ISUD (Individuals with Substance Use Disorder): A Novel Metaheuristic Algorithm for Solving Optimization Problems | ||
| Journal of AI and Data Mining | ||
| دوره 13، شماره 2، تیر 2025، صفحه 207-226 اصل مقاله (13.5 M) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2025.15175.2621 | ||
| نویسندگان | ||
| Farzad Zandi1؛ Parvaneh Mansouri1؛ Reza Sheibani* 2 | ||
| 1epartment of Mathematics and Computer Science, Arak Branch, Islamic Azad University, Arak, Iran. | ||
| 2Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran. | ||
| چکیده | ||
| In the field of optimization, metaheuristic algorithms have garnered significant interest. These algorithms, which draw inspiration from natural selection, evolution, and problem-solving strategies, offer an alternative approach to solving complex optimization problems. Unlike conventional software engineering methods, metaheuristics do not rely on derivative calculations in the search space. Instead, they explore solutions by iteratively refining and adapting their search process. The no-free-lunch (NFL) theorem proves that an optimization scheme cannot perform well in dealing with all optimization challenges. Over the last two decades, a plethora of metaheuristic algorithms has emerged, each with its unique characteristics and limitations. In this paper, we propose a novel meta-heuristic algorithm called ISUD (Individuals with Substance Use Disorder) to solving optimization problems by examining the clinical behaviors of individuals compelled to use drugs. We evaluate the effectiveness of ISUD by comparing it with several well-known heuristic algorithms across 44 benchmark functions of varying dimensions. Our results demonstrate that ISUD outperforms these existing methods, providing superior solutions for optimization problems. | ||
| کلیدواژهها | ||
| Optimization؛ Meta-heuristic Algorithms؛ Swarm Intelligence | ||
| مراجع | ||
|
[1] E. G. Talbi, “Metaheuristics: from design to implementation”. John Wiley & Sons, vol. 205, no. 2, pp. 486-487, 2009.
[2] I. Fister, Jr. Fister, X. S. Yang, & J. Brest, “A comprehensive review of firefly algorithms”. Swarm and Evolutionary Computation, vol. 13, pp. 34-46, 2013.
[3] X. S. Yang, and X. He, “Bat algorithm: literature review and applications”. International Journal of Bio-Inspired Computation, vol. 5, no. 3, pp. 141-149, 2013.
[4] M. Abdel-Basset, and L. A. Shawky, “Flower pollination algorithm: a comprehensive review”. Artificial Intelligence Review, vol. 52, pp. 2533-2557, 2019.
[5] A. Lambora, K. Gupta, and K. Chopra, “Genetic algorithm-A literature review”. In 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), pp. 380-384. IEEE. 2019, February.
[6] J. Kennedy, and R. Eberhart, “Particle swarm optimization”. In Proceedings of ICNN'95-International Conference on Neural Networks, vol. 4, pp. 1942-1948), IEEE, 1995, November.
[7] M. Dorigo, M. Birattari, and T. Stutzle, “Ant colony optimization”. IEEE Computational Intelligence Magazine, vol. 1, no. 4, pp. 28-39, 2006.
[8] D. Bertsimas, and J. Tsitsiklis, “Simulated annealing”. Statistical Science, vol. 8, no. 1, pp. 10-15, 1993.
[9] F. S. Gharehchopogh, “Quantum-inspired metaheuristic algorithms: comprehensive survey and classification”. Artificial Intelligence Review, vol. 56, no. 6, pp. 5479-5543, 2023.
[10] T. Bartz‐Beielstein, J. Branke, J. Mehnen, and O. Mersmann, “Evolutionary algorithms”. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 4, no. 3, pp. 178-195, 2014.
[11] K. V. Price, “Differential evolution”. In Handbook of Optimization: From Classical to Modern Approach, pp. 187-214. Springer Berlin Heidelberg, 2013.
[12] S. Chinnasamy, M. Ramachandran, M. Amudha, and K. Ramu, “A review on hill climbing optimization methodology”. Recent Trends in Management and Commerce, vol. 3, no. 1, pp. 1-7, 2022.
[13] F. Glover, and M. Laguna, “Tabu search”. In: Du, DZ., Pardalos, P.M. (eds) Handbook of Combinatorial Optimization. Springer Boston MA, 1998.
[14] M. J. Willis, H. G. Hiden, P. Marenbach, B. McKay, and G. A. Montague, “Genetic programming: An introduction and survey of applications”. In Second International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, pp. 314-319. IET, 1997, September.
[15] H. R. Lourenço, O. C. Martin, and T. Stützle, “Iterated local search”. In Handbook of Metaheuristics, pp. 320-353. Boston, MA: Springer US, 2003.
[16] D. Jungnickel, and D. Jungnickel, “The greedy algorithm”. Graphs, Networks and Algorithms, vol. 3, pp. 129-153, 1999.
[17] M. Mahmood, and B. Al-Khateeb, “The blue monkey: A new nature inspired metaheuristic optimization algorithm”. Periodicals of Engineering and Natural Sciences, vol. 7, no. 3, pp. 1054-1066, 2019.
[18] M. Dehghani, P. Trojovský, and O. P. Malik, “Green anaconda optimization: a new bio-inspired metaheuristic algorithm for solving optimization problems”. Biomimetics, vol. 8, no. 1, pp. 121, 2023.
[19] A. Askarzadeh, “A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm”. Computers & Structures, vol. 169, pp. 1-12, 2016.
[20] G. I. Sayed, A. Darwish, and A. E. Hassanien, “Quantum multiverse optimization algorithm for optimization problems”. Neural Computing and Applications, vol. 31, pp. 2763-2780, 2019.
[21] 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, pp. 103249, 2020.
[22] S. Mirjalili, “Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm”. Knowledge-Based Systems, vol. 89, pp. 228-249, 2015.
[23] R. V. Rao, V. J. Savsani, and D. P. Vakharia, “Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems”. Computer-Aided Design, vol. 43, no. 3, pp. 303-315, 2011.
[24] S. Mirjalili, “SCA: a sine cosine algorithm for solving optimization problems”. Knowledge-Based Systems, vol. 96, pp. 120-133, 2016.
[25] S. J. Mousavirad, and H. Ebrahimpour-Komleh, “Human mental search: a new population-based metaheuristic optimization algorithm”. Applied Intelligence, vol. 47, pp. 850-887, 2017.
[26] B. Javidy, A. Hatamlou, and S. Mirjalili, “Ions motion algorithm for solving optimization problems”. Applied Soft Computing, vol. 32, pp. 72-79, 2015.
[27] A. Hadian, M. Bagherian, and B. Fathi Vajargah. “A Heuristic Algorithm for Multi-layer Network Optimization in Cloud Computing”. Journal of AI and Data Mining, vol. 9, no. 3, pp. 361-367, 2021.
[28] M. Yazdani, and F. Jolai, “Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm”. Journal of Computational Design and Engineering, vol. 3, no. 1, pp. 24-36, 2016. | ||
|
آمار تعداد مشاهده مقاله: 433 تعداد دریافت فایل اصل مقاله: 701 |
||