Processing, Please wait...

  • Home
  • About Us
  • Search:
  • Advanced Search

Growing Science » International Journal of Industrial Engineering Computations » An enhanced dung beetle optimization algorithm based-on multi-strategies for solving global optimization problems

Journals

  • IJIEC (777)
  • MSL (2643)
  • DSL (690)
  • CCL (528)
  • USCM (1099)
  • ESM (421)
  • AC (562)
  • JPM (293)
  • IJDS (952)
  • JFS (101)
  • HE (37)
  • SCI (36)

IJIEC Volumes

    • Volume 1 (17)
      • Issue 1 (9)
      • Issue 2 (8)
    • Volume 2 (68)
      • Issue 1 (12)
      • Issue 2 (20)
      • Issue 3 (20)
      • Issue 4 (16)
    • Volume 3 (76)
      • Issue 1 (9)
      • Issue 2 (15)
      • Issue 3 (20)
      • Issue 4 (12)
      • Issue 5 (20)
    • Volume 4 (50)
      • Issue 1 (14)
      • Issue 2 (10)
      • Issue 3 (12)
      • Issue 4 (14)
    • Volume 5 (47)
      • Issue 1 (13)
      • Issue 2 (12)
      • Issue 3 (12)
      • Issue 4 (10)
    • Volume 6 (39)
      • Issue 1 (7)
      • Issue 2 (12)
      • Issue 3 (10)
      • Issue 4 (10)
    • Volume 7 (47)
      • Issue 1 (10)
      • Issue 2 (14)
      • Issue 3 (10)
      • Issue 4 (13)
    • Volume 8 (30)
      • Issue 1 (9)
      • Issue 2 (7)
      • Issue 3 (8)
      • Issue 4 (6)
    • Volume 9 (32)
      • Issue 1 (9)
      • Issue 2 (6)
      • Issue 3 (7)
      • Issue 4 (10)
    • Volume 10 (34)
      • Issue 1 (8)
      • Issue 2 (10)
      • Issue 3 (8)
      • Issue 4 (8)
    • Volume 11 (36)
      • Issue 1 (9)
      • Issue 2 (8)
      • Issue 3 (9)
      • Issue 4 (10)
    • Volume 12 (29)
      • Issue 1 (9)
      • Issue 2 (6)
      • Issue 3 (8)
      • Issue 4 (6)
    • Volume 13 (41)
      • Issue 1 (10)
      • Issue 2 (8)
      • Issue 3 (10)
      • Issue 4 (13)
    • Volume 14 (50)
      • Issue 1 (11)
      • Issue 2 (15)
      • Issue 3 (9)
      • Issue 4 (15)
    • Volume 15 (55)
      • Issue 1 (19)
      • Issue 2 (15)
      • Issue 3 (12)
      • Issue 4 (9)
    • Volume 16 (75)
      • Issue 1 (12)
      • Issue 2 (15)
      • Issue 3 (19)
      • Issue 4 (29)
    • Volume 17 (51)
      • Issue 1 (21)
      • Issue 2 (30)

Keywords

Supply chain management(168)
Jordan(165)
Vietnam(151)
Customer satisfaction(120)
Performance(115)
Supply chain(112)
Service quality(98)
Competitive advantage(97)
Tehran Stock Exchange(94)
SMEs(89)
optimization(87)
Sustainability(87)
Artificial intelligence(86)
Financial performance(84)
Trust(83)
TOPSIS(83)
Job satisfaction(81)
Knowledge Management(79)
Social media(78)
Factor analysis(78)


» Show all keywords

Authors

Naser Azad(82)
Zeplin Jiwa Husada Tarigan(66)
Mohammad Reza Iravani(64)
Endri Endri(45)
Muhammad Alshurideh(42)
Hotlan Siagian(40)
Dmaithan Almajali(37)
Jumadil Saputra(36)
Muhammad Turki Alshurideh(35)
Ahmad Makui(33)
Barween Al Kurdi(32)
Hassan Ghodrati(31)
Basrowi Basrowi(31)
Sautma Ronni Basana(31)
Mohammad Khodaei Valahzaghard(30)
Shankar Chakraborty(29)
Ni Nyoman Kerti Yasa(29)
Haitham M. Alzoubi(28)
Sulieman Ibraheem Shelash Al-Hawary(28)
Prasadja Ricardianto(28)


» Show all authors

Countries

Iran(2198)
Indonesia(1311)
Jordan(815)
India(798)
Vietnam(510)
Saudi Arabia(478)
Malaysia(446)
China(231)
United Arab Emirates(226)
Thailand(160)
United States(115)
Turkey(112)
Ukraine(110)
Egypt(106)
Peru(94)
Canada(93)
Morocco(86)
Pakistan(85)
United Kingdom(80)
Nigeria(78)


» Show all countries

International Journal of Industrial Engineering Computations

ISSN 1923-2934 (Online) - ISSN 1923-2926 (Print)
Quarterly Publication
Volume 16 Issue 4 pp. 1289-1306 , 2025

An enhanced dung beetle optimization algorithm based-on multi-strategies for solving global optimization problems Pages 1289-1306 Right click to download the paper Download PDF

Authors: Xinyu Liu, Lili Liu, Leilei Meng, Biao Zhang, Yuyan Han

DOI: 10.5267/j.ijiec.2025.6.001

Keywords: Optimization Algorithm, Dung Beetle Optimizer, Dynamic Opposition-Based Learning, Wave Search Algorithm, Benchmark functions

Abstract: The Dung Beetle Optimization (DBO) algorithm exhibits rapid convergence and robust search capabilities, yet its performance is constrained by excessive reliance on global best and worst solutions. To resolve these weaknesses, this paper introduces an enhanced DBO that incorporates multiple strategies, named DCWDBO. The dynamic opposition-based learning mechanism improves the quality of the initial population. Horizontal and vertical crossover strategies are incorporated to strengthen search capabilities. To preserve high population diversity throughout iterations, the original boundary-control mechanism is replaced with rules from the Wave Search Algorithm. To evaluate DCWDBO’s effectiveness, it was compared with PSO, SCA, SCSO, and standard DBO using benchmark functions from CEC 2017, 2020, and 2022. Results indicate that DCWDBO achieves reliable performance, demonstrating robust global exploration, stable convergence, and superior large-scale optimization capability.

How to cite this paper
Liu, X., Liu, L., Meng, L., Zhang, B & Han, Y. (2025). An enhanced dung beetle optimization algorithm based-on multi-strategies for solving global optimization problems.International Journal of Industrial Engineering Computations , 16(4), 1289-1306.

Refrences
Abu-Hashem, M. A., Shehab, M., Shambour, M. K. Y., Daoud, M. S., & Abualigah, L. (2024). Improved Black Widow Optimization: An investigation into enhancing cloud task scheduling efficiency. Sustainable Computing-Informatics & Systems, 41, 100949. https://doi.org/10.1016/j.suscom.2023.100949 Ahrari, A., Elsayed, S., Sarker, R., Essam, D., & Coello, C. A. C. (2022, January). Problem definition and evaluation criteria for the CEC’2022 competition on dynamic multimodal optimization. In Proceedings of the IEEE World Congress on Computational Intelligence (IEEE WCCI 2022), Padua, Italy (pp. 18-23). Daoud, M. S., Shehab, M., Al-Mimi, H. M., Abualigah, L., Zitar, R. A., & Shambour, M. K. Y. (2023). Gradient-Based Optimizer (GBO): A Review, Theory, Variants, and Applications. Archives of Computational Methods in Engineering, 30(4), 2431-2449. https://doi.org/10.1007/s11831-022-09872-y Eusuff, M., Lansey, K., & Pasha, F. (2006). Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Engineering Optimization, 38(2), 129-154. Holland, J. H. (1975). Adaptation in natural and artificial systems. an introductory analysis with applications to biology, control and artificial intelligence. Ann Arbor: University of Michigan Press. Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks (Vol. 4, pp. 1942-1948). ieee. Liu, X. Y., Li, G. Q., Yang, H. Y., Zhang, N. R., Wang, L. F., & Shao, P. (2023). Agricultural UAV trajectory planning by incorporating multi-mechanism improved grey wolf optimization algorithm. Expert Systems with Applications, 233, 120946. https://doi.org/10.1016/j.eswa.2023.120946 Meng, A., Chen, Y., Yin, H., & Chen, S. (2014). Crisscross optimization algorithm and its application. Knowledge-Based Systems, 67, 218-229. https://doi.org/10.1016/j.knosys.2014.05.004 Meng, L., Duan, P., Gao, K., Zhang, B., Zou, W., Han, Y., & Zhang, C. (2024). MIP modeling of energy-conscious FJSP and its extended problems: From simplicity to complexity. Expert Systems with Applications, 241, 122594. Meng, L., Zhang, C., Zhang, B., Gao, K., Ren, Y., & Sang, H. (2023). MILP modeling and optimization of multi-objective flexible job shop scheduling problem with controllable processing times. Swarm and Evolutionary Computation, 82, 101374. 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.2015.12.022 Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51-67. 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. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46-61. Pan, J., Li, S., Zhou, P., Yang, G., & Lyu, D. (2023). Dung Beetle Optimization Algorithm Guided by Improved Sine Algorithm. Computer Engineering and Application, 59(22), 92-110. Rodríguez-Molina, A., Herroz-Herrera, A., Aldape-Pérez, M., Flores-Caballero, G., & Antón-Vargas, J. A. (2022). Dynamic Path Planning for the Differential Drive Mobile Robot Based on Online Metaheuristic Optimization. Mathematics, 10(21), 3990. https://doi.org/10.3390/math10213990 Sangeetha, S., Kanagaraj, K., Prasath, N., & Saradha, S. (2024). Multiobjective Gannet Dung Beetle Optimization for routing in IoT-WSN. Peer-To-Peer Networking and Applications, 17(6), 4357-4377. https://doi.org/10.1007/s12083-024-01790-z Seyyedabbasi, A., & Kiani, F. (2023). Sand Cat swarm optimization: a nature-inspired algorithm to solve global optimization problems. Engineering with Computers, 39(4), 2627-2651. https://doi.org/10.1007/s00366-022-01604-x Shehab, M., Abualigah, L., Al Hamad, H., Alabool, H., Alshinwan, M., & Khasawneh, A. M. (2020). Moth-flame optimization algorithm: variants and applications. Neural Computing & Applications, 32(14), 9859-9884. https://doi.org/10.1007/s00521-019-04570-6 Tizhoosh, H. R. (2005). Opposition-based learning: a new scheme for machine intelligence (Vol. Proceedings. 2006 International Conference on Intelligence For Modelling, Control and Automation. Jointly with International Conference on Intelligent Agents, Web Technologies and Internet Commerce). Tu, N. W., & Fan, Z. H. (2023). IMODBO for Optimal Dynamic Reconfiguration in Active Distribution Networks. Processes, 11(6), 1827. https://doi.org/10.3390/pr11061827 Wang, Z., Huang, L., Yang, S., Li, D., He, D., & Chan, S. (2023). A quasi-oppositional learning of updating quantum state and Q-learning based on the dung beetle algorithm for global optimization. Alexandria Engineering Journal, 81, 469-488. https://doi.org/10.1016/j.aej.2023.09.042 Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67-82. Wu, G., Mallipeddi, R., & Suganthan, P. (2016). Problem definitions and evaluation criteria for the CEC 2017 competition and special session on constrained single objective real-parameter optimization. Nanyang Technol. Univ., Singapore, Tech. Rep, 1-18. Xu, H., Lü, Z., Yin, A., Shen, L., & Buscher, U. (2014). A study of hybrid evolutionary algorithms for single machine scheduling problem with sequence-dependent setup times. Computers & Operations Research, 50, 47-60. https://doi.org/10.1016/j.cor.2014.04.009 Xu, Y., Yang, Z., Li, X., Kang, H., & Yang, X. (2020). Dynamic opposite learning enhanced teaching-learning-based optimization. Knowledge-Based Systems, 188, 104966. https://doi.org/10.1016/j.knosys.2019.104966 Xue, J. K., & Shen, B. (2022). Dung beetle optimizer: a new meta-heuristic algorithm for global optimization. Journal of Supercomputing, 79(7), 7305-7336. https://doi.org/10.1007/s11227-022-04959-6 Ye, M., Zhou, H., Yang, H., Hu, B., & Wang, X. (2024). Multi-Strategy Improved Dung Beetle Optimization Algorithm and Its Applications. Biomimetics, 9(5), Article 291. https://doi.org/10.3390/biomimetics9050291 Yue, C., Price, K. V., Suganthan, P. N., Liang, J., Ali, M. Z., Qu, B., . . . Biswas, P. P. (2019). Problem definitions and evaluation criteria for the CEC 2020 special session and competition on single objective bound constrained numerical optimization. Comput. Intell. Lab., Zhengzhou Univ., Zhengzhou, China, Tech. Rep, 201911. Zhang, H., San, H., Sun, H., Ding, L., & Wu, X. (2024). A novel optimization method: wave search algorithm. Journal of Supercomputing, 80(12), 16824-16859. https://doi.org/10.1007/s11227-024-06078-w Zhu, F., Li, G., Tang, H., Li, Y., Lv, X., & Wang, X. (2024). Dung beetle optimization algorithm based on quantum computing and multi-strategy fusion for solving engineering problems. Expert Systems with Applications, 236, 121219. https://doi.org/https://doi.org/10.1016/j.eswa.2023.121219 Zhu, X., Ni, C., Chen, G. L., & Guo, J. (2023). Optimization of Tungsten Heavy Alloy Cutting Parameters Based on RSM and Reinforcement Dung Beetle Algorithm. Sensors, 23(12), 5616. https://doi.org/10.3390/s23125616
  • 0
  • 1
  • 2
  • 3
  • 4
  • 5

Journal: International Journal of Industrial Engineering Computations | Year: 2025 | Volume: 16 | Issue: 4 | Views: 930 | Reviews: 0

Related Articles:
  • A novel hybrid algorithm of cooperative variable neighborhood search and co ...
  • An improved black widow optimization (IBWO) algorithm for solving global op ...
  • Ions motion optimization algorithm for multiobjective optimization problems
  • A new non-dominated sorting ions motion algorithm: Development and applicat ...
  • Comments on “A note on multi-objective improved teaching-learning based opt ...

Add Reviews

Name:*
E-Mail:
Review:
Bold Italic Underline Strike | Align left Center Align right | Insert smilies Insert link URLInsert protected URL Select color | Add Hidden Text Insert Quote Convert selected text from selection to Cyrillic (Russian) alphabet Insert spoiler
winkwinkedsmileam
belayfeelfellowlaughing
lollovenorecourse
requestsadtonguewassat
cryingwhatbullyangry
Security Code: *
Include security image CAPCHA.
Refresh Code

® 2010-2026 GrowingScience.Com