Application of Adaptive ε-IZOA-Based Optimization Algorithm in the Optimal Scheduling of Reservoir Clusters

Increasing environmental variability and operational complexity in reservoir systems necessitate advanced optimization frameworks for flood control. This study proposes the ε-constrained Improved Zebra Optimization Algorithm (ε-IZOA), a novel metaheuristic algorithm integrating an enhanced Zebra Optimization Algorithm (ZOA) with adaptive ε-constraint handling, to address multi-reservoir flood control optimization. Three strategic modifications advance the standard ZOA: (1) Bernoulli chaotic mapping for diversified population initialization; (2) adaptive weight balancing for exploration-exploitation trade-off mitigation; and (3) golden sinusoidal vectorization for global search refinement, collectively forming the Improved ZOA (IZOA). The ε-IZOA synergizes IZOA with ε-dominance criteria to dynamically resolve constrained optimization conflicts. Applied to the Yellow River Basin’s five-reservoir cascade, ε-IZOA achieves a 52.97% peak shaving rate at Huayuankou Station, reducing the maximum discharge to 18,745.02 m3/s—a performance surpassing benchmark methods. The algorithm’s success stems from its bio-inspired hybrid architecture, which embeds swarm intelligence principles into nonlinear constraint management. This work establishes ε-IZOA as a computationally robust tool for large-scale reservoir optimization, with implications for mitigating flood risks in climate-sensitive basins. Future research should prioritize its integration with real-time hydrological forecasting systems.