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Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Reservoir Operation Management

Author

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  • Saad Dahmani

    (Laboratoire de Gestion et Traitement des Eaux (LGTE), Université des Sciences et de la Technologie d’Oran Mohamed Boudiaf (USTO-MB))

  • Djilali Yebdri

    (Laboratoire de Gestion et Traitement des Eaux (LGTE), Université des Sciences et de la Technologie d’Oran Mohamed Boudiaf (USTO-MB))

Abstract

Metaheuristics are highly efficient optimization methods that are widely used today. However, the performance of one method cannot be generalized and must be examined in each class of problems. The hybrid algorithm of particle swarm optimization and grey wolf optimizer (HPSOGWO) is new swarm-based metaheuristic with several advantages, such as simple implementation and low memory consumption. This study uses HPSOGWO for reservoir operation optimization. Real-coded genetic algorithm (RGA) and gravitational search algorithm (GSA) have been used as efficient methods in reservoir optimization management for comparative analysis between algorithms through two case studies. In the first case study, four benchmark functions were minimized, in which results revealed that HPSOGWO was more competitive compared with other algorithms and can produce high-quality solutions. The second case study involved minimizing the deficit between downstream demand and release from the Hammam Boughrara reservoir located in Northwest Algeria. A constrained optimization model with non-linear objective function was applied. Based on the average solutions, HPSOGWO performed better compared with RGA and was highly competitive with GSA. In addition, the reliability, resiliency, and vulnerability indices of the reservoir operation, which was derived from the three algorithms, were nearly similar to one another, which justified the usability of HPSOGWO in this field.

Suggested Citation

  • Saad Dahmani & Djilali Yebdri, 2020. "Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Reservoir Operation Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 4545-4560, December.
    • Handle: RePEc:spr:waterr:v:34:y:2020:i:15:d:10.1007_s11269-020-02656-8
    DOI: 10.1007/s11269-020-02656-8
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    References listed on IDEAS

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    1. Narinder Singh & S. B. Singh, 2017. "Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance," Journal of Applied Mathematics, Hindawi, vol. 2017, pages 1-15, November.
    2. Juran Ahmed & Arup Sarma, 2005. "Genetic Algorithm for Optimal Operating Policy of a Multipurpose Reservoir," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 19(2), pages 145-161, April.
    3. Editors, 2014. "International Journal of Systems Science," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(12), pages 1-1, December.
    4. Fi-John Chang & Li Chen, 1998. "Real-Coded Genetic Algorithm for Rule-Based Flood Control Reservoir Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 12(3), pages 185-198, June.
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    Cited by:

    1. Radhikesh Kumar & Maheshwari Prasad Singh & Bishwajit Roy & Afzal Hussain Shahid, 2021. "A Comparative Assessment of Metaheuristic Optimized Extreme Learning Machine and Deep Neural Network in Multi-Step-Ahead Long-term Rainfall Prediction for All-Indian Regions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1927-1960, April.
    2. Wenzhuo Wang & Benyou Jia & Slobodan P. Simonovic & Shiqiang Wu & Ziwu Fan & Li Ren, 2021. "Comparison of Representative Heuristic Algorithms for Multi-Objective Reservoir Optimal Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 2741-2762, July.
    3. Abolfazl Baniasadi Moghadam & Hossein Ebrahimi & Abbas Khashei Siuki & Abolfazl Akbarpour, 2022. "Reliability-based Operation of Reservoirs Using Combined Monte Carlo Simulation Model and a Novel Nature-inspired Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4447-4468, September.
    4. Mohammad Reza Sharifi & Saeid Akbarifard & Kourosh Qaderi & Mohamad Reza Madadi, 2021. "Developing MSA Algorithm by New Fitness-Distance-Balance Selection Method to Optimize Cascade Hydropower Reservoirs Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 385-406, January.

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