Алгоритмы роевого интеллекта для решения задач оптимизации в системах телекоммуникаций

Актуальность. В современном мире телекоммуникации играют критически важную роль в обеспечении цифровой экономики. Сложность и масштаб современных телекоммуникационных сетей, характеризующихся высокой динамичностью, гетерогенностью и постоянным ростом трафика, обуславливают необходимость разработки и применения эффективных методов оптимизации. Традиционные аналитические методы часто оказываются неспособными справиться с комбинаторной сложностью и нелинейностью задач, возникающих в данной области, что делает актуальным поиск альтернативных подходов. В этом контексте алгоритмы роевого интеллекта представляют собой перспективный класс методов, основанных на коллективном поведении биологических организмов и способных эффективно решать сложные задачи оптимизации.

Целью настоящей работы является систематизация и анализ современных исследований, посвященных применению алгоритмов роевого интеллекта в телекоммуникационных сетях. Особое внимание уделено таким методам, как алгоритм пчелиной колонии, алгоритм муравьиной колонии и алгоритм стаи серых волков, а также их модификациям. Основной задачей исследования является выявление ключевых тенденций и направлений развития эвристических алгоритмов с целью повышения производительности, надежности и устойчивости телекоммуникационных систем в условиях роста трафика и усложнения сетевых архитектур.

Научная новизна заключается в проведении систематического обзора современных публикаций, посвященных практическому применению алгоритмов роевого интеллекта в сфере телекоммуникаций. Представлена таксономия рассматриваемых методов, а также проанализированы их основные принципы функционирования и эффективность при решении специфических задач оптимизации в данной предметной области. Особый акцент сделан на адаптации и гибридизации алгоритмов для повышения их производительности в реальных сетевых сценариях.

Теоретическая значимость исследования состоит в обобщении существующего опыта применения биоинспирированных методов оптимизации в телекоммуникациях, что открывает возможности для дальнейшей разработки более эффективных и масштабируемых подходов к управлению сложными динамическими системами. Полученные результаты способствуют углублению понимания потенциала алгоритмов роевого интеллекта в решении задач маршрутизации, распределения ресурсов, планирования сетей и других проблем, характерных для современной цифровой экономики.

1. Ateya A.A., El-Latif A.A.A., Muthanna A., Volkov A., Koucheryavy A. Enabling Metaverse and Telepresence Services in 6G Networks. NY: River Publishers, 2025. 254 p. DOI:10.1201/9788770046749

2. Zangana H.M., Sallow Z.B., Alkawaz M.H., Omar M. Unveiling the Collective Wisdom: A Review of Swarm Intelligence in Problem Solving and Optimization // Inform: Jurnal Ilmiah Bidang Teknologi Informasi Dan Komunikasi. 2024. Vol. 9. Iss. 2. PP. 101–110. DOI:10.25139/inform.v9i2.7934. EDN:WJAKIJ

3. Mao S., Hu F., Lang J., Chen T., Cheng S. Comparative Study of Impacts of Typical Bio-Inspired Optimization Algorithms on Source Inversion Performance // Frontiers in Environmental Science. 2022. Vol. 10. P. 894255. DOI:10.3389/fenvs.2022.894255

4. Duan H., Li P. Bio-inspired computation in unmanned aerial vehicles. Berlin, Heidelberg: Springer, 2014. DOI:10.1007/978-3-642-41196-0

5. Hao Z., Huang H., Cai R. Bio-inspired Algorithms for TSP and Generalized TSP // Greco F. (ed.) Traveling Salesman Problem. IntechOpen, 2008. DOI:10.5772/5583

6. Ateya A.A., Muthanna A., Vybornova A., Algarni A.D., Abuarqoub A., Koucheryavy Y., et al. Chaotic salp swarm algo-rithm for SDN multi-controller networks // Engineering Science and Technology, an International Journal. 2019. Vol. 22. Iss. 4. PP. 1001–1012. DOI:10.1016/j.jestch.2018.12.015. EDN:DOSQQF

7. Alanis A.Y., Arana-Daniel N., López-Franco C. Bio-inspired Algorithms // Bio-inspired Algorithms for Engineering. Elsevier, 2018. PP. 1–14. DOI:10.1016/B978-0-12-813788-8.00001-9

8. Subramanian S., Bhojaneet N., Madhnani H., Pant S., Kumar A., Kotecha K. A Comprehensive Review of Nature-Inspired Optimization Techniques and Their Varied Applications // Nature-Inspired Optimization Algorithms for Cyber-Physical Systems. IGI Global Scientific Publishing, 2025. PP. 105–174. DOI:10.4018/979-8-3693-6834-3.ch005

9. Li P., Duan H. Bio-inspired Computation Algorithms // Bio-inspired Computation in Unmanned Aerial Vehicles. Berlin, Heidelberg: Springer, 2014. PP. 35–69. DOI:10.1007/978-3-642-41196-0_2

10. Almufti M.S., Marqas R.B., Saeed V.A. Taxonomy of bio-inspired optimization algorithms // Journal of Advanced Computer Science & Technology. 2019. Vol. 8. Iss. 2. PP. 23–31. DOI:10.14419/jacst.v8i2.29402

11. Zhang Z., Xu T., Zou K., Tan S., Sun Z. Multi-Objective Grey Wolf Optimizer Based on Improved Head Wolf Selection Strategy // Proceedings of the 43rd Chinese Control Conference (CCC, Kunming, China, 28‒31 July 2024). IEEE, 2024. PP. 1922–1927. DOI:10.23919/CCC63176.2024.10662658

12. Peng Q., Zhan R., Wu H., Shi M. Comparative Study of Wolf Pack Algorithm and Artificial Bee Colony Algorithm: Performance Analysis and Optimization Exploration // International Journal of Swarm Intelligence Research. 2024. Vol. 15. Iss. 1. PP. 1–24. DOI:10.4018/IJSIR.352061

13. Yang J., Gu W. A multi-stage time-backtracking grey wolf optimizer introducing a new hierarchy mechanism // Research Square. 2024. DOI:10.21203/rs.3.rs-4126903/v1

14. Zhao S. Research on the Application of Swarm Behavior to Artificial Intelligence Systems // Applied and Computational Engineering. 2025. Vol. 120. PP. 158–163. DOI:10.54254/2755-2721/2025.19403. EDN:OGCKKC

15. Tyagi N., Bhargava D., Ahlawat A. Implementation of Particle Swarm Optimization Algorithm Inspired by the Social Behaviour of Birds // Proceedings of the 4th International Conference on Technological Advancements in Computational Sciences (ICTACS, Tashkent, Uzbekistan, 13‒15 November 2024). IEEE, 2024. PP. 750–754. DOI:10.1109/ICTACS62700.2024.10840529

16. Cai T., Zhang S., Ye Z., Zhou W., Wang M., He Q., Chen Z., et al. Cooperative metaheuristic algorithm for global optimization and engineering problems inspired by heterosis theory // Scientific Reports. 2024. Vol. 14. Iss. 1. P. 28876. DOI:10.1038/s41598-024-78761-0. EDN:QOGXNY

17. Wu Y., Zhu X., Zhao W., Xia X. A Novel Particle Swarm Optimization Algorithm for Meta-Heuristic Analysis Mechanism Based on Population Learning Strategies and Adaptive Selection of Leadership Particles // Proceedings of the 11th International Conference on Data Science and Advanced Analytics (DSAA, San Diego, USA, 06‒10 October 2024). IEEE, 2024. PP. 1–9. DOI:10.1109/DSAA61799.2024.10722812

18. Yazıcı A.M., Ömür G.A., Celik D.A. Applications and Future Perspectives of Swarm Intelligence in Unmanned and Autonomous Systems: Innovative Conceptual Approaches to Social Sciences // Sosyal Mucit Academic Review. 2024. Vol. 5. Iss. Innovative Conceptual Approaches to Social Sciences. PP. 106–130. DOI:10.54733/smar.1555925. EDN:QUVHXT

19. Pachajoa G.M.M., Achicanoy W., Garzón Ramos D. Automating the Evaluation of the Scalability, Flexibility, and Robustness of Collective Behaviors for Robot Swarms // Proceedings of the Brazilian Symposium on Robotics (SBR) and 2024 Workshop on Robotics in Education (WRE, Goiania, Brazil, 13‒15 November 2024). Piscataway: IEEE, 2024. PP. 144–149. DOI:10.1109/SBR/WRE63066.2024.10837963

20. Paköz B. Swarm Intelligence and Decentralized AI // Human Computer Interaction. 2024. Vol. 8. Iss. 1. PP. 97–100. DOI:10.62802/k7xhrd47. EDN:GLVTOB

21. Yogi M.K., Chakravarthy A.S.N. Application of Variants of Nature-Inspired Optimization for Privacy Preservation in Cyber-Physical Systems // Nature-Inspired Optimization Algorithms for Cyber-Physical Systems. IGI Global Scientific Publishing, 2025. DOI:10.4018/979-8-3693-6834-3.ch009

22. Cheng H., Zhou H., Shen Y. An improved grey wolf optimization algorithm based on bounded subpopulation research strategy // Journal of Physics: Conference Series. 2024. Vol. 2902. P. 012035. DOI:10.1088/1742-6596/2902/1/012035. EDN:ONSHBZ

23. Zhang J., Dai Y., Shi Q. An improved grey wolf optimization algorithm based on scale-free network topology // Heliyon. 2024. Vol. 10. Iss. 16. P. e35958. DOI:10.1016/j.heliyon.2024.e35958. EDN:VACDIH

24. Karaboga D. An idea based on honey bee swarm for numerical optimization. Technical Report-tr06. 2005. URL: https://abc.erciyes.edu.tr/pub/tr06_2005.pdf (Accessed 02.07.2025)

25. Xiao W.-S., Li G., Liu C., Tan L. A novel chaotic and neighborhood search-based artificial bee colony algorithm for solving optimization problems // Scientific Reports. 2023. Vol. 13. P. 20496. DOI:10.1038/s41598-023-44770-8. EDN:MDLWOS

26. Dorigo M., Maniezzo V., Colorni A. Ant system: An Autocatalytic Optimizing Process. 1991.

27. Misra B., Chakraborty S. Ant Colony Optimization ‒ Recent Variants, Application and Perspectives // Dey N. (ed.) Applications of Ant Colony Optimization and its Variants: Case Studies and New Developments. Singapore: Springer Nature, 2024. PP. 1–17. DOI:10.1007/978-981-99-7227-2_1

28. Olivari L. Reducing ACO Population Size to Increase Computational Speed // Tehnički glasnik. 2024. Vol. 18. Iss. 4. PP. 532–539. DOI:10.31803/tg-20230825125127. EDN:ZSJBRX

29. Jiang H., Liu D., Liu X., Wu W., Jiang H. Efficient Grey Wolf Optimization: A High-Performance Optimizer with Reduced Memory Usage and Accelerated Convergence. 2024. DOI:10.20944/preprints202412.1974.v1

30. Kaveh A., Yosefpoor H. Competition of Three Chaotic Meta-heuristic Algorithms with Physical Inspiration for Optimal Design of Truss Structures // Periodica Polytechnica Civil Engineering. 2024. Vol. 68. Iss. 4. PP. 1211–1228. DOI:10.3311/PPci.36853. EDN:SEVTPJ

31. Rodriguez J.S., Parker R.B., Laird C.D., Nicholson B.L., Siirola J.D., Bynum M.L. Scalable Parallel Nonlinear Optimization with PyNumero and Parapint // INFORMS Journal on Computing. 2023. Vol. 35. Iss. 2. PP. 509–517. DOI:10.1287/ijoc.2023.1272. EDN:MQKQXF

32. Fuentes P.A., Tirado F.F., Quintas D.G., Meana J.J., Muniz A.P. On the Fast Evaluation of Polynomials // Journal of Advances in Mathematics and Computer Science. 2022. Vol. 37. Iss. 6. PP. 20–35. DOI:10.9734/jamcs/2022/v37i630457

33. Baichoo S., Ouzounis C.A. Computational complexity of algorithms for sequence comparison, short-read assembly and genome alignment // Biosystems. 2017. Vol. 156-157. PP. 72–85. DOI:10.1016/j.biosystems.2017.03.003

34. Yang H. Analysis and study on path planning algorithms in the further mobile action // Journal of Physics: Conference Series. 2024. Vol. 2824. P. 012006. DOI:10.1088/1742-6596/2824/1/012006. EDN:YVOPJW

35. Shanmugapriya M., Manivannan K.K. Compare the Performance of Meta-Heuristics Algorithm: A Review // Thanigaivelan R., Suchithra M., Kaliappan S., Mothilal T. (ed.) Metaheuristics Algorithm and Optimization of Engineering and Complex Systems. IGI Global Scientific Publishing, 2024. PP. 247–258. DOI:10.4018/979-8-3693-3314-3.ch013

36. Cuevas E., Galvez J., Avalos O., Wario F. Machine Learning and Metaheuristic Computation. John Wiley & Sons, 2024. 437 p. DOI:10.1002/9781394229680

37. Kulkarni V.R., Desai V. ABC and PSO: A comparative analysis // Proceedings of the International Conference on Computational Intelligence and Computing Research (ICCIC, Chennai, India, 15‒17 December 2016). IEEE, 2016. DOI:10.1109/ICCIC.2016.7919625

38. Dorigo M., Stützle T. Ant Colony Optimization: Overview and Recent Advances // International Series in Operations Research & Management Science. Springer, 2019. PP. 311–351. DOI:10.1007/978-3-319-91086-4_10

39. Faris H., Aljarah I., Al-Betar M.A., Mirjalili S. Grey wolf optimizer: a review of recent variants and applications // Neural Computing and Applications. 2018. Vol. 30. PP. 413‒435. DOI:10.1007/s00521-017-3272-5. EDN:JLGMRW

40. Chaudhari K., Thakkar A. Travelling Salesman Problem: An Empirical Comparison Between ACO, PSO, ABC, FA and GA // Proceedings of the Conference on Emerging Research in Computing, Information, Communication and Applications (ERCICA). Advances in Intelligent Systems and Computing. Singapore: Springer, 2019. Vol. 906. PP. 397–405. DOI:10.1007/978-981-13-6001-5_32

41. Negi G., Kumar A., Pant S., Ram M. GWO: a review and applications // International Journal of System Assurance Engineering and Management. 2020. Vol. 12. P. 1–8. DOI:10.1007/s13198-020-00995-8

42. Seyyedabbasi A., Kiani F. I-GWO and Ex-GWO: improved algorithms of the Grey Wolf Optimizer to solve global optimization problems // Engineering with Computers. 2021. Vol. 37. PP. 509‒532. DOI:10.1007/s00366-019-00837-7

43. Миронов А.А., Файзуллин Р.В., Кузикова А.В. Оптимизация параметра величины колонии в муравьином алгоритме для решения задачи маршрутизации в сетях связи // Интеллектуальные системы в производстве. 2024. Т. 22. № 2. С. 63–68. DOI:10.22213/2410-9304-2024-2-63-68. EDN:YDLNPI

44. Kathane K.A., Shete R.M., Nawkhare R., Damahe L.B., Jadhav N.N., Dehankar J.N. Optimizing Dynamic Source Routing Protocol Using Computational Intelligent Approach // Proceedings of the 4th International Conference on Computer, Communication, Control & Information Technology, C3IT, Hooghly, India, 28‒29 September 2024. IEEE, 2024. DOI:10.1109/C3IT60531.2024.10829484

45. Kansal V., Al-Farouni M., Bansal S., Michaelson J., Kumar S., Veena C.H. A Novel Ant Colony Optimization Algorithm for Dynamic Routing in Communication Networks // Proceedings of the International Conference on Communication, Computer Sciences and Engineering (IC3SE, Gautam Buddha Nagar, India, 09‒11 May 2024). IEEE, 2024. PP. 1640–1645. DOI:10.1109/IC3SE62002.2024.10593344

46. Razooqi Y., Al-Asfoor M., Abed M.H. Optimise Energy Consumption of Wireless Sensor Networks by using modified Ant Colony Optimization // Acta Technica Jaurinensis. 2024. Vol. 17. Iss. 3. PP. 111–117. DOI:10.14513/actatechjaur.00742. EDN:CJUYDE

47. Kumar R., Kumar K., Sharma S. Burst Formation and Burst Assignment to Ingress Nodes in Optical Burst Switching Network Using ABC // International Journal of Electronics and Communication Engineering. 2023. Vol. 10. Iss. 10. PP. 25‒39. DOI:10.14445/23488549/ijece-v10i10p103. EDN:YRWAEA

48. Jierui L. Research on the Application of Ant Colony Algorithm in Optimizing Transportation Routes in Cold Chain Logistics // Proceedings of the 2nd International Conference on Mechatronics, IoT and Industrial Informatics (ICMIII, Melbourne, Australia, 12‒14 June 2024). IEEE, 2024. PP. 238–243. DOI:10.1109/ICMIII62623.2024.00050

49. Umar M.M., Mohammed A., Abdulazeez A. Review of QoS-aware resource allocation schemes for 5g networks // Dutse Journal of Pure and Applied Sciences. 2024. Vol. 10. Iss. 3c. PP. 296–303. DOI:10.4314/dujopas.v10i3c.28. EDN:YKTOHU

50. Bikkasani D.C., Yerabolu M.R. AI-Driven 5G Network Optimization: A Comprehensive Review of Resource Allocation, Traffic Management, and Dynamic Network Slicing // American Journal of Artificial Intelligence. 2024. Vol. 8. Iss. 2. PP. 55–62. DOI:10.11648/j.ajai.20240802.14. EDN:AOHEEN

51. Zahoor S., Javaid S., Javaid N., Ashraf M., Ishmanov F., Afzal M.K. Cloud–Fog–Based Smart Grid Model for Efficient Resource Management // Sustainability. 2018. Vol. 10. Iss. 6. P. 2079. DOI:10.3390/su10062079

52. Zhang W., Tuo K. Research on Offloading Strategy for Mobile Edge Computing Based on Improved Grey Wolf Optimization Algorithm // Electronics. 2023. Vol. 12. Iss. 11. P. 2533. DOI:10.3390/electronics12112533. EDN:AYUJJB

53. Liu W., Li C., Zheng A., Zheng Z., Zhang Z., Xiao Y. Fog Computing Resource-Scheduling Strategy in IoT Based on Artificial Bee Colony Algorithm // Electronics. 2023. Vol. 12. Iss. 7. P. 1511. DOI:10.3390/electronics12071511. EDN:EVPFUW

54. Мутханна А.С.А. Интегральное решение проблемы размещения контроллеров и балансировки нагрузки: 2 // Труды учебных заведений связи. 2023. Т. 9. № 2. С. 81–93. DOI:10.31854/1813-324X-2023-9-2-81-93. EDN:FTJGMC

55. Лисов А.А., Возмилов А.Г., Гундарев К.А., Кулганатов А.З. Применение алгоритма стаи серых волков и нейронных сетей для решения дискретных задач // Труды учебных заведений связи. 2024. T. 10. № 5. C. 80–91. DOI:10.31854/ 1813-324X-2024-10-5-24-35. EDN:BEODCG

56. Волков А.Н. Динамические туманные вычисления и бессерверная архитектура: на пути к зеленым ИКТ// Труды учебных заведений связи. 2024. Т. 10. № 3. С. 24‒34. DOI:10.31854/1813-324X-2024-10-3-24-34. EDN:QOELMJ

57. Gaikwad V., Naik A. An improved resource allocation architecture utilising swarm intelligence for mm-wave MIMO communication architecture // International Journal of Wireless and Mobile Computing. 2023. Vol. 25. Iss. 2. PP. 190–199. DOI:10.1504/ijwmc.2023.133070. EDN:VCBTHS

58. Liang Y.-C. Artificial Intelligence for Dynamic Spectrum Management // Dynamic Spectrum Management: From Cognitive Radio to Blockchain and Artificial Intelligence. Singapore: Springer, 2020. PP. 147–166. DOI:10.1007/978-981-15-0776-2_6

59. Alabi C.A., Idakwo M.A., Imoize A.L., Adamu T., Sur S.N. AI for spectrum intelligence and adaptive resource management // Sur S.N., Imoize A.L., Bhattacharya A., Kandar D., Banerjee J.S. (eds.) Artificial Intelligence for Wireless Communication Systems. CRC Press, 2024. 27 p. DOI:10.1201/9781003517689-3

60. Khan K., Goodridge W. Swarm Intelligence-Driven Client Selection for Federated Learning in Cybersecurity applications // arXiv:2411.18877. 2024. DOI:10.48550/arXiv.2411.18877

61. Zhang J., Wang H., Wang X. Application of artificial bee colony algorithm based on homogenization mapping and collaborative acquisition control in network communication security // PLoS One. 2024. Vol. 19. Iss. 7. P. e0306699. DOI:10.1371/journal.pone.0306699. EDN:BTHRFI

62. Ma Y., Chen J., Lv W., Qiu X., Zhang Y., Liu W. An improved artificial bee colony algorithm to minimum propagation latency and balanced load for controller placement in Software Defined Network // Computer Networks. 2024. Vol. 250. P. 110600. DOI:10.1016/j.comnet.2024.110600. EDN:KRNCGH

63. Pliatsios D. Comparison of Swarm Intelligence Methods for Joint Resource Orchestration in Open Radio Access Network // Proceedings of the 14th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP, Rome, Italy, 17‒19 July 2024). IEEE, 2024. PP. 632–637. DOI:10.1109/CSNDSP60683.2024.10636586

64. Berlinski M. Ant Colony Algorithms Application for Telco Networks Performance with Multicriteria Optimization // Proceedings of the International Conference on Software, Telecommunications and Computer Networks (SoftCOM, Split, Croatia, 21‒23 September 2023). IEEE, 2023. DOI:10.23919/SoftCOM58365.2023.10271586

65. Venugopal P.S., Bharathy K.R., Gurusamy R., Rajkumar. Optimization of Delay and Energy in Wireless Body Area Networks Using Swarm Intelligence Based Dynamic Bandwidth Allocation Algorithm // Proceedings of the International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS, Bengaluru, India, 17‒18 December 2024). IEEE, 2024. PP. 127–131. DOI:10.1109/ICICNIS64247.2024.10823293

66. Zhao Y., Men L. Group Intelligence Optimization Algorithm of Adaptive Trigonometric Function and T-Distributed Perturbation Strategy // Proceedings of the 6th International Conference on Communications, Information System and Computer Engineering (CISCE, Guangzhou, China, 10‒12 May 2024). IEEE, 2024. PP. 740–744. DOI:10.1109/CISCE62493.2024.10653078

67. Liu Y., Huo L., Wu J., Bashir A.K. Swarm Learning-Based Dynamic Optimal Management for Traffic Congestion in 6G-Driven Intelligent Transportation System // IEEE Transactions on Intelligent Transportation Systems. 2023. Vol. 24. Iss. 7. PP. 7831–7846. DOI:10.1109/tits.2023.3234444. EDN:ILDTNW

68. Ahmad I., Qayum F., Rahman S.U., Srivastava G. Using Improved Hybrid Grey Wolf Algorithm Based on Artificial Bee Colony Algorithm Onlooker and Scout Bee Operators for Solving Optimization Problems // International Journal of Computational Intelligence Systems. 2024. Vol. 17. Iss. 1. P. 111. DOI:10.1007/s44196-024-00497-6. EDN:DJQIPZ

69. Furio C., Lamberti L., Pruncu C.I. An Efficient and Fast Hybrid GWO-JAYA Algorithm for Design Optimization // Applied Sciences. 2024. Vol. 14. Iss. 20. P. 9610. DOI:10.3390/app14209610

70. Li Y., Lian Z., Zhou K., Dai Y. A quasi-opposition learning and chaos local search based on walrus optimization for global optimization problems // Scientific Reports. 2025. Vol. 15. P. 2881. DOI:10.1038/s41598-025-85751-3. EDN:BZPYYV

71. Sari D.W., Dwijayanti S., Suprapto B.Y. Ant Colony Optimization-Based Path Planning for Autonomous Vehicle Navigation Systems // Proceedings of the International Conference on Electrical Engineering and Computer Science (ICECOS, Palembang, Indonesia, 25‒26 September 2024). IEEE, 2024. PP. 135–140. DOI:10.1109/ICECOS63900.2024.10791115

72. Alfa A.A., Misra S., Abayomi-Alli A., Arogundade O., Jonathan O., Ahuja R. Comparative Analysis of Intelligent Solutions Searching Algorithms of Particle Swarm Optimization and Ant Colony Optimization for Artificial Neural Networks Target Dataset // Proceedings of Second International Conference on Computing, Communications, and Cyber-Security. Lecture Notes in Networks and Systems. Singapore: Springer, 2021. Vol. 203. PP. 459–470. DOI:10.1007/978-981-16-0733-2_32

73. Kalpana N. ABC Algorithm for Evaluating the Performance of the SVC and Optimal Power Flow // Proceedings of the International Conference on Recent Trends in Communication and Intelligent Systems (ICRTCIS, Rajasthan, India, 28‒29 April 2023). Algorithms for Intelligent Systems. Singapore: Springer Nature, 2023. PP. 37–47. DOI:10.1007/978-981-99-5792-7_3

74. Almajidi A.M., Pawar V.P., Alammari A., Ali N.S. ABC-Based Algorithm for Clustering and Validating WSNs // Proceedings of the International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA, Goa, India, 16–17 August 2019). Algorithms for Intelligent Systems. Singapore: Springer, 2020. PP. 117–125. DOI:10.1007/978-981-15-1632-0_13

75. Ding W., Yao H., Ju H., Huang J., Jiang S., Chen Y. Pheromone-guided parallel rough hypercuboid attribute reduction algorithm // Applied Soft Computing. 2024. Vol. 156. P. 111479. DOI:10.1016/j.asoc.2024.111479. EDN:HKPVIE

76. Warnakulasooriya K., Segev A. Comparative analysis of accuracy and computational complexity across 21 swarm intelligence algorithms // Evolutionary Intelligence. 2024. Vol. 18. P. 18. DOI:10.1007/s12065-024-00997-6. EDN:FHRUUA

77. Khera V. Comparative Study of Evolutionary Algorithms // International Journal of Science and Research. 2023. Vol. 12. Iss. 6. PP. 836–840. DOI:10.21275/sr23610122607. EDN:LPWBXF

78. Kalpana N. Innovative Method for Assessing Optimal Power Flow and SVC Performance Using the ABC Algorithm // Proceedings of the 6th International Conference on Communications and Cyber Physical Engineering (ICCCE, Hyderabad, India, 28–29 April 2023). Lecture Notes in Electrical Engineering. Singapore: Springer Nature, 2024. Vol. 1096. PP. 21–31. DOI:10.1007/978-981-99-7137-4_3

79. Du H., Zhu Z., Gu S. Research on Optimization of Computer Network Routing Based on Ant Colony Algorithm // Proceedings of the 2nd International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS, Bristol, United Kingdom, 29‒31 July 2023). IEEE, 2023. PP. 365–368. DOI:10.1109/AIARS59518.2023.00080

80. Makhadmeh S.N., Al-Betar M.A., Al-Obeidat F., Alomari O.A., Abasi A.K., Tubishat M., et al. A multi-objective grey wolf optimizer for energy planning problem in smart home using renewable energy systems // Sustainable Operations and Computers. 2024. Vol. 5. PP. 88–101. DOI:10.1016/j.susoc.2024.04.001. EDN:HSZMYI

81. Makhadmeh S.N., Al-Betar M.A., Al-Obeidat F., Alomari O.A., Abasi A.K., Tubishat M., et al. A Multi-objective Grey Wolf Optimizer for Power Scheduling Problem in Smart Home Using Renewable Energy Systems // Research Square. 2023. DOI:10.21203/rs.3.rs-3771300/v1

82. Huang X., Xu R., Yu W., Wu S. Evaluation and Analysis of Heuristic Intelligent Optimization Algorithms for PSO, WDO, GWO and OOBO // Mathematics. 2023. Vol. 11. Iss. 21. P. 4531. DOI:10.3390/math11214531. EDN:INHEUT

83. Yadav U.K., Singh V.P. Systematically derived weights based order diminution of continuous systems using GWO algorithm // Journal of the Franklin Institute. 2022. Vol. 359. Iss. 17. P. 9902–9924. DOI:10.1016/j.jfranklin.2022.09.050. EDN:ZXUCUI

84. Shyshatskyi A., Kashkevich S., Kyrychenko I., Khakhlyuk O., Kubrak V., Kоval A., et al. Methodical approach to assessing the state of hierarchical systems using a metaheuristic algorithm // Eastern-European Journal of Enterprise Technologies. 2024. Vol. 5. Iss. 4(131). PP. 82–88. DOI:10.15587/1729-4061.2024.311235. EDN:HSRFIL

85. Shahakar M., Mahajan S.A., Patil L. Optimizing System Resources and Adaptive Load Balancing Framework Leveraging ACO and Reinforcement Learning Algorithms // Journal of Electrical Systems. 2024. Vol. 20. Iss. 1s. PP. 244–256. DOI:10.52783/jes.768. EDN:DTXCKX

86. Cao B., Chen Y., Liu X., He H., Song H., Lv Z. Multiobjective Resource Allocation Strategy for Metaverse Resource Management // Proceedings of the International Conference on Metaverse Computing, Networking and Applications (MetaCom, Kyoto, Japan, 26‒28 June 2023). IEEE, 2023. PP. 564–570. DOI:10.1109/MetaCom57706.2023.00100

87. Kambhampati R.T. AI Telco Research: Advancements in Telecommunications Scientific Discovery // International Journal for Research in Applied Science & Engineering Technology. 2024. Vol. 12. Iss. 9. PP. 1514–1519. DOI:10.22214/ijraset.2024.64339

88. Jadon S.S., Tiwari R., Sharma H., Bansal J.C. Hybrid Artificial Bee Colony algorithm with Differential Evolution // Applied Soft Computing. 2017. Vol. 58. PP. 11–24. DOI:10.1016/j.asoc.2017.04.018

89. Seyyedabbasi A., Tareq Tareq W.Z., Bacanin N. An Effective Hybrid Metaheuristic Algorithm for Solving Global Optimization Algorithms // Multimedia Tools and Applications. 2024. Vol. 83. PP. 85103–85138. DOI:10.1007/s11042-024-19437-9. EDN:HMWSUL

90. Lehre P.K., Qin X. Self-adaptation Can Improve the Noise-tolerance of Evolutionary Algorithms // Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA, Potsdam, Germany, 30 August 2023 ‒ 1 September 2023). New York: Association for Computing Machinery, 2023. PP. 105–116. DOI:10.1145/3594805.3607128

91. Lehre P.K., Qin X. Self-adaptation Can Help Evolutionary Algorithms Track Dynamic Optima // Proceedings of the Genetic and Evolutionary Computation Conference (GECCO, Lisbon Portugal, 15‒19 July 2023). New York: Association for Computing Machinery, 2023. PP. 1619–1627. DOI:10.1145/3583131.3590494

92. Zhang Y., Cai Y. Adaptive dynamic self-learning grey wolf optimization algorithm for solving global optimization problems and engineering problems // Mathematical Biosciences and Engineering. 2024. Vol. 21. Iss. 3. PP. 3910–3943. DOI:10.3934/mbe.2024174. EDN:UGPDBW

93. Barrion M.H., Bandala A., Maningo J.M., Dadios E., Naguib R. Advancing Robotic Swarms with Blockchain Technology: A Dynamic Two-Factor Authentication Consensus Framework // Research Square. 2024. DOI:10.21203/rs.3.rs-5301694/v1

94. Yang H. Swarm Contract: A Multi-Sovereign Agent Consensus Mechanism // arXiv:2412.19256. 2024. DOI:10.48550/ arXiv.2412.19256

95. Li Y. Quantum Ant Colony Algorithm for Solving the Traveling Salesman Problem: A Theoretical and Practical Analysis // Applied and Computational Engineering. 2024. Vol. 110. Iss. 1. PP. 175–181. DOI:10.54254/2755-2721/110/2024MELB0121

96. Tajabadi M., Heider D. Fair swarm learning: Improving incentives for collaboration by a fair reward mechanism // Knowledge-Based Systems. 2024. Vol. 304. P. 112451. DOI:10.1016/j.knosys.2024.112451. EDN:UOAGIK

97. Moustafa N. GH-Twin: Graph Learning Empowered Hierarchical Digital Twin for Optimizing Self-Healing Networks // Sustainable Machine Intelligence Journal. 2024. Vol. 8. PP. 35‒45. DOI:10.61356/smij.2024.8289. EDN:DNPELS

98. Wang N., Wu Y., Lorenzo B., Liu B. Semantic-Aware Architecture Design for a Lifelong Swarm Metaverse // IEEE Internet of Things Journal. 2025. Vol. 12. Iss. 9. PP. 12468–12482. DOI:10.1109/JIOT.2024.3520518