, vol. 3, no. 2, pp. 76-84, 2019.