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A Self-Adaptive Population-Based Hybrid Optimisation Technique for Multireservoir Benchmark Problems

Author

Listed:
  • K. B. Baladaniya

    (Sardar Vallabhbhai National Institute of Technology)

  • P. L. Patel

    (Sardar Vallabhbhai National Institute of Technology)

  • P. V. Timbadiya

    (Sardar Vallabhbhai National Institute of Technology)

Abstract

Owing to the random nature of inflows and demands, including environmental flows, reservoir operation and management in water resource engineering are challenging. The complexity of this problem increases for multireservoir systems, and efficient optimisation techniques are needed to satisfy all objective functions. This study presents a control-parameter-free self-adaptive population hybrid teacher, learner phase and Rao (Rao-1, Rao-2, Rao-3, and Rao-4) (SAPHTLR) algorithm. The population size in the proposed algorithm is adjusted dynamically according to the fitness value throughout the search process. The population is divided randomly into six subgroups (two subgroups for the T and L phases and the remaining four for Rao-1 to Rao-4). Each subgroup is randomly allocated to a unique perturbation equation that directs the solution method to explore various areas within the search space. The algorithm is tested on two well-known multireservoir optimisation benchmark problems (BPs) in continuous and discrete domains. The performance of the proposed algorithm is assessed through individual comparisons with the teaching–learning-based optimization (TLBO) and Rao-1 to Rao-4 algorithms using the TOPSIS method, and the results indicate that the SAPHTLR algorithm ranks first for both BPs. The effectiveness of the proposed algorithm is also assessed against different methods in the literature. The best fitness values obtained from SAPHTLR for the discrete-time four-reservoir benchmark problem (DFRBP) and continuous-time four-reservoir benchmark problem (CFRBP) are 401.46 and 308.57, respectively. The proposed algorithm is generic and can be applied in multiobjective optimisation for real-time water resource engineering problems.

Suggested Citation

  • K. B. Baladaniya & P. L. Patel & P. V. Timbadiya, 2025. "A Self-Adaptive Population-Based Hybrid Optimisation Technique for Multireservoir Benchmark Problems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(10), pages 5005-5024, August.
    • Handle: RePEc:spr:waterr:v:39:y:2025:i:10:d:10.1007_s11269-025-04186-7
    DOI: 10.1007/s11269-025-04186-7
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    References listed on IDEAS

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    1. Vijendra Kumar & S. M. Yadav, 2018. "Optimization of Reservoir Operation with a New Approach in Evolutionary Computation Using TLBO Algorithm and Jaya Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(13), pages 4375-4391, October.
    2. Xiaohui Shen & Yonggang Wu & Lingxi Li & Peng He & Tongxin Zhang, 2024. "A Novel Hybrid Algorithm Based on Beluga Whale Optimization and Harris Hawks Optimization for Optimizing Multi-Reservoir Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(12), pages 4883-4909, September.
    3. Priyanshu Jain & Ruchi Khare, 2024. "Strategic Placement of In-line Turbines for Optimum Power Generation and Leakage Reduction in Water Supply Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(10), pages 3623-3638, August.
    4. Majid Mohammadi & Saeed Farzin & Sayed-Farhad Mousavi & Hojat Karami, 2019. "Investigation of a New Hybrid Optimization Algorithm Performance in the Optimal Operation of Multi-Reservoir Benchmark Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(14), pages 4767-4782, November.
    5. Seyed-Mohammad Hosseini-Moghari & Reza Morovati & Mohammad Moghadas & Shahab Araghinejad, 2015. "Optimum Operation of Reservoir Using Two Evolutionary Algorithms: Imperialist Competitive Algorithm (ICA) and Cuckoo Optimization Algorithm (COA)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3749-3769, August.
    6. Abbas Moghani & Hojat Karami, 2024. "The Implementation of a New Optimization Method for Hydropower Generation and Multi-Reservoir Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(5), pages 1711-1735, March.
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