IMPROVEMENT OF BELUGA WHALE OPTIMIZATION ALGORITHM BY DISTANCE BALANCE SELECTION METHOD

Year 2023, , 125 - 144, 20.03.2023

Serdar Paçacı

https://doi.org/10.57120/yalvac.1257808

Cited By: 1

Abstract

In this study, an improved version of the Beluga whale optimization (BWO) algorithm, which is a meta-heuristic optimization algorithm recently presented in the literature, is developed to provide better solutions for the problems. The fitness-distance balance (FDB) selection method was applied in the search processes in the BWO algorithm, which was developed by modeling the swimming, preying and falling characteristics of beluga whales. CEC2020 benchmark functions were used to test the performance of the BWO algorithm and the algorithm named FDBBWO. The algorithms were tested on these test functions for 30, 50 and 100 dimensions. Friedman analysis was performed on the test results and the performance ranks of the algorithms were determined. In addition, Wilcoxon rank sum test was used to analyze whether there were significant differences in the results. As a result of the experimental study, it is observed that the BWO algorithm improves the early convergence problem that may arise due to the lack of diversity in the search process. In this way, the possibility of getting stuck at local optimum points is reduced. In addition, the developed algorithm is compared with 3 different algorithms that have been recently presented in the literature. According to the comparison results, FDBBWO has a superior performance compared to other meta-heuristic algorithms.

Keywords

Beluga whale optimization, fitness-distance balance, meta-heuristic optimization

BEYAZ BALİNA OPTİMİZASYON ALGORİTMASININ UYGUNLUK UZAKLIK DENGESİ SEÇİM YÖNTEMİYLE İYİLEŞTİRİLMESİ

Abstract

Özet 1 :
Bu çalışmada son zamanlarda literatüre sunulmuş bir meta-sezgisel optimizasyon algoritması olan Beyaz balina optimizasyon (Beluga whale optimization, BWO) algoritmasının problemlere daha uygun sonuçlar üretmesi amacıyla iyileştirilmiş bir versiyonu geliştirilmiştir. Beyaz balinaların yüzme, avlanma ve ölme özellikleri modellenerek geliştirilmiş olan BWO algoritmasında yer alan arama süreçlerinde uygunluk uzaklık dengesi (fitness-distance balance, FDB) seçim yöntemi uygulanmıştır. BWO algoritması ve FDBBWO ismi verilerek geliştirilen algoritmanın performanslarını test etmek için CEC2020 test fonksiyonları kullanılmıştır. Bu test fonksiyonları üzerinde 30, 50 ve 100 boyut için algoritmalar test edilmiştir. Elde edilen test sonuçlarına Friedman analizi yapılarak algoritmaların performans sıraları belirlenmiştir. Ayrıca Wilcoxon sıralı işaret testiyle de sonuçlar üzerinde anlamlı derecede farklılıklar oluşup oluşmadığı incelenmiştir. Deneysel çalışma sonucunda BWO algoritmasının arama sürecindeki çeşitlilik eksikliği sebebiyle ortaya çıkabilecek olan erken yakınsama probleminin iyileştiği gözlemlenmiştir. Bu sayede yerel optimum noktalara takılma olasılığı azaltılmıştır. Ayrıca geliştirilen algoritma literatüre son zamanlarda sunulmuş olan 3 farklı algoritmayla karşılaştırılmıştır. Karşılaştırma sonuçlarına göre FDBBWO, diğer meta-sezgisel algoritmalara göre daha üstün bir performans sergilemektedir.


Özet 2 :
In this study, an improved version of the Beluga whale optimization (BWO) algorithm, which is a meta-heuristic optimization algorithm recently presented in the literature, is developed to provide better solutions for the problems. The fitness-distance balance (FDB) selection method was applied in the search processes in the BWO algorithm, which was developed by modeling the swimming, preying and falling characteristics of beluga whales. CEC2020 benchmark functions were used to test the performance of the BWO algorithm and the algorithm named FDBBWO. The algorithms were tested on these test functions for 30, 50 and 100 dimensions. Friedman analysis was performed on the test results and the performance ranks of the algorithms were determined. In addition, Wilcoxon rank sum test was used to analyze whether there were significant differences in the results. As a result of the experimental study, it is observed that the BWO algorithm improves the early convergence problem that may arise due to the lack of diversity in the search process. In this way, the possibility of getting stuck at local optimum points is reduced. In addition, the developed algorithm is compared with 3 different algorithms that have been recently presented in the literature. According to the comparison results, FDBBWO has a superior performance compared to other meta-heuristic algorithms.

Keywords

Beyaz balina optimizasyon algoritması, uygunluk uzaklık dengesi seçimi yöntemi, meta-sezgisel optimizasyon

References

• [1] Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization (Vol. 200, pp. 1-10). Technical report-tr06, Erciyes university, engineering faculty, computer engineering department.
• [2] Del Ser, J., Osaba, E., Molina, D., Yang, X. S., Salcedo-Sanz, S., Camacho, D., Das, S., Suganthan P.N., Coello, C. A. C., and Herrera, F. (2019). Bio-inspired computation: Where we stand and what's next. Swarm and Evolutionary Computation, 48, 220-250.
• [3] Golberg D.E., (1989). Genetic Algorithms in Search, Optimization and Machine Learning, Addion Wesley.
• [4] Storn, R., and Price, K. (1997). Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4), 341.
• [5] Mirjalili, S., Mirjalili, S. M., and Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61.
• [6] Kennedy, J., and Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks (Vol. 4, pp. 1942-1948). IEEE.
• [7] Dorigo, M., and Di Caro, G. (1999). Ant colony optimization: a new meta-heuristic. In Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406) (Vol. 2, pp. 1470-1477). IEEE.
• [8] Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design, 43(3), 303-315.
• [9] Rashedi, E., Nezamabadi-Pour, H., and Saryazdi, S. (2009). GSA: a gravitational search algorithm. Information sciences, 179(13), 2232-2248.
• [10] Hashim, F. A., Houssein, E. H., Mabrouk, M. S., Al-Atabany, W., & Mirjalili, S. (2019). Henry gas solubility optimization: A novel physics-based algorithm. Future Generation Computer Systems, 101, 646-667.
• [11] Salimi, H. (2015). Stochastic fractal search: a powerful metaheuristic algorithm. Knowledge-Based Systems, 75, 1-18.
• [12] Cheng, M. Y., and Prayogo, D. (2014). Symbiotic organisms search: a new metaheuristic optimization algorithm. Computers & Structures, 139, 98-112.
• [13] Al-Betar, M. A., Alyasseri, Z. A. A., Awadallah, M. A., and Abu Doush, I. (2021). Coronavirus herd immunity optimizer (CHIO). Neural Computing and Applications, 33, 5011-5042.
• [14] Zhong, C., Li, G., and Meng, Z. (2022). Beluga whale optimization: A novel nature-inspired metaheuristic algorithm. Knowledge-Based Systems, 251, 109215.
• [15] Kahraman, H. T., Aras, S., ve Gedikli, E. (2020). Fitness-distance balance (FDB): A new selection method for meta-heuristic search algorithms. Knowledge-Based Systems, 190, 105169.
• [16] Guvenc, U., Duman, S., Kahraman, H. T., Aras, S., & Katı, M. (2021). Fitness–Distance Balance based adaptive guided differential evolution algorithm for security-constrained optimal power flow problem incorporating renewable energy sources. Applied Soft Computing, 108, 107421.
• [17] Aras, S., Gedikli, E., and Kahraman, H. T. (2021). A novel stochastic fractal search algorithm with fitness-distance balance for global numerical optimization. Swarm and Evolutionary Computation, 61, 100821.
• [18] Duman, S., Kahraman, H. T., Guvenc, U., and Aras, S. (2021). Development of a Lévy flight and FDB-based coyote optimization algorithm for global optimization and real-world ACOPF problems. Soft Computing, 25, 6577-6617.
• [19] Sharifi, M. R., Akbarifard, S., Qaderi, K., and Madadi, M. R. (2021). Developing MSA algorithm by new fitness-distance-balance selection method to optimize cascade hydropower reservoirs operation. Water Resources Management, 35, 385-406.
• [20] Zheng, K., Yuan, X., Xu, Q., Dong, L., Yan, B., and Chen, K. (2022). Hybrid particle swarm optimizer with fitness-distance balance and individual self-exploitation strategies for numerical optimization problems. Information Sciences, 608, 424-452.
• [21] Bakir, H., Guvenc, U., Kahraman, H. T., & Duman, S. (2022). Improved Lévy flight distribution algorithm with FDB-based guiding mechanism for AVR system optimal design. Computers & Industrial Engineering, 168, 108032.
• [22] Duman, S., Kahraman, H. T., Sonmez, Y., Guvenc, U., Kati, M., and Aras, S. (2022). A powerful meta-heuristic search algorithm for solving global optimization and real-world solar photovoltaic parameter estimation problems. Engineering Applications of Artificial Intelligence, 111, 104763.
• [23] Ozkaya, B., Guvenc, U., & Bingol, O. (2022). Fitness Distance Balance based LSHADE algorithm for energy hub economic dispatch problem. IEEE Access, 10, 66770-66796.
• [24] Duman, S., Kahraman, H. T., and Kati, M. (2023). Economical operation of modern power grids incorporating uncertainties of renewable energy sources and load demand using the adaptive fitness-distance balance-based stochastic fractal search algorithm. Engineering Applications of Artificial Intelligence, 117, 105501.
• [25] C. T. Yue, K. V. Price, P. N. Suganthan, J. J. Liang, M. Z. Ali, B. Y. Qu, N. H. Awad, and Partha P Biswas , Problem Definitions and Evaluation Criteria for the CEC 2020 Special Session and Competition on Single Objective Bound Constrained Numerical Optimization, Technical Report 2019 11, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China And Technical Report, Nanyang Technological University, Singapore
• [26] Desuky, A. S., Cifci, M. A., Kausar, S., Hussain, S., and El Bakrawy, L. M. (2022). Mud Ring Algorithm: A new meta-heuristic optimization algorithm for solving mathematical and engineering challenges. IEEE Access, 10, 50448-50466.
• [27] Ezugwu, A. E., Agushaka, J. O., Abualigah, L., Mirjalili, S., and Gandomi, A. H. (2022). Prairie dog optimization algorithm. Neural Computing and Applications, 34(22), 20017-20065.
• [28] Dehghani, M., Montazeri, Z., Trojovská, E., and Trojovský, P. (2023). Coati Optimization Algorithm: A new bio-inspired metaheuristic algorithm for solving optimization problems. Knowledge-Based Systems, 259, 110011.
• [29] GÜRFİDAN, R., & ERSOY, M. (2020). A New Hybrid Encryption Approach for Secure Communication: GenComPass. International Journal of Computer Network & Information Security, 12(4).