Intelligent Scheduling Method for Cascade Reservoirs Driven by Dual Optimization of Harris Hawks and Marine Predators

Cascade reservoir optimization faces significant challenges due to multi-dimensional, non-convex, and nonlinear characteristics with coupled constraints. As reservoir numbers increase, computational complexity escalates dramatically, limiting conventional optimization methods’ effectiveness. This pa...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Water (Basel) Jg. 17; H. 22; S. 3291
Hauptverfasser: Chen, Xiaolin, Qin, Hui, Liu, Shuai, Chen, Jiawen, Li, Yongxiang, Zhu, Xin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 18.11.2025
Schlagworte:
Accuracy
Algorithms
Analysis
Behavior
Efficiency
Exploitation
Hydroelectric power
Linear programming
Management
Mathematical programming
Methods
Optimization algorithms
Power
Random variables
Scheduling
Water
Massachusetts
China
Jinsha River
ISSN:2073-4441, 2073-4441
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Cascade reservoir optimization faces significant challenges due to multi-dimensional, non-convex, and nonlinear characteristics with coupled constraints. As reservoir numbers increase, computational complexity escalates dramatically, limiting conventional optimization methods’ effectiveness. This paper proposes HHONMPA, a hybrid algorithm combining Harris Hawks Optimization (HHO) with Marine Predators Algorithm (MPA). The method uses SPM chaotic mapping for population initialization to enhance diversity and integrates both algorithms’ predatory behaviors. During exploration, it employs Brownian motion and improved Lévy flight strategies for global search, while exploitation uses enhanced HHO for local optimization. A novel Dual-Period Oscillation Attenuation Strategy dynamically adjusts parameters to balance exploration-exploitation. Performance validation using CEC2017 benchmark functions shows HHONMPA significantly outperforms the original HHO and MPA in solution accuracy and convergence speed, confirmed through statistical testing. Engineering validation applies the algorithm to the Lower Jinsha River—Three Gorges four-reservoir system, conducting experiments across various hydrological scenarios. Results demonstrate substantial improvements in search accuracy and convergence efficiency compared to existing methods. HHONMPA effectively addresses large-scale cascade reservoir optimization challenges, offering promising prospects for water resource management and hydropower scheduling applications.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2073-4441
2073-4441
DOI:10.3390/w17223291