A Differential Evolution Algorithm for CEC 2019 Problems

Year 2023, Volume: 5 Issue: 2, 304 - 311, 27.10.2023

Hatem Dumlu Gurcan Yavuz

https://doi.org/10.46387/bjesr.1311593

Abstract

In this study, a new algorithm, which is a variant of the Differential Evolution algorithm, is proposed. This variant, which we call KU-DGA, is tested with the CEC 2019. As a result of the test, mean, standard deviation and best values were calculated. Moreover, these results are compared with the CEC 2019 results of WOAmM (Enhanced WOA), WOA (Whale Optimization Algorithm), MFO (Moth-flame Optimization Algorithm), BOA (Butterfly Optimization Algorithm), SCA (Sine Cosine Algorithm) and JAYA algorithms in the literature. As a result, the proposed KU-DGA algorithm outperformed its competitors in seven of the ten functions in CEC 2019 (F3,F4,F5,F6,F7,F8,F9,F10) based on the "average" value. Moreover, based on the "best" value results, it outperformed the competing algorithms by getting the most successful results in seven out of ten functions (F1,F5,F6,F7,F8,F9,F10).

Keywords

Self-Adaptive Differential Evolution Algorithm, CEC 2019, Differential Evolution Algorithm, Optimization

CEC 2019 Problemleri İçin Bir Diferansiyel Gelişim Algoritması

Abstract

Bu çalışmada Diferansiyel Gelişim algoritması varyantı olan yeni bir algoritma önerilmiştir. KU-DGA adını verdiğimiz bu varyant CEC 2019 ölçüt seti ile çalıştırılmış ve ortalama, standart sapma ve en iyi değerleri hesaplanmıştır. Ayrıca bu sonuçlar literatürdeki WOAmM(Genişletilmiş WOA), WOA(Balina Optimizasyon Algoritması), MFO(Güve Alevi Optimizasyon Algoritması), BOA(Kelebek Optimizasyon Algoritması), SCA(Sinüs Kosinüs Algoritması) ve JAYA algoritmalarının CEC 2019 sonuçları ile karşılaştırılmıştır. Sonuç olarak önerilen KU-DGA algoritmasının karşılaştırılan algoritmalara kıyasla “ortalama” değer sonuçları baz alındığında CEC 2019’ da yer alan on fonksiyonun yedisinde (F3,F4,F5,F6,F7,F8,F9,F10) rakiplerini geride bırakmıştır. Ayrıca önerilen varyant “en iyi” değer sonuçları baz alındığında on fonksiyondan yedisinde (F1,F5,F6,F7,F8,F9,F10) en başarılı sonuçları alarak rakip algoritmaları geride bırakmıştır.

Keywords

Kendinden Uyarlamalı Diferansiyel Gelişim Algoritması, CEC 2019, Diferansiyel Gelişim Algoritması, Optimizasyon

References

• Z. Meng and C. Yang “Two-stage differential evolution with novel parameter control,” Inf Sci (NY), vol. 596, pp. 321–342, 2022.
• J. Zhang and A. C. Sanderson “JADE: adaptive differential evolution with optional external archive,” IEEE Transactions on evolutionary computation, vol. 13, no. 5, pp. 945–958, 2009.
• R. Tanabe and A. Fukunaga “Evaluating the performance of SHADE on CEC 2013 benchmark problems,” in 2013 IEEE Congress on evolutionary computation, IEEE, 2013, pp. 1952–1959.
• R. Tanabe and A. S. Fukunaga “Improving the search performance of SHADE using linear population size reduction,” in 2014 IEEE congress on evolutionary computation (CEC), IEEE, 2014, pp. 1658–1665.
• E.H. Houssein, H. Rezk, A. Fathy, M. A. Mahdy, and A. M. Nassef “A modified adaptive guided differential evolution algorithm applied to engineering applications,” Eng Appl Artif Intell, vol. 113, p. 104920, 2022.
• W. Deng, S. Shang, X. Cai, H. Zhao, Y. Song, and J. Xu “An improved differential evolution algorithm and its application in optimization problem,” Soft comput, vol. 25, pp. 5277–5298, 2021.
• Z. Tan, K. Li, and Y. Wang, “Differential evolution with adaptive mutation strategy based on fitness landscape analysis,” Inf Sci (NY), vol. 549, pp. 142–163, 2021.
• A.K. Qin, V.L. Huang, and P.N. Suganthan “Differential evolution algorithm with strategy adaptation for global numerical optimization,” IEEE transactions on Evolutionary Computation, vol. 13, no. 2, pp. 398–417, 2008.
• X. Wang, Y. Wang, K.-C. Wong, and X. Li “A self-adaptive weighted differential evolution approach for large-scale feature selection,” Knowl Based Syst, vol. 235, p. 107633, 2022.
• Y. Wang, Z. Cai, and Q. Zhang “Differential evolution with composite trial vector generation strategies and control parameters,” IEEE transactions on evolutionary computation, vol. 15, no. 1, pp. 55–66, 2011.
• T. Sağ “PVS: a new population-based vortex search algorithm with boosted exploration capability using polynomial mutation”, Neural Comput and Applic, vol. 34, pp. 18211-18287, 2022.
• S. Ekinci, D. Izci, E. Eker, and L. Abualigah “An effective control design approach based on novel enhanced aquila optimizer for automatic voltage regulator”, Springer Netherlands, vol. 56, no. 2, 2023.
• M.H. Sulaiman, Z. Mustaffa, M.M. Saari, H. Daniyal, and S. Mirjalili “Evolutionary mating algorithm,” Neural Comput Appl, vol. 35, no. 1, pp. 487–516, 2023.
• Y. Duan and X. Yu “A collaboration-based hybrid GWO-SCA optimizer for engineering optimization problems,” Expert Syst Appl, vol. 213, no. PB, p. 119017, 2023.
• B. Shen, M. Khishe, and S. Mirjalili “Evolving Marine Predators Algorithm by dynamic foraging strategy for real-world engineering optimization problems,” Eng Appl Artif Intell, vol. 123, p. 106207, 2023.
• R. Storn, “Differrential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces, Technical report,” International Computer Science Institute, vol. 11, 1995.
• R. Storn and K. Price “Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces,” Journal of global optimization, vol. 11, no. 4, p. 341, 1997.
• S. Das and P. N. Suganthan “Differential evolution: A survey of the state-of-the-art,” IEEE transactions on evolutionary computation, vol. 15, no. 1, pp. 4–31, 2010.
• C.-W. Chiang, W.-P. Lee, and J.-S. Heh “A 2-Opt based differential evolution for global optimization,” Appl Soft Comput , vol. 10, no. 4, pp. 1200–1207, 2010.
• D. Aydın, G. Yavuz, and T. Stützle “ABC-X: a generalized, automatically configurable artificial bee colony framework,” Swarm Intelligence, vol. 11, pp. 1–38, 2017.
• G. Yavuz and D. Aydın “Improved self-adaptive search equation-based artificial bee colony algorithm with competitive local search strategy,” Swarm Evol Comput, vol. 51, p. 100582, 2019.
• K.V. Price, N.H. Awad, M.Z. Ali, and P.N. Suganthan “Problem definitions and evaluation criteria for the 100-digit challenge special session and competition on single objective numerical optimization,” in Technical Report, Nanyang Technological University Singapore, 2018.
• G. Yavuz “100 Basamak Probleminin JADE Algoritması ile Çözülmesi,” Avrupa Bilim ve Teknoloji Dergisi, no. 21, pp. 493–500, 2021.
• M. López-Ibáñez, J. Dubois-Lacoste, L. P. Cáceres, M. Birattari, and T. Stützle “The irace package: Iterated racing for automatic algorithm configuration,” Operations Research Perspectives, vol. 3, pp. 43–58, 2016.
• S. Chakraborty, A. K. Saha, S. Sharma, S. Mirjalili, and R. Chakraborty “A novel enhanced whale optimization algorithm for global optimization,” Comput Ind Eng, vol. 153, p. 107086, 2021.
• R. Rao “Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems,” International Journal of Industrial Engineering Computations, vol. 7, no. 1, pp. 19–34, 2016.
• S. Mirjalili “SCA: a sine cosine algorithm for solving optimization problems,” Knowl Based Syst, vol. 96, pp. 120–133, 2016.
• S. Arora and S. Singh, “Butterfly optimization algorithm: a novel approach for global optimization,” Soft comput, vol. 23, pp. 715–734, 2019.
• S. Mirjalili “Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm,” Knowl Based Syst, vol. 89, pp. 228–249, 2015.
• S. Mirjalili and A. Lewis, “The whale optimization algorithm,” Advances in engineering software, vol. 95, pp. 51–67, 2016.