DataVXJournal of Bionic EngineeringPaper
journal article·2025-05

Multi-strategy Enhanced Hiking Optimization Algorithm for Task Scheduling in the Cloud Environment

Libang Wu, Shaobo Li, Fengbin Wu, Rongxiang Xie, Panliang Yuan

Journal of Bionic Engineering· Volume 22· Issue 3
Publisher page
Bibliographic record

Abstract

No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.

Source: Not provided · Quality: missing

Research context

OpenAlex topics: Cloud Computing and Resource Management · Metaheuristic Optimization Algorithms Research · Distributed and Parallel Computing Systems

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (49)

Show source-provided references
1. Abdel-Basset, M., El-Shahat, D., Jameel, M., & Abouhawwash, M. (2023). Exponential distribution optimizer (EDO): A novel math-inspired algorithm for global optimization and engineering problems. Artificial Intelligence Review, 56(9), 9329–9400. https://doi.org/10.1007/s10462-023-10403-910.1007/s10462-023-10403-9 ↗
2. Mirjalili, S. (2016). SCA: A sine cosine algorithm for solving optimization problems. Knowledge-based Systems, 96, 120–133. https://doi.org/10.1016/j.knosys.2015.12.02210.1016/j.knosys.2015.12.022 ↗
3. Abualigah, L., & Altalhi, M. (2024). A novel generalized normal distribution arithmetic optimization algorithm for global optimization and data clustering problems. Journal of Ambient Intelligence and Humanized Computing, 15(1), 389–417. https://doi.org/10.1007/s12652-022-03898-710.1007/s12652-022-03898-7 ↗
4. Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., & Gandomi, A. H. (2021). The arithmetic optimization algorithm. Computer Methods in Applied Mechanics and Engineering, 376, 113609. https://doi.org/10.1016/j.cma.2020.11360910.1016/j.cma.2020.113609 ↗
5. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf optimizer. Advances in Engineering Software, 69, 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.00710.1016/j.advengsoft.2013.12.007 ↗
6. Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. IEEE Computational Intelligence Magazine, 1(4), 28–39. https://doi.org/10.1109/MC1.2006.32969110.1109/MC1.2006.329691 ↗
7. Mirjalili, S., & Lewis, A. (2016). The Whale optimization algorithm. Advances in Engineering Software, 95, 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.00810.1016/j.advengsoft.2016.01.008 ↗
8. Abdollahzadeh, B., Khodadadi, N., Barshandeh, S., Trojovský, P., Gharehchopogh, F. S., El-kenawy, E. S. M., & Mirjalili, S. (2024). Puma optimizer (PO): A novel metaheuristic optimization algorithm and its application in machine learning. Cluster Computing, 1–49. https://doi.org/10.1007/s10586-023-04221-510.1007/s10586-023-04221-5 ↗
9. Xue, J., & Shen, B. (2023). Dung beetle optimizer: A new meta-heuristic algorithm for global optimization. The Journal of Supercomputing, 79(7), 7305–7336. https://doi.org/10.1007/s11227-022-04959-610.1007/s11227-022-04959-6 ↗
10. Meng, X. B., Gao, X. Z., Lu, L., Liu, Y., & Zhang, H. (2016). A new bio-inspired optimisation algorithm: Bird swarm algorithm. Journal of Experimental & Theoretical Artificial Intelligence, 28(4), 673–687. https://doi.org/10.1080/0952813X.2015.104253010.1080/0952813X.2015.1042530 ↗
11. Wang, J., & Chen, H. (2018). BSAS: Beetle swarm antennae search algorithm for optimization problems. https://doi.org/10.48550/arXiv.1807.1047010.48550/arXiv.1807.10470 ↗
12. Abdel-Basset, M., Mohamed, R., Jameel, M., & Abouhawwash, M. (2023). Spider Wasp optimizer: A novel meta-heuristic optimization algorithm. Artificial Intelligence Review, 56(10). https://doi.org/10.1007/s10462-023-10446-y10.1007/s10462-023-10446-y ↗
13. Mirjalili, S., & Mirjalili, S. (2019). Genetic algorithm. Evolutionary Algorithms and Neural Networks: Theory and Applications, 43–55. https://doi.org/10.1007/978-3-319-93025-1_410.1007/978-3-319-93025-1_4 ↗
14. Bartz-Beielstein, T., Branke, J., Mehnen, J., & Mersmann, O. (2014). Evolutionary algorithms. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 4(3), 178–195. https://doi.org/10.1002/widm.112410.1002/widm.1124 ↗
15. Pant, M., Zaheer, H., Garcia-Hernandez, L., & Abraham, A. (2020). Differential evolution: A review of more than two decades of research. Engineering Applications of Artificial Intelligence, 90, 103479. https://doi.org/10.1016/j.engappai.2020.10347910.1016/j.engappai.2020.103479 ↗
16. Simon, D. (2008). Biogeography-based optimization. IEEE Transactions on Evolutionary Computation, 12(6), 702–713. https://doi.org/10.1109/TEVC.2008.91900410.1109/TEVC.2008.919004 ↗
17. Harik, G. R., Lobo, F. G., & Goldberg, D. E. (1999). The compact genetic algorithm. IEEE Transactions on Evolutionary Computation, 3(4), 287–297. https://doi.org/10.1109/4235.79797110.1109/4235.797971 ↗
18. Deep, K., Singh, K. P., Kansal, M. L., & Mohan, C. (2009). A real coded genetic algorithm for solving integer and mixed integer optimization problems. Applied Mathematics and Computation, 212(2), 505–518. https://doi.org/10.1016/j.amc.2009.02.04410.1016/j.amc.2009.02.044 ↗
19. Zhu, D., Wang, S., Zhou, C., Yan, S., & Xue, J. (2024). Human memory optimization algorithm: A memory-inspired optimizer for global optimization problems. Expert Systems With Applications, 237, 121597. https://doi.org/10.1016/j.eswa.2023.12159710.1016/j.eswa.2023.121597 ↗
20. Tian, Z., & Gai, M. (2024). Football team training algorithm: A novel sport-inspired meta-heuristic optimization algorithm for global optimization. Expert Systems With Applications, 245, 123088. https://doi.org/10.1016/j.eswa.2023.12308810.1016/j.eswa.2023.123088 ↗
21. Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-aided Design, 43(3), 303–315. https://doi.org/10.1016/j.cad.2010.12.01510.1016/j.cad.2010.12.015 ↗
22. Ray, T., & Yao, X. (2009, May). A cooperative coevolutionary algorithm with correlation based adaptive variable partitioning. In 2009 IEEE Congress on Evolutionary Computation, Trondheim, Norway (pp. 983–989). https://doi.org/10.1109/CEC.2009.498305210.1109/CEC.2009.4983052 ↗
23. Kumar, M., Kulkarni, A. J., & Satapathy, S. C. (2018). Socio evolution & learning optimization algorithm: A socio-inspired optimization methodology. Future Generation Computer Systems, 81, 252–272. https://doi.org/10.1016/j.future.2017.10.05210.1016/j.future.2017.10.052 ↗
24. Moosavi, S. H. S., & Bardsiri, V. K. (2019). Poor and rich optimization algorithm: A new human-based and multi populations algorithm. Engineering Applications of Artificial Intelligence, 86, 165–181. https://doi.org/10.1016/j.engappai.2019.08.02510.1016/j.engappai.2019.08.025 ↗
25. Satapathy, S., & Naik, A. (2016). Social group optimization (SGO): A new population evolutionary optimization technique. Complex & Intelligent Systems, 2(3), 173–203. https://doi.org/10.1007/s40747-016-0022-810.1007/s40747-016-0022-8 ↗
26. Fadakar, E., & Ebrahimi, M. (2016, March). A new metaheuristic football game inspired algorithm. In 2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC), Bam, Iran (pp. 6–11). https://doi.org/10.1109/CSIEC.2016.748212010.1109/CSIEC.2016.7482120 ↗
27. Zhou, Z., Li, F., Zhu, H., Xie, H., Abawajy, J. H., & Chowdhury, M. U. (2020). An improved genetic algorithm using greedy strategy toward task scheduling optimization in cloud environments. Neural Computing and Applications, 32, 1531–1541. https://doi.org/10.1007/s00521-019-04119-710.1007/s00521-019-04119-7 ↗
28. Wei, X. (2020). Task scheduling optimization strategy using improved ant colony optimization algorithm in cloud computing. Journal of Ambient Intelligence and Humanized Computing, 1–12. https://doi.org/10.1007/s12652-020-02614-710.1007/s12652-020-02614-7 ↗
29. Khan, M. S. A., & Santhosh, R. (2022). Task scheduling in cloud computing using hybrid optimization algorithm. Soft Computing, 26(23), 13069–13079. https://doi.org/10.1007/s00500-021-06488-510.1007/s00500-021-06488-5 ↗
30. Tsai, J. T., Fang, J. C., & Chou, J. H. (2013). Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm. Computers & Operations Research, 40(12), 3045–3055. https://doi.org/10.1016/j.cor.2013.06.01210.1016/j.cor.2013.06.012 ↗
31. Wu, L. B., Li, S., Wu, F., & Xie, R. (2024, July). An enhanced puma optimizer for cloud computing task scheduling. In 2024 3rd International Conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC), Mianyang, China (pp.260–264). IEEE. https://doi.org/10.1109/RAIIC61787.2024.1067135310.1109/RAIIC61787.2024.10671353 ↗
32. Pirozmand, P., Jalalinejad, H., Hosseinabadi, A. A. R., Mirkamali, S., & Li, Y. (2023). An improved particle swarm optimization algorithm for task scheduling in cloud computing. Journal of Ambient Intelligence and Humanized Computing, 14(4), 4313–4327. https://doi.org/10.1007/s12652-023-04541-910.1007/s12652-023-04541-9 ↗
33. Moon, Y., Yu, H., Gil, J. M., & Lim, J. (2017). A slave ants based ant colony optimization algorithm for task scheduling in cloud computing environments. Human-centric Computing and Information Sciences, 7, 28. https://doi.org/10.1186/s13673-017-0109-210.1186/s13673-017-0109-2 ↗
34. Eldesokey, H. M., El-atty, A., El‐Shafai, S. M., Amoon, W., Abd, M., & El‐Samie, F. E. (2021). Hybrid swarm optimization algorithm based on task scheduling in a cloud environment. International Journal of Communication Systems, 34(13), e4694. https://doi.org/10.1002/dac.469410.1002/dac.4694 ↗
35. Su, Y., Bai, Z., & Xie, D. (2021). The optimizing resource allocation and task scheduling based on cloud computing and ant colony optimization algorithm. Journal of Ambient Intelligence and Humanized Computing, 1–9. https://doi.org/10.1007/s12652-021-03445-w10.1007/s12652-021-03445-w ↗
36. Moon, Y., Yu, H., Gil, J. M., & Lim, J. (2017). A slave ants based ant colony optimization algorithm for task scheduling in cloud computing environments. Human-centric Computing and Information Sciences, 7, 1–10. https://doi.org/10.1186/s13673-017-0109-210.1186/s13673-017-0109-2 ↗
37. Eldesokey, H. M., El-atty, A., El‐Shafai, S. M., Amoon, W., Abd, M., & El‐Samie, F. E. (2021). Hybrid swarm optimization algorithm based on task scheduling in a cloud environment. International Journal of Communication Systems, 34(13). https://doi.org/10.1002/dac.469410.1002/dac.4694 ↗
38. Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67–82. https://doi.org/10.1109/4235.58589310.1109/4235.585893 ↗
39. Oladejo, S. O., Ekwe, S. O., & Mirjalili, S. (2024). The hiking optimization algorithm: A novel human-based metaheuristic approach. Knowledge-based Systems, 296, 111880. https://doi.org/10.1016/j.knosys.2024.11188010.1016/j.knosys.2024.111880 ↗
40. Ahmad, M. F., Isa, N. A. M., Lim, W. H., & Ang, K. M. (2022). Differential evolution with modified initialization scheme using chaotic oppositional based learning strategy. Alexandria Engineering Journal, 61(12), 11835–11858. https://doi.org/10.1016/j.aej.2022.05.02810.1016/j.aej.2022.05.028 ↗
41. Mahdavi, S., Rahnamayan, S., & Deb, K. (2018). Opposition based learning: A literature review. Swarm and Evolutionary Computation, 39, 1–23. https://doi.org/10.1016/j.swevo.2017.09.01010.1016/j.swevo.2017.09.010 ↗
42. Tian, D. (2017). Particle swarm optimization with chaos-based initialization for numerical optimization. Intelligent Automation & Soft Computing, 1–12. https://doi.org/10.1080/10798587.2017.129388110.1080/10798587.2017.1293881 ↗
43. Kaur, G., & Arora, S. (2018). Chaotic Whale optimization algorithm. Journal of Computational Design and Engineering, 5(3), 275–284. https://doi.org/10.1016/j.jcde.2017.12.00610.1016/j.jcde.2017.12.006 ↗
44. Wang, L., Liu, X., Sun, M., Qu, J., & Wei, Y. (2018). A new chaotic starling particle swarm optimization algorithm for clustering problems. Mathematical Problems in Engineering, 2018(1), 8250480. https://doi.org/10.1155/2018/825048010.1155/2018/8250480 ↗
45. Kohli, M., & Arora, S. (2018). Chaotic grey wolf optimization algorithm for constrained optimization problems. Journal of Computational Design and Engineering, 5(4), 458–472. https://doi.org/10.1016/j.jcde.2017.02.00510.1016/j.jcde.2017.02.005 ↗
46. Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN’95-international Conference on Neural Networks, (pp. 1942–1948). Perth, WA, Australia: IEEE. https://doi.org/10.1109/ICNN.1995.48896810.1109/ICNN.1995.488968 ↗
47. Jia, H., Peng, X., & Lang, C. (2021). Remora optimization algorithm. Expert Systems With Applications, 185, 115665. https://doi.org/10.1016/j.eswa.2021.11566510.1016/j.eswa.2021.115665 ↗
48. Jena, B., Naik, M. K., Wunnava, A., & Panda, R. (2021). A differential squirrel search algorithm. In Advances in Intelligent Computing and Communication: Proceedings of ICAC 2020 (pp. 143–152). Springer Singapore. https://doi.org/10.1007/978-981-16-0695-3_1510.1007/978-981-16-0695-3_15 ↗
49. Fu, S., Li, K., Huang, H., Ma, C., Fan, Q., & Zhu, Y. (2024). Red-billed blue magpie optimizer: A novel metaheuristic algorithm for 2D/3D UAV path planning and engineering design problems. Artificial Intelligence Review, 57(6), 1–89. https://doi.org/10.1007/s10462-024-10716-310.1007/s10462-024-10716-3 ↗