The International Arab Journal of Information Technology (IAJIT)

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Narwhal Optimizer: A Novel Nature-Inspired Metaheuristic Algorithm

Recently, metaheuristic algorithms have become a very interesting research field due to their ability to address complex and diverse problems. This paper presents a novel metaheuristic called Narwhal Optimizer (NO) inspired by narwhals behaviors. The NO algorithm mimics the hunting mechanism of narwhals. The narwhals are marine mammals known for their sophisticated communication based on clicks sound to locate their prey. The algorithm is based on three main steps: signal emission, signal propagation, and position updating of the narwhals. The hunting process, which is based on signal emission and propagation, is formulated as an optimization algorithm. The strategies observed in narwhal pods are emulated to enhance exploration and exploitation in the search space. The NO algorithm is benchmarked on 13 well-known functions, including unimodal, multimodal, and fixed-dimension multimodal functions. The experimental results showed that NO provides satisfactory and reasonable solutions in terms of avoiding local minima and achieving global optimality.

[1] Abdel-Basset M., Mohamed R., Jameel M., and Abouhawwash M., “Nutcracker Optimizer: A Novel Nature-Inspired Metaheuristic Algorithm for Global Optimization and Engineering Design Problems,” Knowledge-Based Systems, vol. 262, pp. 110248, 2023. https://doi.org/10.1016/j.knosys.2022.110248 [2] Ali A., Yaseen M., Aljanabi M., and Abed S., “Transfer Learning: A New Promising Techniques,” Mesopotamian Journal of Big Data, vol. 2023, 2023. DOI:10.58496/MJBD/2023/004 [3] Braik M., Hammouri A., Atwan J., Al-Betar M., and Awadallah M., “White Shark Optimizer: A Novel Bio-Inspired Meta-Heuristic Algorithm for Global Optimization Problems,” Knowledge- Based Systems, vol. 243, pp. 108457, 2022. https://doi.org/10.1016/j.knosys.2022.108457 [4] Bosire A. and Maingi D., “Using Deep Analysis of Driver Behavior for Vehicle Theft Detection and Recovery,” in Proceedings of International Arab Conference on Information Technology, Oman, pp. 1-6, 2021. DOI:10.1109/acit53391.2021.9677433 [5] Bonabeau E., Dorigo M., and Theraulaz G., Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press, 1999. https://doi.org/10.1093/oso/9780195131581.001. 0001 [6] Digalakis J. and Margaritis K., “On Benchmarking Functions for Genetic Algorithms,” International Journal of Computer Mathematics, vol. 77, no. 4, pp. 481-506, 2021. https://doi.org/10.1080/00207160108805080 [7] Dorigo M., Birattari M., and Stutzle T., “Ant Colony Optimization,” IEEE Computational Intelligence Magazine, vol. 1, no. 4, pp. 28-39, 2006. DOI: 10.1109/MCI.2006.329691 [8] El-kenawy E., Khodadadi N., Mirjalili S., Abdelhamid A., Eid M., and Ibrahim A., “Grey 426 The International Arab Journal of Information Technology, Vol. 21, No. 3, May 2024 Lag Goose Optimization: Nature-Inspired Optimization Algorithm,” Expert Systems with Applications, vol. 238, pp. 122147, 2024. https://doi.org/10.1016/j.eswa.2023.122147 [9] Eslami N., Yazdani S., Mirzaei M., and Hadavandi E., “Aphid Ant Mutualism: A Novel Nature- Inspired Metaheuristic Algorithm for Solving Optimization Problems,” Mathematics and Computers in Simulation, vol. 201, pp. 362-395, 2022. https://doi.org/10.1016/j.matcom.2022.05.015 [10] Han M., Du Z., Yuen F., Zhu H., Li Y., and Yuan Q., “Walrus Optimizer: A Novel Nature-Inspired Metaheuristic Algorithm,” Expert Systems with Applications, vol. 239, pp. 2023. https://doi.org/10.1016/j.eswa.2023.122413 [11] Gandomi A., Yang X., and Alavi H., “Cuckoo Search Algorithm: A Metaheuristic Approach to Solve Structural Optimization Problems,” Engineering with Computers, vol. 29, pp. 17-35, 2013. [12] Kennedy J. and Eberhart R., “Particle Swarm Optimization,” in Proceedings of ICNN'95- International Conference on Neural Networks, Perth, pp. 1942-1948, 1995. DOI: 10.1109/ICNN.1995.488968 [13] Kirkpatrick S., Gelatt C., and Vecchi M., “Optimization by Simulated Annealing,” Science, vol. 220, no. 4598, pp. 671-680, 1983. DOI: 10.1126/science.220.4598.67 [14] Liu H., Cai Z., and Wang Y., “Hybridizing Particle Swarm Optimization with Differential Evolution for Constrained Numerical and Engineering Optimization,” Applied Soft Computing, vol. 10, no. 2, pp. 629-640, 2012. https://doi.org/10.1016/j.asoc.2009.08.031 [15] Maxwell J., A Treatise on Electricity and Magnetism, Oxford: Clarendon, 1892. [16] Mahdavi M., Fesanghary M., and Damangir E., “An Improved Harmony Search Algorithm for Solving Optimization Problems,” Applied Mathematics and Computation, vol. 188, no. 2, pp.1567-1579, 2007. https://doi.org/10.1016/j.amc.2006.11.033 [17] Maree M., Eleyat M., and Mesqali E., “Optimizing Machine Learning-based Sentiment Analysis Accuracy in Bilingual Sentences via Preprocessing Techniques,” The International Arab Journal of Information Technology, vol. 21, no. 2, pp. 257-270, 2024. https://doi.org/10.34028/iajit/21/2/8 [18] Mirjalili S. and Lewis A., “S-Shaped Versus V- Shaped Transfer Functions for Binary Particle Swarm Optimization,” Swarm and Evolutionary Computation, vol. 9, pp. 1-14, 2013. https://doi.org/10.1016/j.swevo.2012.09.002 [19] Mirjalili S., Mirjalili S., and Lewis A., “Grey Wolf Optimizer,” Advances in Engineering Software, vol. 69, pp. 46-61, 2014. https://doi.org/10.1016/j.advengsoft.2013.12.007 [20] Mirjalili S., Gandomi A., Mirjalili S., Saremi S., Faris H., and Mirjalili S., “Salp Swarm Algorithm: A Bio-Inspired Optimizer for Engineering Design Problems,” Advances in Engineering Software, vol. 114, pp. 163-191, 2017. https://doi.org/10.1016/j.advengsoft.2017.07.002 [21] Rashedi E., Nezamabadi H., and Saryazdi S., “GSA: A Gravitational Search Algorithm,” Information Sciences, vol. 179, no. 13, pp. 2232- 2248, 2009. [22] Sadollah A., Bahreininejad A., Eskandar H., and Hamdi M., “Mine Blast Algorithm: A New Population-Based Algorithm for Solving Constrained Engineering Optimization Problems,” Applied Soft Computing, vol. 13, no. 5, pp. 2592-2612, 2013. https://doi.org/10.1016/j.ins.2009.03.004 [23] Storn R. and Price K., “Differential Evolution a Simple and Efficient Heuristic for global Optimization over Continuous Spaces,” Journal of Global Optimization, vol. 11, no. 4, pp. 341- 359, 1997. DOI:10.1023/A:1008202821328 [24] Zhong C., Li C., and Meng Z., “Beluga Whale Optimization: A Novel Nature-Inspired Metaheuristic Algorithm,” Knowledge Based Systems, vol. 251, pp. 109215, 2022. https://doi.org/10.1016/j.knosys.2022.109215 Seyyid Medjahed received a Doctor of Informatics degree from University of Science and Technology Mohamed-Boudiaf Oran, Algeria in 2017. He is currently an associate professor at Computer Science Department in Reliane University, Algeria since 2012. His research includes meta-heuristics, global optimization, machine learning, data mining, bioinformatics. Fatima Boukhatem holds a Doctor degree from University of Djilali Liabes, Sidi Belabes, Algeria. Her research areas of interest include physical, Bioinformatics, Artificial Intelligent.