
تعداد نشریات | 26 |
تعداد شمارهها | 447 |
تعداد مقالات | 4,566 |
تعداد مشاهده مقاله | 5,413,506 |
تعداد دریافت فایل اصل مقاله | 3,604,782 |
Expected binary particle swarm optimization | ||
Journal of Mahani Mathematical Research | ||
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 18 مرداد 1404 اصل مقاله (701.04 K) | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22103/jmmr.2025.24703.1752 | ||
نویسندگان | ||
Mohammad Bagher Dowlatshahi* 1؛ Saba Beiranvand2 | ||
1Department of Computer Engineering, Lorestan University, Khorramabad, Iran | ||
2Department of Computer Engineering, Technical and Vocational University (TVU), Tehran, Iran | ||
چکیده | ||
Binary Particle Swarm Optimization (BPSO), proposed by Kennedy and Eberhart, extends PSO to binary search spaces. However, BPSO suffers from computational complexity due to velocity-based position updates and limited scalability in high-dimensional problems. In this paper, we introduce Expected BPSO (EBPSO), a simplified and faster variant that removes the velocity component and directly uses a probabilistic position update mechanism inspired by expected particle behavior. We theoretically analyze EBPSO’s convergence and evaluate its performance across two domains: (1) ten scalable binary benchmark functions (F1–F10) and (2) feature selection for classification using four real-world datasets (Breast Cancer, Iris, Wine, and Digits). EBPSO consistently outperforms BPSO, Binary GA, and other recent binary metaheuristics (e.g., BDO, BSCA, BGWO, BRKO) in both solution quality and runtime. For example, EBPSO achieved up to 15× speedup over BPSO and maintained a competitive advantage across all tested dimensions. In the feature selection task, EBPSO was used within a wrapper model using an SVM classifier. It reached accuracies of 99.07\% on Digits, 99.44\% on Wine, and 98.42\% on Breast Cancer datasets while selecting fewer features than other methods. Statistical significance was confirmed using paired t-tests and Wilcoxon signed-rank tests, both yielding p-values < 0.01 across all evaluations. Overall, EBPSO demonstrates superior performance, scalability, and statistical robustness, making it a promising tool for large-scale binary optimization and efficient feature selection. | ||
کلیدواژهها | ||
Binary Particle Swarm Optimization؛ Binary optimization problems؛ Expected Behavior؛ Convergence Analysis | ||
مراجع | ||
[1] Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN'95 - International Conference on Neural Networks (pp. 1942{1948).
[2] Kennedy, J. (2010). Particle swarm optimization. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of Machine Learning (pp. 760{766). Boston, MA: Springer US. https://doi.org/10.1007/978-0-387-30164-8-630
[3] Masdari, M., Salehi, F., Jalali, M., & Bidaki, M. (2017). A survey of PSO-based scheduling algorithms in cloud computing. Journal of Network and Systems Management, 25(1), 122{158.
[4] Jain, M., Saihjpal, V., Singh, N., & Singh, S. B. (2022). An overview of variants and advancements of PSO algorithm. Applied Sciences, 12(17), 8392. https://doi.org/10.3390/app12178392
[5] Wang, D., Tan, D., & Liu, L. (2018). Particle swarm optimization algorithm: an overview. Soft Computing, 22(2), 387{408.
[6] Lalwani, S., Sharma, H., Satapathy, S. C., Deep, K., & Bansal, J. C. (2019). A survey on parallel particle swarm optimization algorithms. Arabian Journal for Science and Engineering, 44, 2899{2923.
[7] Pan, H., & Gong, J. (2023). Application of particle swarm optimization (PSO) algorithm in determining thermodynamics of solid combustibles. Energies, 16(14), 5302.
[8] Fang, J., Liu, W., Chen, L., Lauria, S., Miron, A., & Liu, X. (2023). A survey of algorithms, applications and trends for particle swarm optimization.
[9] Suriyan, K., & Nagarajan, R. (2024). Particle swarm optimization in biomedical technologies: innovations, challenges, and opportunities. Emerging Technologies for Health Literacy and Medical Practice, 220{238.
[10] Abualigah, L., et al. (2024). Particle swarm optimization algorithm: review and applications. Metaheuristic Optimization Algorithms, 1{14.
[11] Duong, T. T. N., Bui, D.-N., & Phung, M. D. (2025). Navigation variable-based multiobjective particle swarm optimization for UAV path planning with kinematic constraints. Neural Computing and Applications, 37(7), 5683{5697.
[12] Dowlatshahi, M. B., Derhami, V., & Nezamabadi-Pour, H. (2020). Fuzzy particle swarm optimization with nearest-better neighborhood for multimodal optimization. Iranian Journal of Fuzzy Systems, 17(4), 7{24.
[13] Kennedy, J., & Eberhart, R. C. (1997). A discrete binary version of the particle swarm algorithm. In Proceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics (pp. 4104{4108).
[14] Crepinsek, M., Liu, S.-H., & Mernik, M. (2013). Exploration and exploitation in evolutionary algorithms: A survey. ACM Computing Surveys, 45(3), 1{33. https://doi.org/10.1145/2480741.2480751
[15] Engelbrecht, A. P. (2006). Fundamentals of computational swarm intelligence. John Wiley & Sons, Inc.
[16] Shi, Y., & Eberhart, R. (1998). A modi ed particle swarm optimizer. In 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No. 98TH8360) (pp. 69{73).
[17] Parsopoulos, K. E., & Vrahatis, M. N. (2002). Recent approaches to global optimization problems through particle swarm optimization. Natural Computing, 1, 235{306.
[18] Shi, Y., & Eberhart, R. C. (1999). Empirical study of particle swarm optimization. In Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406) (pp. 1945{1950).
[19] Brits, R., Engelbrecht, A. P., & van den Bergh, F. (2007). Locating multiple optima using particle swarm optimization. Applied Mathematics and Computation, 189(2), 1859{1883.
[20] Liu, J., Mei, Y., & Li, X. (2015). An analysis of the inertia weight parameter for binary particle swarm optimization. IEEE Transactions on Evolutionary Computation, 20(5), 666{681.
[21] Harik, G. R., Lobo, F. G., & Goldberg, D. E. (1999). The compact genetic algorithm. IEEE Transactions on Evolutionary Computation, 3(4), 287{297. [22] Wang, Y., et al. (2025). Research on Grid-Connected Speed Control of Hydraulic Wind Turbine Based on Enhanced Chaotic Particle Swarm Optimization Fuzzy PID. Algorithms, 18(4), 187. [23] Xu, W., et al. (2025). Improvement of Citrus Yield Prediction Using UAV Multispectral Images and the CPSO Algorithm. Agronomy, 15(1), 171. [24] Wang, S., Liu, W., Chen, L., & Zong, S. (2025). In uence maximization based on discrete particle swarm optimization on multilayer network. Information Systems, 127, 102466. [25] Salini, Y., Pokkuluri, K. S., Deepa, D., & Joseph, M. (2025). Machine Learning-Based Swarm Optimization for Residential Demand-Based Electricity. In Sustainable Smart Homes Built with Internet of Things (pp. 149{165). [26] Raul, S. K., Rout, R. R., & Somayajulu, D. (2024). Deep Learning and Dynamic BPSO for Road Accident Severity Prediction using Twitter Data. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1{7). [27] Fouad, Y., Abdelaziz, N. E., & Elshewey, A. M. (2024). IoT Trac Parameter Classi - cation based on Optimized BPSO for Enabling Green Wireless Networks. Engineering Technology & Applied Science Research, 14(6), 18929{18934. [28] Verma, A., Dhanda, N., & Yadav, V. (2025). Enhanced Edge Detection through Binary Particle Swarm Optimization and L0 Guided Filtering. EAI Endorsed Transactions on Scalable Information Systems, 12(1). [29] Redjimi, K., Nebti, S., & Redjimi, M. (2024). BPSO-SEP: a routing protocol based on binary particle swarm optimization. Studies in Engineering and Exact Sciences, 5(3), e12922. [30] Lan, T., et al. (2025). A robust method of dual adaptive prediction for ship fuel consumption based on polymorphic particle swarm algorithm driven. Applied Energy, 379, 124911. [31] Alkhammash, E. H., et al. (2023). Application of Machine Learning to Predict COVID-19 Spread via an Optimized BPSO Model. Biomimetics, 8(6), 457. [32] Lin, S., Wang, J., Huang, B., Kong, X., & Yang, H. (2025). Bio particle swarm optimization and reinforcement learning algorithm for path planning of automated guided vehicles in dynamic industrial environments. Scienti c Reports, 15(1), 463. [33] Osama, M. (2024). Power Eciency Enhancement in 5G HetNets Using Adaptive BPSO and Classi cation Trees. In 2024 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) (pp. 193{198). [34] Khaseeb, J. Y., Keshk, A., & Youssef, A. (2025). Improved Binary GreyWolf Optimization Approaches for Feature Selection Optimization. Applied Sciences, 15(2), 489. [35] Kessentini, S. (2024). Analysis and improvement of the binary particle swarm optimization. Annals of Operations Research, 1{31. [36] Liu, X., Liu, M., & Yin, H. (2024). Application of QPSO-BPSO in fault self-healing of distributed power distribution networks. Energy Informatics, 7(1), 53. [37] Abbes, W., et al. (2023). An Enhanced Binary Particle Swarm Optimization (E-BPSO) algorithm for service placement in hybrid cloud platforms. Neural Computing and Applications, 35(2), 1343{1361. [38] Lino, M., Montez, C., Le~ao, E., Rabelo, R., Fayran, A., & Vasques, F. (2024). A lightweight BPSO mechanism for topology recon guration in data-driven IIoT plants. Internet of Things, 26, 101208. [39] Glymour, C., Madigan, D., Pregibon, D., & Smyth, P. (1997). Statistical themes and lessons for data mining. Data Mining and Knowledge Discovery, 1, 11{28. [40] Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2010). BGSA: binary gravitational search algorithm. Natural Computing, 9, 727{745. [41] Nezamabadi-Pour, H. (2015). A quantum-inspired gravitational search algorithm for binary encoded optimization problems. Engineering Applications of Arti cial Intelligence, 40, 62{75. [42] Li, M., & Kou, J. (2008). Crowding with nearest neighbors replacement for multiple species niching and building blocks preservation in binary multimodal functions optimization. Journal of Heuristics, 14, 243{270. [43] Seif, Z., & Ahmadi, M. B. (2015). Opposition versus randomness in binary spaces. Applied Soft Computing, 27, 28{37. [44] Hennessy, J. L., & Patterson, D. A. (2012). Computer Architecture: A Quantitative Approach (5th ed.). Waltham, MA: Morgan Kaufmann. | ||
آمار تعداد مشاهده مقاله: 3 تعداد دریافت فایل اصل مقاله: 2 |