Optimization of Cascade Reservoir Operation Using Artificial Fish Swarm Algorithm

In this article, we present a methodology for solving cascade reservoir optimization scheduling problems using the Artificial Fish Swarm Algorithm (AFSA). This approach enables optimization calculations through straightforward modifications of corresponding constraint conditions, primarily implemented by adjusting boundary parameters and fitness functions in the code structure. In practical applications, this method assists reservoir managers in better planning reservoir operations to meet diverse demands. The algorithm's implementation typically involves defining fish swarm behaviors such as preying, swarming, and following through mathematical models, which enhances search efficiency in high-dimensional solution spaces. Furthermore, employing AFSA significantly improves the precision and efficiency of optimization scheduling, leading to better water resource conservation and promoting sustainable development through iterative optimization processes that balance exploitation and exploration mechanisms.