پیوندهای مفید
آمار
| تعداد نشریات | 8 |
| تعداد شماره‌ها | 414 |
| تعداد مقالات | 5,478 |
| تعداد مشاهده مقاله | 6,010,619 |
| تعداد دریافت فایل اصل مقاله | 5,299,932 |
Rank-based Adaptive Brooding in Mimetic Coral Reefs Search | ||
| AUT Journal of Modeling and Simulation | ||
| دوره 56، شماره 2، 2024، صفحه 171-184 اصل مقاله (830.34 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/miscj.2024.23438.5376 | ||
| نویسندگان | ||
| Seyed Amirhossein Farjadi؛ Mehdi Alimohammadi؛ Mohammad-R. Akbarzadeh-T.* | ||
| Department of Computer Engineering, Center of Excellence on Soft Computing and Intelligent Information Processing, Ferdowsi University of Mashhad, Mashhad, Iran | ||
| چکیده | ||
| Mimetic Coral Reefs Optimization (MCRO) has proven highly effective for feature selection due to its capacity to explore diverse solution spaces, enhancing model accuracy and robustness. However, integrating MCRO with local search techniques remains challenging, as it tends to be computationally intensive and prone to premature convergence. To address these issues, this paper introduces a Rank-based Adaptive Brooding (RAB) mechanism, designed to refine the local mimetic search strategy within MCRO. RAB adaptively adjusts the brooding operator based on the ranks of coral larvae, minimizing disruption to high-rank larvae and harnessing the exploratory potential of lower-rank larvae. This approach promotes a more balanced exploration-exploitation trade-off, leading to faster convergence and enhanced performance in complex problem spaces. The proposed method's efficacy is tested across eight UCI datasets using KNN, Decision Tree, and SVM classifiers, and the results are evaluated by precision, recall, and F1 score. Empirical results reveal that RAB outperforms existing adaptive strategies with fixed brooding, delivering superior feature selection performance, particularly in high-dimensional datasets. Additionally, the optimization capabilities of RAB were examined using 39 CEC benchmark functions, revealing consistent improvements in feature selection accuracy while demonstrating variable outcomes in broader optimization tasks. Notably, RAB showed significant enhancements in eight benchmark cases, highlighting its potential for broader applicability in optimization scenarios. | ||
| کلیدواژه‌ها | ||
| Feature Selection؛ Coral Reef Optimization؛ Rank-Based Brooding؛ Adaptation؛ Mimetic Search Strategy | ||
| مراجع | ||
|
[1] J. Cai, J. Luo, S. Wang, and S. Yang, "Feature selection in machine learning: A new perspective," Neurocomputing, vol. 300, pp. 70-79, 2018.
[2] D. Theng and K. K. Bhoyar, "Feature selection techniques for machine learning: a survey of more than two decades of research," Knowledge and Information Systems, vol. 66, no. 3, pp. 1575-1637, 2024.
[3] R. I. Lung and M.-A. Suciu, "An Evolutionary Approach to Feature Selection and Classification," in International Conference on Machine Learning, Optimization, and Data Science, 2023: Springer, pp. 333-347.
[4] M. Z. Ali, A. Abdullah, A. M. Zaki, F. H. Rizk, M. M. Eid, and E. M. El-Kenway, "Advances and challenges in feature selection methods: a comprehensive review," J Artif Intell Metaheuristics, vol. 7, no. 1, pp. 67-77, 2024.
[5] R.-C. Chen, C. Dewi, S.-W. Huang, and R. E. Caraka, "Selecting critical features for data classification based on machine learning methods," Journal of Big Data, vol. 7, no. 1, p. 52, 2020.
[6] A. A. Farag, Z. M. Ali, A. M. Zaki, F. H. Rizk, M. M. Eid, and E.-S. M. EL-Kenawy, "Exploring Optimization Algorithms: A Review of Methods and Applications," Full Length Article, vol. 7, no. 2, pp. 08-8-17, 2024.
[7] R. Kamala and R. J. Thangaiah, "An improved hybrid feature selection method for huge dimensional datasets," IAES International Journal of Artificial Intelligence, vol. 8, no. 1, p. 77, 2019.
[8] P. Drotár, M. Gazda, and L. Vokorokos, "Ensemble feature selection using election methods and ranker clustering," Information Sciences, vol. 480, pp. 365-380, 2019.
[9] L. Pereira et al., "A binary cuckoo search and its application for feature selection," Cuckoo Search and Firefly Algorithm: Theory and Applications, pp. 141-154, 2014.
[10] N. H. Shikoun, A. S. Al-Eraqi, and I. S. Fathi, "BinCOA: An Efficient Binary Crayfish Optimization Algorithm for Feature Selection," IEEE Access, vol. 12, pp. 28621-28635, 2024.
[11] R. C. T. De Souza, L. dos Santos Coelho, C. A. De Macedo, and J. Pierezan, "A V-shaped binary crow search algorithm for feature selection," in 2018 IEEE Congress on Evolutionary computation (CEC), 2018: IEEE, pp. 1-8.
[12] J. Osei-Kwakye, F. Han, A. A. Amponsah, Q.-H. Ling, and T. A. Abeo, "A diversity enhanced hybrid particle swarm optimization and crow search algorithm for feature selection," Applied Intelligence, vol. 53, no. 17, pp. 20535-20560, 2023.
[13] K. K. Ghosh, S. Ahmed, P. K. Singh, Z. W. Geem, and R. Sarkar, "Improved binary sailfish optimizer based on adaptive β-hill climbing for feature selection," IEEE access, vol. 8, pp. 83548-83560, 2020.
[14] S. Ahmed, K. K. Ghosh, L. Garcia-Hernandez, A. Abraham, and R. Sarkar, "Improved coral reefs optimization with adaptive β-hill climbing for feature selection," Neural Computing and Applications, vol. 33, no. 12, pp. 6467-6486, 2021.
[15] R. Xie, S. Li, and F. Wu, "An Improved Northern Goshawk Optimization Algorithm for Feature Selection," Journal of Bionic Engineering, pp. 1-39, 2024.
[16] S. Mirjalili, "Evolutionary algorithms and neural networks," Studies in computational intelligence, vol. 780, pp. 43-53, 2019.
[17] J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proceedings of ICNN'95-international conference on neural networks, 1995, vol. 4: ieee, pp. 1942-1948.
[18] R. Storn and K. Price, "Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces," Journal of global optimization, vol. 11, pp. 341-359, 1997.
[19] C. C. Ribeiro, P. Hansen, V. Maniezzo, and A. Carbonaro, "Ant colony optimization: an overview," Essays and surveys in metaheuristics, pp. 469-492, 2002.
[20] F. C. García López, M. García Torres, J. A. Moreno Pérez, and J. M. Moreno Vega, "Scatter search for the feature selection problem," in Conference on Technology Transfer, 2003: Springer, pp. 517-525.
[21] D. Karaboga and B. Basturk, "A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm," Journal of global optimization, vol. 39, pp. 459-471, 2007.
[22] M. Neshat, G. Sepidnam, and M. Sargolzaei, "Swallow swarm optimization algorithm: a new method to optimization," Neural Computing and Applications, vol. 23, no. 2, pp. 429-454, 2013.
[23] S. Mirjalili, "Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems," Neural computing and applications, vol. 27, pp. 1053-1073, 2016.
[24] 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, vol. 51, pp. 1531-1551, 2021.
[25] F. Bérchez-Moreno, A. M. Durán-Rosal, C. Hervás Martínez, P. A. Gutiérrez, and J. C. Fernández, "A memetic dynamic coral reef optimisation algorithm for simultaneous training, design, and optimisation of artificial neural networks," Scientific Reports, vol. 14, no. 1, p. 6961, 2024.
[26] S. Salcedo-Sanz, J. Del Ser, I. Landa-Torres, S. Gil-López, and J. Portilla-Figueras, "The coral reefs optimization algorithm: a novel metaheuristic for efficiently solving optimization problems," The Scientific World Journal, vol. 2014, no. 1, p. 739768, 2014.
[27] S. Salcedo-Sanz, "A review on the coral reefs optimization algorithm: new development lines and current applications," Progress in Artificial Intelligence, vol. 6, pp. 1-15, 2017.
[28] A. M. Durán-Rosal, P. A. Gutiérrez, S. Salcedo-Sanz, and C. Hervás-Martínez, "An empirical validation of a new memetic CRO algorithm for the approximation of time series," in Conference of the Spanish Association for Artificial Intelligence, 2018: Springer, pp. 209-218.
[29] S. A. Farjadi and M.-R. Akbarzadeh-T, "Rank-Based Adaptive Brooding in a Mimetic Coral Reefs Search for Feature Selection," in 2023 31st International Conference on Electrical Engineering (ICEE), 2023: IEEE, pp. 177-182.
[30] M. Mafarja, A. Qasem, A. A. Heidari, I. Aljarah, H. Faris, and S. Mirjalili, "Efficient hybrid nature-inspired binary optimizers for feature selection," Cognitive Computation, vol. 12, no. 1, pp. 150-175, 2020.
[31] M. Srinivas and L. M. Patnaik, "Adaptive probabilities of crossover and mutation in genetic algorithms," IEEE Transactions on Systems, Man, and Cybernetics, vol. 24, no. 4, pp. 656-667, 1994.
[32] I. A. Korejo, Z. Khuhro, F. Jokhio, N. Channa, and H. Nizamani, "An adaptive crossover operator for genetic algorithms to solve the optimization problems," Sindh University Research Journal-SURJ (Science Series), vol. 45, no. 2, 2013.
[33] L. M. Abouelmagd, M. Y. Shams, N. E. El-Attar, and A. E. Hassanien, "Feature selection based coral reefs optimization for breast cancer classification," in Medical Informatics and Bioimaging Using Artificial Intelligence: Challenges, Issues, Innovations and Recent Developments: Springer, 2021, pp. 53-72.
[34] C. Yan, J. Ma, H. Luo, and A. Patel, "Hybrid binary coral reefs optimization algorithm with simulated annealing for feature selection in high-dimensional biomedical datasets," Chemometrics and Intelligent Laboratory Systems, vol. 184, pp. 102-111, 2019.
[35] B. Rajakumar and A. George, "A new adaptive mutation technique for genetic algorithm," in 2012 IEEE International Conference on Computational Intelligence and Computing Research, 2012: IEEE, pp. 1-7.
[36] D. Thierens, "Adaptive mutation rate control schemes in genetic algorithms," in Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No. 02TH8600), 2002, vol. 1: IEEE, pp. 980-985.
[37] N. S. Altman, "An introduction to kernel and nearest-neighbor nonparametric regression," The American Statistician, vol. 46, no. 3, pp. 175-185, 1992.
[38] E. Emary, H. M. Zawbaa, and A. E. Hassanien, "Binary grey wolf optimization approaches for feature selection," Neurocomputing, vol. 172, pp. 371-381, 2016.
[39] M. M. Mafarja and S. Mirjalili, "Hybrid whale optimization algorithm with simulated annealing for feature selection," Neurocomputing, vol. 260, pp. 302-312, 2017.
[40] S. Ahmed, K. K. Ghosh, L. Garcia-Hernandez, A. Abraham, and R. Sarkar, "Improved coral reefs optimization with adaptive β-hill climbing for feature selection," Neural Computing and Applications, vol. 33, no. 12, pp. 6467-6486, 2021. | ||
|
آمار تعداد مشاهده مقاله: 309 تعداد دریافت فایل اصل مقاله: 151 |
||