ZAMANLA DEĞİŞEN SİGMOİD TRANSFER FONKSİYONU VE ÇAPRAZLAMA STRATEJİSİ İLE GELİŞTİRİLMİŞ İKİLİ BAL PORSUĞU ALGORİTMASI
Abstract
Bal porsuklarının yiyecek arama davranışını modelleyen Bal Porsuğu Algoritması (HBA), yakın zamanda önerilen bir meta-sezgisel algoritmadır. Bu çalışmada, sürekli optimizasyon problemlerinin çözümü için önerilen bu algoritmanın ikili versiyonu geliştirildi. Sürekli algoritmayı ikili bir algoritmaya dönüştürmek için S-şekilli transfer fonksiyonu ve çaprazlama stratejisi kullanıldı. Sabit ve zamanla değişen özelliklere sahip sekiz adet S-şekilli transfer fonksiyonu kullanıldı ve en başarılı fonksiyon belirlendi. Ayrıca zamanla değişen transfer fonksiyonlarının etkisi de incelendi. Çaprazlama stratejisi olarak tek nokta, iki nokta ve tekdüze olmak üzere üç strateji uygulandı ve diğerlerinden daha başarılı olan tekdüze stratejisi algoritmaya entegre edildi. Bu şekilde geliştirilen ikili algoritma (BinHBA), on beşi küçük ölçekli ve on ikisi büyük ölçekli olmak üzere toplam yirmi yedi sırt çantası problemi üzerinde test edildi. Sonuçları analiz etmek ve mevcut literatürde bulunan yöntemlerle karşılaştırmak için istatistiksel testler kullanıldı. Sonuçlar, ikili optimizasyon problemleri için önerilen BinHBA'nın etkili ve tercih edilebilir olduğunu gösterdi.
Keywords
İkili optimization Çaprazlama stratejisi Bal porsuğu algoritması Sırt çantası problemleri Zamanla-değişen transfer fonksiyonu
References
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- Yasear, S. A., and Ghanimi, H. M. (2022). A modified honey badger algorithm for solving optimal power flow optimization problem. International Journal of Intelligent Engineering and Systems, 15(4), 142-155.
- Yildizdan, G., and Bas, E. (2024). A new binary coati optimization algorithm for binary optimization problems. Neural Computing and Applications, 36(6), 2797-2834.
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BINARY HONEY BADGER ALGORITHM ENHANCED WITH TIME-VARYING SIGMOID TRANSFER FUNCTION AND CROSSOVER STRATEGY
Abstract
Modeling the foraging behavior of honey badgers, the Honey Badger Algorithm (HBA) is a recently proposed metaheuristic algorithm. In this study, a binary version of this algorithm that was proposed for solving continuous optimization problems was developed. The S-shaped transfer function and crossover strategy were used to transform the continuous algorithm into a binary algorithm. Eight S-shaped transfer functions with constant and time-varying features were used, and the most successful function was determined. Additionally, the effect of time-varying transfer functions was examined. Three strategies, single-point, two-point, and uniform, were applied as crossover strategies, and the uniform strategy, which was more successful than others, was integrated into the algorithm. The binary algorithm (BinHBA) developed in this way was tested on a total of twenty-seven knapsack problems, fifteen small-scale and twelve large-scale. Statistical tests were employed to analyze the results and compare them with methods found in the existing literature. The results showed that the proposed BinHBA for binary optimization problems is effective and preferable.
Keywords
Binary optimization Crossover strategy Honey badger algorithm Knapsack problems Time-varying transfer function
References
- Abasi, A. K., Aloqaily, M., and Guizani, M. (2023). Optimization of CNN using modified Honey Badger Algorithm for Sleep Apnea detection. Expert Systems with Applications, 229, 120484. doi: https://doi.org/10.1016/j.eswa.2023.120484
- Abdel-Basset, M., Mohamed, R., and Mirjalili, S. (2021). A Binary Equilibrium Optimization Algorithm for 0–1 Knapsack Problems. Computers & Industrial Engineering, 151, 106946. doi: https://doi.org/10.1016/j.cie.2020.106946
- Abdollahzadeh, B., Barshandeh, S., Javadi, H., and Epicoco, N. (2022). An enhanced binary slime mould algorithm for solving the 0–1 knapsack problem. Engineering with Computers, 1-22.
- Al-Betar, M. A., Hammouri, A. I., Awadallah, M. A., and Abu Doush, I. (2021). Binary β-hill climbing optimizer with S-shape transfer function for feature selection. Journal of Ambient Intelligence and Humanized Computing, 12(7), 7637-7665.
- Aljebreen, M., Alabduallah, B., Mahgoub, H., Allafi, R., Hamza, M. A., Ibrahim, S. S., . . . Alsaid, M. I. (2023). Integrating IoT and honey badger algorithm based ensemble learning for accurate vehicle detection and classification. Ain Shams Engineering Journal, 14(11), 102547. doi: https://doi.org/10.1016/j.asej.2023.102547
- Altuwairiqi, M. (2024). An optimized multi-hop routing protocol for wireless sensor network using improved honey badger optimization algorithm for efficient and secure QoS. Computer Communications, 214, 244-259. doi: https://doi.org/10.1016/j.comcom.2023.08.011
- Awadallah, M. A., Hammouri, A. I., Al-Betar, M. A., Braik, M. S., and Elaziz, M. A. (2022). Binary Horse herd optimization algorithm with crossover operators for feature selection. Computers in Biology and Medicine, 141, 105152. doi: https://doi.org/10.1016/j.compbiomed.2021.105152
- Baş, E. (2023). Binary aquila optimizer for 0–1 knapsack problems. Engineering Applications of Artificial Intelligence, 118, 105592. doi: https://doi.org/10.1016/j.engappai.2022.105592
- Büyüköz, G. O., and Haklı, H. (2023). Binary Honey Badger Algorithm for 0-1 Knapsack Problem. Journal of Intelligent Systems: Theory and Applications, 6(2), 108-118. doi: https://doi.org/10.38016/jista.1200225
- Feda, A. K., Adegboye, M., Adegboye, O. R., Agyekum, E. B., Mbasso, W. F., and Kamel, S. (2024). S-shaped grey wolf optimizer-based FOX algorithm for feature selection. Heliyon, 10(2).
- García, S., Molina, D., Lozano, M., and Herrera, F. (2009). A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 special session on real parameter optimization. Journal of Heuristics, 15, 617-644.
- Hashim, F. A., Houssein, E. H., Hussain, K., Mabrouk, M. S., and Al-Atabany, W. (2022). Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems. Mathematics and Computers in Simulation, 192, 84-110. doi: https://doi.org/10.1016/j.matcom.2021.08.013
- He, Y., Zhang, F., Mirjalili, S., and Zhang, T. (2022). Novel binary differential evolution algorithm based on Taper-shaped transfer functions for binary optimization problems. Swarm and evolutionary computation, 69, 101022. doi: https://doi.org/10.1016/j.swevo.2021.101022
- Hu, G., Zhong, J., and Wei, G. (2023). SaCHBA_PDN: Modified honey badger algorithm with multi-strategy for UAV path planning. Expert Systems with Applications, 223, 119941. doi: https://doi.org/10.1016/j.eswa.2023.119941
- Huang, P., Zhou, Y., Deng, W., Zhao, H., Luo, Q., and Wei, Y. (2024). Orthogonal opposition-based learning honey badger algorithm with differential evolution for global optimization and engineering design problems. Alexandria Engineering Journal, 91, 348-367. doi: https://doi.org/10.1016/j.aej.2024.02.024
- Jain, D. K., Ding, W., and Kotecha, K. (2023). Training fuzzy deep neural network with honey badger algorithm for intrusion detection in cloud environment. International Journal of Machine Learning and Cybernetics, 14(6), 2221-2237. doi:10.1007/s13042-022-01758-6
- Jin, C., Li, S., Zhang, L., and Zhang, D. (2023). The Improvement of the Honey Badger Algorithm and Its Application in the Location Problem of Logistics Centers. Applied Sciences, 13(11), 6805. doi: https://doi.org/10.3390/app13116805
- Kapner, D. J., Cook, T. S., Adelberger, E. G., Gundlach, J. H., Heckel, B. R., Hoyle, C., and Swanson, H. E. (2007). Tests of the gravitational inverse-square law below the dark-energy length scale. Physical review letters, 98(2), 021101. doi: https://doi.org/10.1103/PhysRevLett.98.021101
- Karakoyun, M., and Ozkis, A. (2022). A binary tree seed algorithm with selection-based local search mechanism for huge-sized optimization problems. Applied Soft Computing, 129, 109590. doi: https://doi.org/10.1016/j.asoc.2022.109590
- Li, X., Fang, W., and Zhu, S. (2023). An improved binary quantum-behaved particle swarm optimization algorithm for knapsack problems. Information Sciences, 648, 119529. doi: https://doi.org/10.1016/j.ins.2023.119529
- Mafarja, M., Aljarah, I., Heidari, A. A., Faris, H., Fournier-Viger, P., Li, X., and Mirjalili, S. (2018). Binary dragonfly optimization for feature selection using time-varying transfer functions. Knowledge-Based Systems, 161, 185-204. doi: https://doi.org/10.1016/j.knosys.2018.08.003
- Majumdar, P., Mitra, S., and Bhattacharya, D. (2023). Honey Badger algorithm using lens opposition based learning and local search algorithm. Evolving Systems, 1-26. doi: https://doi.org/10.1007/s12530-023-09495-z
- Ni, B., Wang, Y., Huang, J., and Li, G. (2022). Hybrid Enhanced Binary Honey Badger Algorithm with Quadratic Programming for Cardinality Constrained Portfolio Optimization. International Journal of Foundations of Computer Science, 33(06n07), 787-803. doi: https://doi.org/10.1142/S0129054122420151
- Pisinger, D. (2005). Where are the hard knapsack problems? Computers & Operations Research, 32(9), 2271-2284. doi: https://doi.org/10.1016/j.cor.2004.03.002
- Qasem, S. N. (2024). A novel honey badger algorithm with multilayer perceptron for predicting COVID-19 time series data. The Journal of Supercomputing, 80(3), 3943-3969. doi: https://doi.org/10.1007/s11227-023-05560-1
- Wang, B., Kang, H., Sun, G., and Li, J. (2024). Efficient traffic-based IoT device identification using a feature selection approach with Lévy flight-based sine chaotic sub-swarm binary honey badger algorithm. Applied Soft Computing, 155, 111455. doi: https://doi.org/10.1016/j.asoc.2024.111455
- Woolson, R. F. (2007). Wilcoxon signed‐rank test. Wiley encyclopedia of clinical trials, 1-3.
- Yasear, S. A., and Ghanimi, H. M. (2022). A modified honey badger algorithm for solving optimal power flow optimization problem. International Journal of Intelligent Engineering and Systems, 15(4), 142-155.
- Yildizdan, G., and Bas, E. (2024). A new binary coati optimization algorithm for binary optimization problems. Neural Computing and Applications, 36(6), 2797-2834.
- Yildizdan, G., and Baş, E. (2023). A novel binary artificial jellyfish search algorithm for solving 0–1 knapsack problems. Neural Processing Letters, 55(7), 8605-8671.
Details
| Primary Language | English |
|---|---|
| Subjects | Computer Software |
| Journal Section | Research Articles |
| Authors | |
| Early Pub Date | April 16, 2025 |
| Publication Date | April 24, 2025 |
| Submission Date | May 2, 2024 |
| Acceptance Date | January 23, 2025 |
| Published in Issue | Year 2025 Volume: 33 Issue: 1 |
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