Volume 8 , Issue 1 , PP: 39-59, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
El-Sayed M. El-Kenawy 1 , Faris H. Rizk 2 , Ahmed Mohamed Zaki 3 , Mahmoud Elshabrawy Mohamed 4 , Abdelhameed Ibrahim 5 , Abdelaziz A. Abdelhamid 6 , Nima Khodadadi 7 , Ehab M. Almetwally 8 , Marwa M. Eid 9
Doi: https://doi.org/10.54216/JAIM.080104
In this paper, we propose the Ocotillo Optimization Algorithm (OcOA), a novel desert-inspired metaheuristic designed to solve complex optimization problems. Inspired by the adaptive strategies of desert plants, OcOA aims to achieve a balance between exploration and exploitation in high-dimensional and multimodal search spaces. The algorithm dynamically adjusts its behavior based on feedback from prior iterations, optimizing both search breadth and solution refinement. To evaluate its effectiveness, OcOA was tested against several well-known algorithms on a range of benchmark functions, including unimodal and multimodal functions from the CEC 2005 suite such as Sphere, Rosenbrock, Ackley, and Rastrigin. The results demonstrate that OcOA outperforms competing approaches in terms of accuracy, convergence speed, and computational efficiency. Additionally, its adaptability was validated through feature selection tasks, highlighting its robustness in handling both continuous and discrete optimization challenges. This study positions OcOA as a competitive optimization tool for various real-world applications
Ocotillo Optimization Algorithm, metaheuristic, exploration-exploitation balance, complex optimization, adaptive algorithm
[1] B. Abdollahzadeh, F. S. Gharehchopogh, and S. Mirjalili. African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Computers & Industrial Engineering, 158:107408, 2021.
[2] A. Baghdadi, M. Heristchian, and H. Kloft. Design of prefabricated wall-floor building systems using meta-heuristic optimization algorithms. Automation in Construction, 114:103156, 2020.
[3] D. Balderas, A. Ortiz, E. M´endez, P. Ponce, and A. Molina. Empowering digital twin for industry 4.0 using metaheuristic optimization algorithms: Case study pcb drilling optimization. The International Journal of Advanced Manufacturing Technology, 113(5):1295–1306, 2021.
[4] M. M. Fouad, A. I. El-Desouky, R. Al-Hajj, and E.-S. M. El-Kenawy. Dynamic group-based cooperative optimization algorithm. IEEE Access, 8:148378–148403, 2020.
[5] V. Hayyolalam and A. A. Pourhaji Kazem. Black widow optimization algorithm: A novel meta-heuristic approach for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 87:103249, 2020.
[6] E. H. Houssein, M. R. Saad, F. A. Hashim, H. Shaban, and M. Hassaballah. L´evy flight distribution: A new metaheuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 94:103731, 2020.
[7] S. Kaur, Y. Kumar, A. Koul, and S. Kumar Kamboj. A systematic review on metaheuristic optimization techniques for feature selections in disease diagnosis: Open issues and challenges. Archives of Computational Methods in Engineering, 30(3):1863–1895, 2023.
[8] A. Kaveh, M. Khanzadi, and M. Rastegar Moghaddam. Billiards-inspired optimization algorithm; a new meta-heuristic method. Structures, 27:1722–1739, 2020.
[9] M. Nssibi, G. Manita, and O. Korbaa. Advances in nature-inspired metaheuristic optimization for feature selection problem: A comprehensive survey. Computer Science Review, 49:100559, 2023.
[10] J.-S. Pan, L.-G. Zhang, R.-B. Wang, V. Sn´aˇsel, and S.-C. Chu. Gannet optimization algorithm: A new metaheuristic algorithm for solving engineering optimization problems. Mathematics and Computers in Simulation, 202:343–373, 2022.
[11] T. Rahkar Farshi. Battle royale optimization algorithm. Neural Computing and Applications, 33(4):1139– 1157, 2021.
[12] S. Talatahari, M. Azizi, and A. H. Gandomi. Material generation algorithm: A novel metaheuristic algorithm for optimization of engineering problems. Processes, 9(5), 2021.
[13] A. Tzanetos and G. Dounias. Nature inspired optimization algorithms or simply variations of metaheuristics? Artificial Intelligence Review, 54(3):1841–1862, 2021.
[14] J. Wang, M. Khishe, M. Kaveh, and H. Mohammadi. Binary chimp optimization algorithm (bchoa): A new binary meta-heuristic for solving optimization problems. Cognitive Computation, 13(5):1297–1316, 2021.
[15] B. Zerouali, N. Bailek, A. Tariq, A. Kuriqi, M. Guermoui, A. H. Alharbi, D. S. Khafaga, and E.-S. M. El-kenawy. Enhancing deep learning-based slope stability classification using a novel metaheuristic optimization algorithm for feature selection. Scientific Reports, 14(1):21812, 2024.
[16] B. Abdollahzadeh, F. S. Gharehchopogh, N. Khodadadi, and S. Mirjalili. Mountain gazelle optimizer: A new nature-inspired metaheuristic algorithm for global optimization problems. Advances in Engineering Software, 174:103282, 2022.
[17] L. Abualigah. Multi-verse optimizer algorithm: A comprehensive survey of its results, variants, and applications. Neural Computing and Applications, 32(16):12381–12401, 2020.
[18] L. Abualigah, M. Shehab, M. Alshinwan, S. Mirjalili, and M. A. Elaziz. Ant lion optimizer: A comprehensive survey of its variants and applications. Archives of Computational Methods in Engineering, 28(3):1397–1416, 2021.
[19] Q. Li, S.-Y. Liu, and X.-S. Yang. Influence of initialization on the performance of metaheuristic optimizers. Applied Soft Computing, 91:106193, 2020.
[20] H.-L. Minh, T. Sang-To, M. AbdelWahab, and T. Cuong-Le. A new metaheuristic optimization based on k-means clustering algorithm and its application to structural damage identification. Knowledge-Based Systems, 251:109189, 2022.
[21] S. Talatahari, M. Azizi, M. Tolouei, B. Talatahari, and P. Sareh. Crystal structure algorithm (crystal): A metaheuristic optimization method. IEEE Access, 9:71244–71261, 2021.
[22] F. A. Hashim, E. H. Houssein, K. Hussain, M. S. Mabrouk, and W. Al-Atabany. Honey badger algorithm: New metaheuristic algorithm for solving optimization problems. Mathematics and Computers in Simulation, 192:84–110, 2022.
[23] F. A. Hashim, K. Hussain, E. H. Houssein, M. S. Mabrouk, and W. Al-Atabany. Archimedes optimization algorithm: A new metaheuristic algorithm for solving optimization problems. Applied Intelligence, 51(3):1531–1551, 2021.
[24] E. Pira. City councils evolution: A socio-inspired metaheuristic optimization algorithm. Journal of Ambient Intelligence and Humanized Computing, 14(9):12207–12256, 2023.
[25] M. H. Sulaiman and Z. Mustaffa. Optimal power flow incorporating stochastic wind and solar generation by metaheuristic optimizers. Microsystem Technologies, 27(9):3263–3277, 2021.