Processing, Please wait...

  • Home
  • About Us
  • Search:
  • Advanced Search

Growing Science » Decision Science Letters » An improved pelican optimization algorithm for function optimization and constrained engineering design problems

Journals

  • IJIEC (777)
  • MSL (2643)
  • DSL (690)
  • CCL (528)
  • USCM (1092)
  • ESM (421)
  • AC (562)
  • JPM (293)
  • IJDS (952)
  • JFS (96)
  • HE (32)
  • SCI (26)

DSL Volumes

    • Volume 1 (10)
      • Issue 1 (5)
      • Issue 2 (5)
    • Volume 2 (30)
      • Issue 1 (5)
      • Issue 2 (6)
      • Issue 3 (9)
      • Issue 4 (10)
    • Volume 3 (53)
      • Issue 1 (15)
      • Issue 2 (10)
      • Issue 3 (19)
      • Issue 4 (9)
    • Volume 4 (48)
      • Issue 1 (10)
      • Issue 2 (12)
      • Issue 3 (14)
      • Issue 4 (12)
    • Volume 5 (39)
      • Issue 1 (12)
      • Issue 2 (10)
      • Issue 3 (8)
      • Issue 4 (9)
    • Volume 6 (30)
      • Issue 1 (8)
      • Issue 2 (6)
      • Issue 3 (9)
      • Issue 4 (7)
    • Volume 7 (41)
      • Issue 1 (8)
      • Issue 2 (8)
      • Issue 3 (8)
      • Issue 4 (17)
    • Volume 8 (38)
      • Issue 1 (8)
      • Issue 2 (6)
      • Issue 3 (14)
      • Issue 4 (10)
    • Volume 9 (39)
      • Issue 1 (8)
      • Issue 2 (9)
      • Issue 3 (14)
      • Issue 4 (8)
    • Volume 10 (43)
      • Issue 1 (7)
      • Issue 2 (8)
      • Issue 3 (20)
      • Issue 4 (8)
    • Volume 11 (49)
      • Issue 1 (9)
      • Issue 2 (9)
      • Issue 3 (14)
      • Issue 4 (17)
    • Volume 12 (64)
      • Issue 1 (12)
      • Issue 2 (24)
      • Issue 3 (13)
      • Issue 4 (15)
    • Volume 13 (78)
      • Issue 1 (21)
      • Issue 2 (18)
      • Issue 3 (19)
      • Issue 4 (20)
    • Volume 14 (87)
      • Issue 1 (21)
      • Issue 2 (23)
      • Issue 3 (25)
      • Issue 4 (18)
    • Volume 15 (41)
      • Issue 1 (19)
      • Issue 2 (22)

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)
Artificial intelligence(85)
Financial performance(84)
Sustainability(84)
Trust(83)
TOPSIS(83)
Job satisfaction(81)
Factor analysis(78)
Genetic Algorithm(78)
Social media(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)
Sautma Ronni Basana(31)
Basrowi Basrowi(31)
Hassan Ghodrati(31)
Mohammad Khodaei Valahzaghard(30)
Shankar Chakraborty(29)
Ni Nyoman Kerti Yasa(29)
Sulieman Ibraheem Shelash Al-Hawary(28)
Prasadja Ricardianto(28)
Haitham M. Alzoubi(28)


» Show all authors

Countries

Iran(2190)
Indonesia(1311)
Jordan(813)
India(793)
Vietnam(510)
Saudi Arabia(477)
Malaysia(444)
China(231)
United Arab Emirates(226)
Thailand(160)
United States(114)
Ukraine(110)
Turkey(110)
Egypt(105)
Peru(94)
Canada(92)
Morocco(86)
Pakistan(85)
United Kingdom(80)
Nigeria(78)


» Show all countries

Decision Science Letters

ISSN 1929-5812 (Online) - ISSN 1929-5804 (Print)
Quarterly Publication
Volume 14 Issue 3 pp. 623-640 , 2025

An improved pelican optimization algorithm for function optimization and constrained engineering design problems Pages 623-640 Right click to download the paper Download PDF

Authors: Haval Tariq Sadeeq, Araz Abrahim, Thamer Hameed, Najdavan Kako, Reber Mohammed, Dindar Ahmed

DOI: 10.5267/j.dsl.2025.4.004

Keywords: Metaheuristic algorithms, Engineering optimization, Constrained design problems, Pelican optimization algorithm, Improved pelican optimization algorithm

Abstract: Metaheuristic algorithms are a class of optimization techniques that have revolutionized problem-solving across various domains. These algorithms provide a versatile and powerful approach to finding near-optimal solutions for complex, combinatorial, and computationally intensive problems. They draw inspiration from natural processes, such as evolution, swarm behavior, or annealing, to iteratively refine solutions by intelligently navigating the problem space. Metaheuristics have become indispensable tools in both academia and industry, helping researchers and practitioners address real-world problems efficiently and effectively. The Pelican optimization algorithm (POA) is a recently developed metaheuristic algorithm that simulates the hunting behavior of pelicans. In complex optimization problems, an POA may have slow convergence or fall in sub-optimal regions, especially in high complex ones. In this paper, Levy flight is integrated into the exploration phase to enhance its search capabilities. Furthermore, a novel exponential parameter has been introduced to enhance the algorithm's overall performance by facilitating a smoother shift between exploration and exploitation phases. These modifications are intended to keep the algorithm from being locked in local optima. The developed algorithm named as IPOA was tested using widely recognized twenty-three benchmark functions with a variety of characteristics, a set of CEC2022 test suites, and five different engineering constrained problems. The results demonstrate the superiority and effectiveness of IPOA in tackling function optimization and constrained design engineering problems.

How to cite this paper
Sadeeq, H., Abrahim, A., Hameed, T., Kako, N., Mohammed, R & Ahmed, D. (2025). An improved pelican optimization algorithm for function optimization and constrained engineering design problems.Decision Science Letters , 14(3), 623-640.

Refrences
Abu-Hashem, M., & Shambour, M. (2024). An improved black widow optimization (IBWO) algorithm for solving global optimization problems. International Journal of Industrial Engineering Computations, 15(3), 705–720. Ajagekar, A., Al Hamoud, K., & You, F. (2022). Hybrid Classical-Quantum Optimization Techniques for Solving Mixed-Integer Programming Problems in Production Scheduling. IEEE Transactions on Quantum Engineering, 3(March), 1–16. https://doi.org/10.1109/TQE.2022.3187367 Al-Betar, M. A., Awadallah, M. A., Braik, M. S., Makhadmeh, S., & Doush, I. A. (2024). Elk herd optimizer: a novel nature-inspired metaheuristic algorithm. In Artificial Intelligence Review (Vol. 57, Issue 3). Springer Netherlands. https://doi.org/10.1007/s10462-023-10680-4 Alamir, N., Kamel, S., Megahed, T. F., Hori, M., & Abdelkader, S. M. (2023). Developing Hybrid Demand Response Technique for Energy Management in Microgrid Based on Pelican Optimization Algorithm. Electric Power Systems Research, 214(PA), 108905. https://doi.org/10.1016/j.epsr.2022.108905 Alghamdi, A. S. (2024). Cost-Effective Planning of Hybrid Energy Systems Using Improved Horse Herd Optimizer and Cloud Theory under Uncertainty. In Electronics (Vol. 13, Issue 13). https://doi.org/10.3390/electronics13132471 Amine Tahiri, M., Zohra El hlouli, F., Bencherqui, A., Karmouni, H., Amakdouf, H., Sayyouri, M., & Qjidaa, H. (2023). White blood cell automatic classification using deep learning and optimized quaternion hybrid moments. Biomedical Signal Processing and Control, 86(PA), 105128. https://doi.org/10.1016/j.bspc.2023.105128 Braik, M., Hammouri, A., Atwan, J., Al-Betar, M. A., & Awadallah, M. A. (2022). White Shark Optimizer: A novel bio-inspired meta-heuristic algorithm for global optimization problems. Knowledge-Based Systems, 243, 108457. https://doi.org/10.1016/j.knosys.2022.108457 Chen, L., Zhao, B., & Ma, Y. (2023). FSSSA: A Fuzzy Squirrel Search Algorithm Based on Wide-Area Search for Numerical and Engineering Optimization Problems. Mathematics, 11(17), 3722. https://doi.org/10.3390/math11173722 Dao, T.-K., Ngo, T.-G., Pan, J.-S., Nguyen, T.-T.-T., & Nguyen, T.-T. (2024). Enhancing Path Planning Capabilities of Automated Guided Vehicles in Dynamic Environments: Multi-Objective PSO and Dynamic-Window Approach. Biomimetics, 9(1), 35. Emam, M. M., Houssein, E. H., & Ghoniem, R. M. (2023). A modified reptile search algorithm for global optimization and image segmentation: Case study brain MRI images. Computers in Biology and Medicine, 152(October 2022), 106404. https://doi.org/10.1016/j.compbiomed.2022.106404 Faramarzi, A., Heidarinejad, M., Mirjalili, S., & Gandomi, A. H. (2020). Marine Predators Algorithm: A nature-inspired metaheuristic. Expert Systems with Applications, 152, 113377. https://doi.org/10.1016/j.eswa.2020.113377 Gandomi, A. H., & Deb, K. (2020). Implicit constraints handling for efficient search of feasible solutions. Computer Methods in Applied Mechanics and Engineering, 363, 112917. https://doi.org/https://doi.org/10.1016/j.cma.2020.112917 Gao, C., Hu, Z., Xiong, Z., & Su, Q. (2020). Grey prediction evolution algorithm based on accelerated even grey model. IEEE Access, 8, 107941–107957. Hashish, M. S., Hasanien, H. M., Ullah, Z., Alkuhayli, A., & Badr, A. O. (2023). Giant Trevally Optimization Approach for Probabilistic Optimal Power Flow of Power Systems Including Renewable Energy Systems Uncertainty. Sustainability, 15(18), 13283. 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. Houssein, E. H., Oliva, D., Samee, N. A., Mahmoud, N. F., & Emam, M. M. (2023). Liver Cancer Algorithm: A novel bio-inspired optimizer. Computers in Biology and Medicine, 107389. Jiang, Y., Wu, Q., Zhu, S., & Zhang, L. (2022). Orca predation algorithm: A novel bio-inspired algorithm for global optimization problems. Expert Systems with Applications, 188(April 2021), 116026. https://doi.org/10.1016/j.eswa.2021.116026 Kaur, S., Awasthi, L. K., Sangal, A. L., & Dhiman, G. (2020). Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization. Engineering Applications of Artificial Intelligence, 90(December 2018), 103541. https://doi.org/10.1016/j.engappai.2020.103541 Kuang, X., Hou, J., Liu, X., Lin, C., Wang, Z., & Wang, T. (2024). Improved African Vulture Optimization Algorithm Based on Random Opposition-Based Learning Strategy. In Electronics (Vol. 13, Issue 16). https://doi.org/10.3390/electronics13163329 Kusuma, P. D., & Prasasti, A. L. (2022). Guided Pelican Algorithm. International Journal of Intelligent Engineering and Systems, 15(6), 179–190. https://doi.org/10.22266/ijies2022.1231.18 Latifi Amoghin, M., Abbaspour-Gilandeh, Y., Tahmasebi, M., Kaveh, M., El-Mesery, H. S., Szymanek, M., & Sprawka, M. (2024). VIS/NIR Spectroscopy as a Non-Destructive Method for Evaluation of Quality Parameters of Three Bell Pepper Varieties Based on Soft Computing Methods. In Applied Sciences (Vol. 14, Issue 23). https://doi.org/10.3390/app142310855 Le Digabel, S., & Wild, S. M. (2023). A taxonomy of constraints in black-box simulation-based optimization. Optimization and Engineering. https://doi.org/10.1007/s11081-023-09839-3 Li, J., An, Q., Lei, H., Deng, Q., & Wang, G. G. (2022). Survey of Lévy Flight-Based Metaheuristics for Optimization. Mathematics, 10(15). https://doi.org/10.3390/math10152785 Luo, W., Lin, X., Li, C., Yang, S., & Shi, Y. (2022). Benchmark functions for CEC 2022 competition on seeking multiple optima in dynamic environments. ArXiv Preprint ArXiv:2201.00523. Mataifa, H., Krishnamurthy, S., & Kriger, C. (2022). Volt/VAR Optimization: A Survey of Classical and Heuristic Optimization Methods. IEEE Access, 10, 13379–13399. https://doi.org/10.1109/ACCESS.2022.3146366 Mirjalili, S., & Lewis, A. (2016). The Whale Optimization Algorithm. Advances in Engineering Software, 95, 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008 Mirjalili, S., Mirjalili, S. M., & Hatamlou, A. (2016). Multi-Verse Optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), 495–513. https://doi.org/10.1007/s00521-015-1870-7 Mohammed, G. P., Alasmari, N., Alsolai, H., Alotaibi, S. S., Alotaibi, N., & Mohsen, H. (2022). Autonomous Short-Term Traffic Flow Prediction Using Pelican Optimization with Hybrid Deep Belief Network in Smart Cities. Applied Sciences (Switzerland), 12(21). https://doi.org/10.3390/app122110828 Parvathi, K. A., Kotaiah, N. C., & Rani, K. R. (2022). Pelican Optimization Algorithm for Optimal Demand Response in Islanded Active Distribution Network Considering Controllable Loads. International Journal of Intelligent Engineering and Systems, 15(6), 132–141. https://doi.org/10.22266/ijies2022.1231.14 Połap, D., & Woźniak, M. (2021). Red fox optimization algorithm. Expert Systems with Applications, 166(October 2020), 114107. https://doi.org/10.1016/j.eswa.2020.114107 Rabie, A. H., Mansour, N. A., & Saleh, A. I. (2023). Leopard seal optimization (LSO): A natural inspired meta-heuristic algorithm. Communications in Nonlinear Science and Numerical Simulation, 125, 107338. https://doi.org/10.1016/j.cnsns.2023.107338 Sadeeq, H. T., & Abdulazeez, A. M. (2022a). Giant Trevally Optimizer (GTO): A Novel Metaheuristic Algorithm for Global Optimization and Challenging Engineering Problems. IEEE Access, October, 121615–121640. https://doi.org/10.1109/ACCESS.2022.3223388 Sadeeq, H. T., & Abdulazeez, A. M. (2022b). Improved Northern Goshawk Optimization Algorithm for Global Optimization. 89–94. Sadeeq, H. T., & Abdulazeez, A. M. (2023a). Car side impact design optimization problem using giant trevally optimizer. Structures, 55(February), 39–45. https://doi.org/10.1016/j.istruc.2023.06.016 Sadeeq, H. T., & Abdulazeez, A. M. (2023b). Metaheuristics: A Review of Algorithms. International Journal of Online and Biomedical Engineering, 19(9), 142–164. https://doi.org/10.3991/ijoe.v19i09.39683 Saleem, S., & Gallagher, M. (2022). Using regression models for characterizing and comparing black box optimization problems. Swarm and Evolutionary Computation, 68(June 2021), 100981. https://doi.org/10.1016/j.swevo.2021.100981 Shehadeh, H. A. (2023). Chernobyl disaster optimizer (CDO): a novel meta-heuristic method for global optimization. Neural Computing and Applications, 35(15), 10733–10749. https://doi.org/10.1007/s00521-023-08261-1 Song, H.-M., Xing, C., Wang, J.-S., Wang, Y.-C., Liu, Y., Zhu, J.-H., & Hou, J.-N. (2023). Improved pelican optimization algorithm with chaotic interference factor and elementary mathematical function. Soft Computing, 27(15), 10607–10646. https://doi.org/10.1007/s00500-023-08205-w Tian, T., Liang, Z., Wei, Y., Luo, Q., & Zhou, Y. (2024). Hybrid Whale Optimization with a Firefly Algorithm for Function Optimization and Mobile Robot Path Planning. Biomimetics, 9(1), 39. Trojovský, P., & Dehghani, M. (2022). Pelican Optimization Algorithm: A Novel Nature-Inspired Algorithm for Engineering Applications. Sensors, 22(3). https://doi.org/10.3390/s22030855 Wan, Y., Zuo, T. Y., Chen, L., Tang, W. C., & Chen, J. (2020). Efficiency-Oriented Production Scheduling Scheme: An Ant Colony System Method. IEEE Access, 8, 19286–19296. https://doi.org/10.1109/ACCESS.2020.2968378 Wang, B., Jin, Q., Zhao, R., & Zhang, Y. (2023). A New Optimization Idea: Parallel Search-based Golden Jackal Algorithm. IEEE Access, 11(August), 1–1. https://doi.org/10.1109/access.2023.3312684 Wang, J., Wang, W. C., Chau, K. W., Qiu, L., Hu, X. X., Zang, H. F., & Xu, D. M. (2024). An Improved Golden Jackal Optimization Algorithm Based on Multi-strategy Mixing for Solving Engineering Optimization Problems. Journal of Bionic Engineering, 21(2), 1092–1115. https://doi.org/10.1007/s42235-023-00469-0 Wolpert, D., & Macready, W. (1997). No Free Lunch Theorems for Optimization. Evolutionary Computation, IEEE Transactions On, 1, 67–82. Wongvanich, N., Roongmuanpha, N., & Tangsrirat, W. (2023). Extended Exploration Grey Wolf Optimization, CFOA-Based Circuit Implementation of the sigr Function and its Applications in Finite-Time Terminal Sliding Mode Control. IEEE Access, 11, 88388–88402. https://doi.org/10.1109/ACCESS.2023.3305943 Yang, H., Yang, X., & Li, G. (2023). Dual feature extraction system for ship-radiated noise and its application extension. Ocean Engineering, 285(P2), 115352. https://doi.org/10.1016/j.oceaneng.2023.115352 Yu, Y., Yao, M., Huang, J., & Xiao, X. (2024). When Process Analysis Technology Meets Transfer Learning: A Model Transfer Strategy Between Different Spectrometers for Quantitative Analysis. IEEE Transactions on Instrumentation and Measurement, 73, 1–19. https://doi.org/10.1109/TIM.2024.3353273 Yuan, X., Karbasforoushha, M. A., Syah, R. B. Y., Khajehzadeh, M., Keawsawasvong, S., & Nehdi, M. L. (2023). An Effective Metaheuristic Approach for Building Energy Optimization Problems. Buildings, 13(1). https://doi.org/10.3390/buildings13010080 Zeidabadi, F. A., Dehghani, M., Trojovský, P., Hubálovský, Š., Leiva, V., & Dhiman, G. (2022). Archery Algorithm: A Novel Stochastic Optimization Algorithm for Solving Optimization Problems. Computers, Materials and Continua, 72(1), 399–416. https://doi.org/10.32604/cmc.2022.024736 Zhao, W., Wang, L., & Mirjalili, S. (2022). Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications. Computer Methods in Applied Mechanics and Engineering, 388, 114194. https://doi.org/10.1016/j.cma.2021.114194 Zhong, K., Xiao, F., & Gao, X. (2024). APFA: Ameliorated Pathfinder Algorithm for Engineering Applications. Journal of Bionic Engineering, 0123456789. https://doi.org/10.1007/s42235-024-00510-w Zhong, M., Wen, J., Ma, J., Cui, H., Zhang, Q., & Parizi, M. K. (2023). A hierarchical multi-leadership sine cosine algorithm to dissolving global optimization and data classification: The COVID-19 case study. Computers in Biology and Medicine, 164(June), 107212. https://doi.org/10.1016/j.compbiomed.2023.107212
  • 0
  • 1
  • 2
  • 3
  • 4
  • 5

Journal: Decision Science Letters | Year: 2025 | Volume: 14 | Issue: 3 | Views: 637 | Reviews: 0

Related Articles:
  • An improved black widow optimization (IBWO) algorithm for solving global op ...
  • Hybrid algorithm proposal for optimizing benchmarking problems: Salp swarm ...
  • A new hybrid algorithm based on MVO and SA for function optimization
  • Ions motion optimization algorithm for multiobjective optimization problems
  • A new non-dominated sorting ions motion algorithm: Development and applicat ...

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