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A Methodology to Improving the Performance of MOAHA Optimization Algorithm using Chaos Theory; Principle and Application in Optimal Reservoir Operation

Author

Listed:
  • Zahra khoramipoor

    (Semnan University)

  • Saeed Farzin

    (Semnan University)

Abstract

This study introduces a novel algorithm, CMOAHA, which integrates the multi-objective hummingbird optimizer with chaos theory to optimize multi-reservoir system management. The algorithm aims to maximize hydropower energy production and minimize evaporation losses. The performance of CMOAHA is compared to that of MOGWO, MOALO, and NSGA-II algorithms. Using evaluation criteria such as MS, CV, and MID, CMOAHA demonstrates superior efficiency, achieving values of CV = 4,834,269.468, MS = 19,359,123.542, and MID = 6,895,142.911. In contrast, the gray wolf optimizer (MOGWO) shows the lowest performance, with CV = 17,602,966.401 and MID = 17,429,422.893. Rankings confirm that the improved hummingbird algorithm achieves the highest efficiency, with a rating of 0.95, while NSGA-II ranks lowest. Moreover, the output of the CMOAHA algorithm closely aligns with results from LINGO software, achieving a 96.73% match to the global optimum. These findings highlight the enhanced performance and strong capabilities of the hummingbird algorithm when augmented with chaos theory. The results showed that the multi-objective artificial hummingbird algorithm enhanced by chaos theory provides better accuracy and certainty compared to other algorithms. Graphical Abstract

Suggested Citation

  • Zahra khoramipoor & Saeed Farzin, 2025. "A Methodology to Improving the Performance of MOAHA Optimization Algorithm using Chaos Theory; Principle and Application in Optimal Reservoir Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(6), pages 2819-2840, April.
    • Handle: RePEc:spr:waterr:v:39:y:2025:i:6:d:10.1007_s11269-025-04092-y
    DOI: 10.1007/s11269-025-04092-y
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    References listed on IDEAS

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    1. Mohammad Ehteram & Hojat Karami & Saeed Farzin, 2018. "Reservoir Optimization for Energy Production Using a New Evolutionary Algorithm Based on Multi-Criteria Decision-Making Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(7), pages 2539-2560, May.
    2. Mahdi Valikhan Anaraki & Saeed Farzin & Sayed-Farhad Mousavi & Hojat Karami, 2021. "Uncertainty Analysis of Climate Change Impacts on Flood Frequency by Using Hybrid Machine Learning Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 199-223, January.
    3. Mahta Nazari & Reza Kerachian, 2024. "Optimal Operation of Reservoirs Considering Water Quantity and Quality Aspects: A Systematic State-of-the-Art Review," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(15), pages 5911-5944, December.
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