Comparison of Black Widow Optimization and Aquila Optimizer with Current Metaheuristics

Year 2024, Volume: 8 Issue: 1, 17 - 25, 31.07.2024

Metin Kalyon , Sibel Arslan

Abstract

Metaheuristic optimization algorithms are an optimization approach that produces acceptable solutions in situations where it is difficult to create a mathematical model in an optimization problem or in large-scale, multivariate optimization problems. Metaheuristics play a significant role in solving optimization problems. In this study, five current meta- heuristics (Aquila Optimizer (AO), Artificial Rabbits Optimization (ARO), Black Widow Optimization (BWO), Harris Hawk Optimization (HHO) and Sooty Tern Optimization Algorithm (STOA), which are inspired by swarm intelligence and foraging behavior of creatures in nature) are compared. These algorithms are discussed in detail and information is given about their working principles. As far as is known, this is the first time that the performances of these five algorithms have been compared. The algorithms were evaluated with unimodal and multimodal test functions. The simulation results demonstrate that AO and BWO are more successful than the other algorithms. It is also evaluated that the metaheuristics used in the study can be applied to many engineering problems.

Keywords

Metaheuristics, Aquila Optimization, Artificial Rabbit Optimization, Black Widow Optimization, Harris Hawk Optimization, Sooty Tern Optimization Algorithm

References

• [1] G. G. Emel and Ç. Taşkın, “Genetik algoritmalar ve uygulama alanları,” Uludağ Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, vol. 21, no. 1, pp. 129–152, 2002.
• [2] M. Mitchell and S. Forrest, “Genetic algorithms and artificial life,” Artificial life, vol. 1, no. 3, pp. 267–289, 1994.
• [3] Z. Beheshti and S. M. H. Shamsuddin, “A review of population-based meta-heuristic algorithms,” Int. j. adv. soft comput. appl, vol. 5, no. 1, pp. 1–35, 2013
• [4] ˙I. Öznur and S. Korukoğlu, “Genetik algoritma yaklaşımı ve yöneylem ¨ arastırmasında bir uygulama,” Yonetim ve Ekonomi Dergisi ¨, vol. 10, no. 2, pp. 191–208, 2003.
• [5] M. A. Albadr, S. Tiun, M. Ayob, and F. Al-Dhief, “Genetic algorithm based on natural selection theory for optimization problems,” Symmetry, vol. 12, no. 11, p. 1758, 2020
• [6] M. Sharma and P. Kaur, “A comprehensive analysis of nature-inspired meta-heuristic techniques for feature selection problem,” Archives of Computational Methods in Engineering, vol. 28, pp. 1103–1127, 2021.
• [7] E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi, “Gsa: a gravitational search algorithm,” Information sciences, vol. 179, no. 13, pp. 2232– 2248, 2009.
• [8] D. Datta, A. R. Amaral, and J. R. Figueira, “Single row facility layout problem using a permutation-based genetic algorithm,” European Journal of Operational Research, vol. 213, no. 2, pp. 388–394, 2011.
• [9] S. S. Chouhan, A. Kaul, and U. P. Singh, “Soft computing approaches for image segmentation: a survey,” Multimedia Tools and Applications, vol. 77, no. 21, pp. 28 483–28 537, 2018
• [10] E. Eyup and E. Tanyıldızı, “Güncel metasezgisel optimizasyon algoritmalarının performans karşılaştırılması,” in 2018 International Conference on Artificial Intelligence and Data Processing (IDAP). IEEE, 2018, pp. 1–16.
• [11] L. Abualigah, D. Yousri, M. Abd Elaziz, A. A. Ewees, M. A. AlQaness, and A. H. Gandomi, “Aquila optimizer: a novel meta-heuristic optimization algorithm,” Computers & Industrial Engineering, vol. 157, p. 107250, 2021.
• [12] O. E. Turgut and M. S. Turgut, “Local search enhanced aquila optimization algorithm ameliorated with an ensemble of wavelet mutation strategies for complex optimization problems,” Mathematics and Computers in Simulation, vol. 206, pp. 302–374, 2023
• [13] S. Akyol, “Global optimizasyon için yeni bir hibrit yöntem: kaya kartalı optimizasyonu-tanjant arama algoritması,” Fırat Universitesi Muhendislik Bilimleri Dergisi , vol. 33, no. 2, pp. 721–733, 2021.
• [14] Y.-J. Zhang, Y.-X. Yan, J. Zhao, and Z.-M. Gao, “Aoaao: The hybrid algorithm of arithmetic optimization algorithm with aquila optimizer,” IEEE Access, vol. 10, pp. 10 907–10 933, 2022.
• [15] L. Wang, Q. Cao, Z. Zhang, S. Mirjalili, and W. Zhao, “Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems,” Engineering Applications of Artificial Intelligence, vol. 114, p. 105082, 2022.
• [16] H. Bakır, “Dynamic fitness-distance balance-based artificial rabbits optimization algorithm to solve optimal power flow problem,” Expert Systems with Applications, vol. 240, p. 122460, 2024.
• [17] Y. Wang, Y. Xiao, Y. Guo, and J. Li, “Dynamic chaotic opposition-based learning-driven hybrid aquila optimizer and artificial rabbits optimization algorithm: framework and applications,” Processes, vol. 10, no. 12, p.2703, 2022.
• [18] B. Gülmez, “Stock price prediction with optimized deep lstm network ¨with artificial rabbits optimization algorithm,” Expert Systems with Applications, vol. 227, p. 120346, 2023.
• [19] V. Hayyolalam and A. A. P. Kazem, “Black widow optimization algorithm: a novel meta-heuristic approach for solving engineering optimization problems,” Engineering Applications of Artificial Intelligence, vol. 87, p. 103249, 2020.
• [20] G. Hu, B. Du, X. Wang, and G. Wei, “An enhanced black widow optimization algorithm for feature selection,” Knowledge-Based Systems, vol. 235, p. 107638, 2022.
• [21] 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.
• [22] O. Altay, “Guncel metasezgisel yöntemlerin standart kalite testi fonksiyonlarında karşılaştırılması,” International Journal of Pure and Applied Sciences, vol. 8, no. 2, pp. 286–301, 2022.
• [23] J. C. Bednarz, “Cooperative hunting harris’ hawks (parabuteo unicinctus),” Science, vol. 239, no. 4847, pp. 1525–1527, 1988.
• [24] S. Wang, H. Jia, L. Abualigah, Q. Liu, and R. Zheng, “An improved hybrid aquila optimizer and harris hawks algorithm for solving industrial engineering optimization problems,” Processes, vol. 9, no. 9, p. 1551, 2021.
• [25] Y. Ç. Kuyu, “Optimizasyon problemleri için yeni metasezgisel yaklaşımlar,” Ph.D. dissertation, Bursa Uludag University (Turkey), 2023.
• [26] G. Dhiman and A. Kaur, “Stoa: a bio-inspired based optimization algorithm for industrial engineering problems,” Engineering Applications of Artificial Intelligence, vol. 82, pp. 148–174, 2019.
• [27] H. Jia, Y. Li, K. Sun, N. Cao, and H. M. Zhou, “Hybrid sooty tern optimization and differential evolution for feature selection.” Computer Systems Science & Engineering, vol. 39, no. 3, 2021.
• [28] A. Singh, A. Sharma, S. Rajput, A. K. Mondal, A. Bose, and M. Ram, “Parameter extraction of solar module using the sooty tern optimization algorithm,” Electronics, vol. 11, no. 4, p. 564, 2022.