| تعداد نشریات | 31 |
| تعداد شمارهها | 851 |
| تعداد مقالات | 8,206 |
| تعداد مشاهده مقاله | 16,269,401 |
| تعداد دریافت فایل اصل مقاله | 10,760,123 |
A Competitive Framework for Structured Particle Swarm Optimization using Fuzzy Logic | ||
| Iranian Journal of Fuzzy Systems | ||
| دوره 23، شماره 4، مهر و آبان 2026، صفحه 145-170 اصل مقاله (2.27 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22111/ijfs.2026.54604.9673 | ||
| نویسندگان | ||
| Shipra Vatsa؛ Neha Singhal* ؛ Alka Tripathi | ||
| Department of Mathematics, Jaypee Institute of Information Technology Noida, India | ||
| چکیده | ||
| Particle Swarm Optimization (PSO) is one of the most popular metaheuristic algorithm used to solve complex real-world optimization problems to date. The major drawback of PSO is its weak exploration ability, which leads the algorithm prematurely to a local optimum. To address this issue, we propose an improved particle swarm optimization with an intelligent multi-swarm strategy (PSO-IMS), inspired by powerful PSO variants FHPSO, HPSO-ALS, and ALPSO. To ensure uniform dispersion of particles during initialization, low-discrepancy Sobol sequence has been used in this article. PSO-IMS uses the worst performing particle in the swarm, called gworst to enhance the algorithm’s exploration ability in the initial stages of the search. The method integrates a fuzzy-logic based parameter adaptation mechanism to manage uncertainty and dynamically adjust key control parameters, thereby reflecting a more realistic hierarchical decision process. To validate the performance of PSO-IMS, it has been tested on seventeen benchmark functions along with CEC 2013 test suite, and compared against four powerful PSO variants, in which our proposed algorithm showed satisfactory results. As an application of the proposed algorithm, it has been applied to the traveling salesman problem with thirty-one cities as well as an Unmanned Aerial Vehicles (UAV) path planning problem. | ||
| کلیدواژهها | ||
| Particle Swarm Optimization؛ Fuzzy Logic؛ Hierarchy Strategy؛ Global Worst Particle | ||
| مراجع | ||
|
[1] D. Bertsimas, J. Tsitsiklis, Simulated annealing, Statistical Science, 8(1) (1993), 10-15. https://doi. org/10.1214/ss/1177011077 [2] H. Chen, T. Jia, J. Guo, L. Yang, A multi-strategy enhanced black-winged kite algorithm for UAV path planning, The Journal of Supercomputing, 81(15) (2025), 1-32. https://doi.org/10.1007/ s11227-025-07905-4 [3] G. Chen, H. Xinbo, J. Jia, Z. Min, Natural exponential inertia weight strategy in particle swarm optimization, 6th World Congress on Intelligent Control and Automation, 1 (2006), 3672-3675. https: //doi.org/10.1109/WCICA.2006.1713055 [4] R. C. Eberhart, Y. Shi, Tracking and optimizing dynamic systems with particle swarms, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No. 01TH8546), 1 (2001), 94-100. https: //doi.org/10.1109/CEC.2001.934376 [5] M. Eftekhari, A. Mehrpooya, F. Saberi-Movahed, V. Torra, How fuzzy concepts contribute to machine learning, Springer, Cham, Switzerland, (2022). https://doi.org/10.1007/978-3-030-94066-9 [6] R. Etesami, M. Madadi, F. Keynia, Adaptive fuzzy swarm-based search algorithm (AFSSA) for complex engineering optimization, Iranian Journal of Fuzzy Systems, 22(6) (2025), 125-145. https://doi.org/ 10.22111/ijfs.2025.52217.9209 [7] Y. Feng, G. Teng, A. Wang, Y. Yao, Chaotic inertia weight in particle swarm optimization, Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007), (2007), 475- 475. https://doi.org/10.1109/ICICIC.2007.209 [8] A. Ghadiri, M. Pazoki, S. Erfani, Hybrid GA-PSO-optimized neural network for biogas production: Comparative evaluation of metaheuristic algorithms, Renewable Energy, 262 (2026), 125432. https: //doi.org/10.1016/j.renene.2026.125432 [9] A. Ghodousian, S. Zal, A two-phase-ACO algorithm for solving nonlinear optimization problems subjected to fuzzy relational equations, Iranian Journal of Fuzzy Systems, 21(5) (2024), 151-174. https://doi. org/10.22111/ijfs.2024.49652.8760 [10] E. Guo, Y. Gao, C. Hu, A two-stage evolutionary algorithm based on hybrid penalty strategy and its application to multi-UAV path planning, Expert Systems with Applications, 298(C) (2026), 129698. https://doi.org/10.1016/j.eswa.2025.129698 [11] J. H. Holland, Genetic algorithms, Scientific American, 267(1) (1992), 66-73. https://www.jstor.org/ stable/24939139 [12] H. Jabeen, Z. Jalil, A. R. Baig, Opposition based initialization in particle swarm optimization (OPSO), Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers, (2009), 2047-2052. https://doi.org/10.1145/1570256.1570274 [13] J. Jin, X. Pang, B. Wang, D. Wang, Z. Zheng, Optimal scheduling method of carbon-green certificate trading virtual power plant via Q-learning-enhanced particle swarm algorithm, Complex and Intelligent Systems, 12(1) (2026), 51. https://doi.org/10.1007/s40747-025-02176-1 [14] D. S. Johnson, C. H. Papadimitriou, M. Yannakakis, How easy is local search?, Journal of Computer and System Sciences, 37(1) (1988), 79-100. https://doi.org/10.1016/0022-0000(88)90046-3 [15] A. Karkadakattil, AI and metaheuristic optimization in additive manufacturing of lightweight alloys: A critical review, Journal of The Institution of Engineers (India): Series C, 107 (2026), 1063-1085. https://doi.org/10.1007/s40032-026-01355-4 [16] J. Kennedy, Small worlds and mega-minds: Effects of neighborhood topology on particle swarm performance, Proceedings of the 1999 Congress on Evolutionary Computation-CEC’99 (Cat. No. 99TH8406), 3 (1999), 1931-1938. https://doi.org/10.1109/CEC.1999.785509 [17] J. Kennedy, R. Eberhart, Particle swarm optimization, Proceedings of ICNN’95-International Conference on Neural Networks, 4 (1995), 1942-1948. https://doi.org/10.1109/ICNN.1995.488968 [18] J. Kennedy, R. Mendes, Population structure and particle swarm performance, Proceedings of the 2002 Congress on Evolutionary Computation, CEC’02 (Cat. No. 02TH8600), 2 (2002), 1671-1676. https: //doi.org/10.1109/CEC.2002.1004493 [19] P. A. Kowalski, S. Kucharczyk, J. Ma´ndziuk, Constrained hybrid metaheuristic algorithm for probabilistic neural networks learning, Information Sciences, 713 (2025), 122185. https://doi.org/10.1016/j. ins.2025.122185 [20] X. Li, Z. Yang, M. Li, W. Hong, Integrated scheduling of cargo vessels, research vessels, and marine experiments in multifunctional ports using Q-learning enhanced PSO, Swarm and Evolutionary Computation, 102 (2026), 102315. https://doi.org/10.1016/j.swevo.2026.102315 [21] J. J. Liang, A. K. Qin, P. N. Suganthan, S. Baskar, Comprehensive learning particle swarm optimizer for global optimization of multimodal functions, IEEE Transactions on Evolutionary Computation, 10(3) (2006), 281-295. https://doi.org/10.1109/TEVC.2005.857610 [22] B. Liang, Y. Zhao, Y. Li, A hybrid particle swarm optimization with crisscross learning strategy, Engineering Applications of Artificial Intelligence, 105 (2021), 104418. https://doi.org/10.1016/j. engappai.2021.104418 [23] P. Melin, F. Olivas, O. Castillo, F. Valdez, J. Soria, M. Valdez, Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic, Expert Systems with Applications, 40(8) (2013), 3196-3206. https://doi.org/10.1016/j.eswa.2012.12.033 [24] S. Mirjalili, S. M. Mirjalili, A. Lewis, Grey wolf optimizer, Advances in Engineering Software, 69 (2014), 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007 [25] M. Nasir, S. Das, D. Maity, S. Sengupta, U. Halder, P. N. Suganthan, A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization, Information Sciences, 209 (2012), 16- 36. https://doi.org/10.1016/j.ins.2012.04.028 [26] A. Ozlek, B. Ervural, B. Cayir Ervural, A hybrid IRN-based BWM–COPRAS framework for electrooptic system selection in UAVs with heterogeneous evaluations, Iranian Journal of Fuzzy Systems, 23(2) (2026), 157-175. https://doi.org/10.22111/ijfs.2026.52261.9218 [27] D. Pal, H. K. Sharma, O. Prentkovskis, F. Chakraborty, L. Maskeli¯unait˙e, A study of the multi-objective neighboring only quadratic minimum spanning tree problem in the context of uncertainty, Applied Sciences, 14(19) (2024), 8941. https://doi.org/10.3390/app14198941 [28] D. Pal, H. K. Sharma, O. Prentkovskis, F. Chakraborty, L. Maskeli¯unait˙e, Multi-objective windy postman problem in a fuzzy transportation network, Promet-Traffic and Transportation, 37(4) (2025), 853-873. https://doi.org/10.7307/ptt.v37i4.1134 [29] P. Y. Pamungkas, N. M. E. Normasari, A. A. Fanani, Modified gorilla troops optimizer with elite opposition-based learning and tangent flight operator to solve traveling salesman problem, Journal of Intelligent and Fuzzy Systems, 50(3) (2026), 771-787. https://doi.org/10.1177/18758967251360039 [30] P. Patro, K. Kumar, G. S. Kumar, A. K. Sahu, Intelligent data classification using optimized fuzzy neural network and improved cuckoo search optimization, Iranian Journal of Fuzzy Systems, 20(6) (2023), 155- 169. https://doi.org/10.22111/ijfs.2023.44767.7887 [31] A. Raj, P. Punia, P. Kumar, An innovative fuzzy gravitational search algorithm (FGSA) with an adaptive swap mechanism for solving travelling salesman problem (TSP), International Journal of Information Technology, (2025), 1-17. https://doi.org/10.1007/s41870-025-02848-8 [32] K. Rajwar, K. Deep, S. Das, An exhaustive review of the metaheuristic algorithms for search and optimization: Taxonomy, applications, and open challenges, Artificial Intelligence Review, 56(11) (2023), 13187-13257. https://doi.org/10.1007/s10462-023-10470-y [33] A. Ratnaweera, S. K. Halgamuge, H. C. Watson, Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients, IEEE Transactions on Evolutionary Computation, 8(3) (2004), 240-255. https://doi.org/10.1109/TEVC.2004.826071 [34] R. Sharma, J. S. Matharu, K. S. Parmar, A survey on particle swarm optimization: Evolution, adaptations and practical implementations, Applied Soft Computing, 186 (2025), 114016. https://doi.org/ 10.1016/j.asoc.2025.114016 [35] Y. Shi, R. C. Eberhart, A modified particle swarm optimizer, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No. 98TH8360), (1998), 69-73. https://doi.org/10.1109/ICEC.1998.699146 [36] Y. Shi, R. C. Eberhart, Empirical study of particle swarm optimization, Proceedings of the 1999 Congress on Evolutionary Computation-CEC’99 (Cat. No. 99TH8406), 3 (1999), 1945-1950. https://doi.org/ 10.1109/CEC.1999.785511 [37] H. Sun, J. Cao, X. Liang, C. Lan, Q. Zheng, X. Su, H. Li, X. Ding, Return path planning for UAVs in mountainous power transmission line inspection based on an improved grey wolf optimization algorithm, 2025 International Conference of Clean Energy and Electrical Engineering (ICCEEE), (2025), 1-6. https: //doi.org/10.1109/ICCEEE63357.2025.11156523 [38] Z. Tian, Path planning for mobile robots based on enhanced particle swarm optimization algorithm, Journal of Electrical Engineering and Technology, (2026), 1-14. https://doi.org/10.1007/ s42835-026-02747-3 [39] N. Q. Uy, N. X. Hoai, R. I. McKay, P. M. Tuan, Initialising PSO with randomised low-discrepancy sequences: The comparative results, 2007 IEEE Congress on Evolutionary Computation, (2007), 1985- 1992. https://doi.org/10.1109/CEC.2007.4424717 [40] L. Wang, D. Tian, X. Gou, Z. Shi, Hybrid particle swarm optimization with adaptive learning strategy, Soft Computing, 28(17) (2024), 9759-9784. https://doi.org/10.1007/s00500-024-09814-9 [41] Y.Wang, Z.Wang, G.Wang, Hierarchical learning particle swarm optimization using fuzzy logic, Expert Systems with Applications, 232 (2023), 120759. https://doi.org/10.1016/j.eswa.2023.120759 [42] F. Wang, H. Zhang, K. Li, Z. Lin, J. Yang, X. Shen, A hybrid particle swarm optimization algorithm using adaptive learning strategy, Information Sciences, 436-437 (2018), 162-177. https://doi.org/10. 1016/j.ins.2018.01.027 [43] W. Ye, W. Feng, S. Fan, A novel multi-swarm particle swarm optimization with dynamic learning strategy, Applied Soft Computing, 61 (2017), 832-843. https://doi.org/10.1016/j.asoc.2017.08.051 [44] L. A. Zadeh, Fuzzy sets, Information and Control, 8(3) (1965), 338-353. https://doi.org/10.1016/ S0019-9958(65)90241-X [45] Z. Zhan, J. Zhang, et. al., Adaptive particle swarm optimization, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 39(6) (2008), 1362-1381. https://doi.org/10.1109/TSMCB. 2009.2015956 [46] B. Zhang, H. Duan, Three-dimensional path planning for uninhabited combat aerial vehicle based on predator-prey pigeon-inspired optimization in dynamic environment, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 14(1) (2015), 97-107. https://doi.org/10.1109/TCBB.2015. 2443789 | ||
|
آمار تعداد مشاهده مقاله: 23 تعداد دریافت فایل اصل مقاله: 22 |
||