Research on hybrid reservoir scheduling optimization based on improved walrus optimization algorithm with coupling...

Area of Science:

• Hydraulic Engineering and Water Resource Management
• Optimization Algorithms and Computational Intelligence

Background:

• Reservoir flood control scheduling is a complex optimization problem with numerous constraints.
• Existing optimization algorithms face challenges in efficiently handling these constraints.

Purpose of the Study:

• To develop an innovative algorithm, ε-IWOA (adaptive ε-constraint and Multi Strategy Optimization Improvement), for enhanced reservoir flood control scheduling.
• To improve the optimization performance and stability of the walrus optimization algorithm (WOA) for constrained problems.

Main Methods:

• The ε-IWOA algorithm combines the basic walrus optimization algorithm (WOA) with the adaptive ε-constraint method.
• It incorporates SPM chaotic mapping for initialization, a spiral search strategy, and local enhancement using Cauchy mutation and reverse learning.
• The algorithm's performance is validated on 24 constrained optimization test functions and applied to a three-reservoir flood control system in the Luanhe River Basin.

Main Results:

• The ε-IWOA algorithm demonstrated superior optimization ability and stable performance on test functions.
• In the case study, ε-IWOA achieved high reservoir flood control capacity utilization (89.32%-90.02%) and a 49% peak reduction at control points.
• Comparative analysis showed ε-IWOA outperformed ε-WOA, ε-DE, and ε-PSO algorithms in reservoir scheduling.

Conclusions:

• The ε-IWOA algorithm offers a practical and effective solution for reservoir optimization scheduling.
• This study provides novel insights and methods for optimizing flood control scheduling in reservoir groups.
• The findings contribute to the advancement of reservoir scheduling practices.