journal article·2025-05

A Multi-group Meta-heuristic Optimization with Dynamic Population Partition and Hybrid Strategies: Algorithm and Applications

Dongshuai Niu, Guangwen Yi, Long Chen, Zhenzhou Tang

Journal of Bionic Engineering· Volume 22· Issue 3
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OpenAlex topics: Metaheuristic Optimization Algorithms Research · Optimization and Search Problems · Advanced Multi-Objective Optimization Algorithms

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References (38)

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1. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN’95-International Conference on Neural Networks, Perth, WA, Australia (vol. 4, pp. 1942–1948). IEEE.10.1109/ICNN.1995.488968 ↗
2. 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 ↗
3. Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software,114, 163–191. https://doi.org/10.1016/j.advengsoft.2017.07.00210.1016/j.advengsoft.2017.07.002 ↗
4. Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems,97, 849–872. https://doi.org/10.1016/j.future.2019.02.02810.1016/j.future.2019.02.028 ↗
5. Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-Qaness, M. A., & Gandomi, A. H. (2021). Aquila optimizer: A novel meta-heuristic optimization algorithm. Computers & Industrial Engineering,157, 107250. https://doi.org/10.1016/j.cie.2021.10725010.1016/j.cie.2021.107250 ↗
6. Bairwa, A. K., Joshi, S., & Singh, D. (2021). Dingo optimizer: A nature-inspired metaheuristic approach for engineering problems. Mathematical Problems in Engineering,2021, 1–12. https://doi.org/10.1155/2021/257186310.1155/2021/2571863 ↗
7. Seyyedabbasi, A., & Kiani, F. (2022). Sand Cat swarm optimization: A nature-inspired algorithm to solve global optimization problems. Engineering with Computers, 39, 2627-2651. https://doi.org/10.1007/s00366-022-01604-x10.1007/s00366-022-01604-x ↗
8. Zhong, C., Li, G., & Meng, Z. (2022). Beluga whale optimization: A novel nature-inspired metaheuristic algorithm. Knowledge-Based Systems,251, 109215. https://doi.org/10.1016/j.knosys.2022.10921510.1016/j.knosys.2022.109215 ↗
9. Azizi, M., Talatahari, S., & Gandomi, A. H. (2023). Fire Hawk Optimizer: A novel metaheuristic algorithm. Artificial Intelligence Review,56(1), 287–363. https://doi.org/10.1007/s10462-022-10173-w10.1007/s10462-022-10173-w ↗
10. Tian, A. Q., Liu, F. F., & Lv, H. X. (2024). Snow Geese Algorithm: A novel migration-inspired meta-heuristic algorithm for constrained engineering optimization problems. Applied Mathematical Modelling,126, 327–347. https://doi.org/10.1016/j.apm.2023.10.04510.1016/j.apm.2023.10.045 ↗
11. Wang, Y., Zheng, H., Wu, Q., Yang, S., & Zhou, Y. (2025). White-faced capuchin optimizer: A new bionic metaheuristic algorithm for solving optimization problems. The Journal of Supercomputing,81(1), 1–64. https://doi.org/10.1007/s11227-024-06713-610.1007/s11227-024-06713-6 ↗
12. 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 ↗
13. Mirjalili, S., Mirjalili, S. M., & Hatamlou, A. (2016). Multi-verse optimizer: A nature-inspired algorithm for global optimization. Neural Computing and Applications,27, 495–513. https://doi.org/10.1007/s00521-015-1870-710.1007/s00521-015-1870-7 ↗
14. 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 ↗
15. Abdel-Basset, M., Mohamed, R., Azeem, S. A. A., Jameel, M., & Abouhawwash, M. (2023). Kepler optimization algorithm: A new metaheuristic algorithm inspired by Kepler’s laws of planetary motion. Knowledge-Based Systems,268, 110454. https://doi.org/10.1016/j.knosys.2023.11045410.1016/j.knosys.2023.110454 ↗
16. Liu, P., Hu, Q., Jin, K., Yu, G., & Tang, Z. (2021). Toward the energy-saving optimization of WLAN deployment in real 3-D environment: A hybrid swarm intelligent method. IEEE Systems Journal,16(2), 2425–2436. https://doi.org/10.1109/JSYST.2021.306543410.1109/JSYST.2021.3065434 ↗
17. Wang, R., Hao, K., Chen, L., Liu, X., Zhu, X., & Zhao, C. (2024). A modified hybrid particle swarm optimization based on comprehensive learning and dynamic multi-swarm strategy. Soft Computing,28(5), 3879–3903. https://doi.org/10.1007/s00500-023-09332-010.1007/s00500-023-09332-0 ↗
18. Li, Q., Ning, H., & Gong, J. (2023). An improved sine cosine algorithm with heterogeneous subpopulations for global optimization and fractional order PID controller design. Applied Intelligence,53(15), 18581–18604. https://doi.org/10.1007/s10489-023-04473-z10.1007/s10489-023-04473-z ↗
19. Sun, Y., & Chen, Y. (2021). Multi-population improved whale optimization algorithm for high dimensional optimization. Applied Soft Computing,112, 107854. https://doi.org/10.1016/j.asoc.2021.10785410.1016/j.asoc.2021.107854 ↗
20. Tian, A. Q., Wang, X. Y., Xu, H., Pan, J. S., Snášel, V., & Lv, H. X. (2024). Multi-objective optimization model for railway heavy-haul traffic: Addressing carbon emissions reduction and transport efficiency improvement. Energy,294, 130927. https://doi.org/10.1016/j.energy.2024.13092710.1016/j.energy.2024.130927 ↗
21. 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 ↗
22. Luo, J., Chen, H., Xu, Y., Huang, H., & Zhao, X. (2018). An improved grasshopper optimization algorithm with application to financial stress prediction. Applied Mathematical Modelling,64, 654–668. https://doi.org/10.1016/j.apm.2018.07.04410.1016/j.apm.2018.07.044 ↗
23. Hu, J., Li, Z. H., Ou, P., & Luo, S. D. (2009). Stepwise strategies in particle swarm optimization. Journal of University of Electronic Science and Technology of Chain,38(3), 435–439. https://doi.org/10.3969/j.issn.1001-0548.2009.03.02810.3969/j.issn.1001-0548.2009.03.028 ↗
24. Suganthan, P. N., Hansen, N., Liang, J. J., Deb, K., Chen, Y. P., Auger, A., & Tiwari, S. (2005). Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. KanGAL Report,2005005(2005), 2005.
25. Wu, G., Mallipeddi, R., & Suganthan, P. N. (2017). Problem definitions and evaluation criteria for the CEC 2017 competition on constrained real-parameter optimization. National University of Defense Technology, Changsha, Hunan, PR China and Kyungpook National University, Daegu, South Korea and Nanyang Technological University, Singapore, Technical Report.
26. Price, K.,V., Awad, N.,H., Ali, M., Z., & Suganthan, P., N. (2018). Problem definitions and evaluation criteria for the 100-digit challenge special session and competition on single objective numerical optimization. Technical report. Nanyang Technological University Singapore.
27. Brockmann, D., Hufnagel, L., & Geisel, T. (2006). The scaling laws of human travel. Nature,439(7075), 462–465. https://doi.org/10.1038/nature0429210.1038/nature04292 ↗
28. Kumar, N., Hussain, I., Singh, B., & Panigrahi, B. K. (2017). Single sensor-based MPPT of partially shaded PV system for battery charging by using Cauchy and Gaussian sine cosine optimization. IEEE Transactions on Energy Conversion,32(3), 983–992. https://doi.org/10.1109/TEC.2017.266951810.1109/TEC.2017.2669518 ↗
29. Gupta, S. (2022). Enhanced sine cosine algorithm with crossover: A comparative study and empirical analysis. Expert Systems with Applications,198, 116856. https://doi.org/10.1016/j.eswa.2022.11685610.1016/j.eswa.2022.116856 ↗
30. Gupta, S., & Deep, K. (2019). A hybrid self-adaptive sine cosine algorithm with opposition based learning. Expert Systems with Applications,119, 210–230. https://doi.org/10.1016/j.eswa.2018.10.05010.1016/j.eswa.2018.10.050 ↗
31. Yang, W., Xia, K., Fan, S., Wang, L., Li, T., Zhang, J., & Feng, Y. (2022). A multi-strategy Whale optimization algorithm and its application. Engineering Applications of Artificial Intelligence,108, 104558. https://doi.org/10.1016/j.engappai.2021.10455810.1016/j.engappai.2021.104558 ↗
32. Anitha, J., Pandian, S. I. A., & Agnes, S. A. (2021). An efficient multilevel color image thresholding based on modified whale optimization algorithm. Expert Systems with Applications,178, 115003. https://doi.org/10.1016/j.eswa.2021.11500310.1016/j.eswa.2021.115003 ↗
33. Liu, M., Yao, X., & Li, Y. (2020). Hybrid whale optimization algorithm enhanced with Lévy flight and differential evolution for job shop scheduling problems. Applied Soft Computing,87, 105954. https://doi.org/10.1016/j.asoc.2019.10595410.1016/j.asoc.2019.105954 ↗
34. García, S., Fernández, A., Luengo, J., & Herrera, F. (2010). Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Information Sciences,180(10), 2044–2064. https://doi.org/10.1016/j.ins.2009.12.01010.1016/j.ins.2009.12.010 ↗
35. Han, B., Gopalakrishnan, V., Ji, L., & Lee, S. (2015). Network function virtualization: Challenges and opportunities for innovations. IEEE Communications Magazine,53(2), 90–97. https://doi.org/10.1109/MCOM.2015.704539610.1109/MCOM.2015.7045396 ↗
36. Tajiki, M. M., Salsano, S., Chiaraviglio, L., Shojafar, M., & Akbari, B. (2018). Joint energy efficient and QoS-aware path allocation and VNF placement for service function chaining. IEEE Transactions on Network and Service Management,16(1), 374–388. https://doi.org/10.1109/TNSM.2018.287322510.1109/TNSM.2018.2873225 ↗
37. Li, D., Hong, P., Xue, K., & Pei, J. (2019). Availability aware VNF deployment in datacenter through shared redundancy and multi-tenancy. IEEE Transactions on Network and Service Management,16(4), 1651–1664. https://doi.org/10.1109/TNSM.2019.293650510.1109/TNSM.2019.2936505 ↗
38. Orlowski, S., Wessäly, R., Pióro, M., & Tomaszewski, A. (2010). SNDlib 1.0-Survivable network design library. Networks: An International Journal,55(3), 276–286. https://doi.org/10.1002/net.2037110.1002/net.20371 ↗