Multiple Strategies Based Salp Swarm Algorithm for Optimal Operation of Multiple Hydropower Reservoirs

Reasonable optimal operation policy for complex multiple reservoir systems is very important the safe and efficient utilization of water resources. The hydropower reservoirs should be optimized to maximize total generation, while ensuring flood control safety by effective storage release reservoirs. To achieve this goal, a new meta-heuristic algorithm, salp swarm algorithm (SSA), used optimize joint first time. SSA competitive bio-inspired optimizer, which has received substantial attention from researchers in wide variety applications finance, engineering, science because its little controlling parameters adaptive exploratory behavior. However, it still faces few drawbacks such as lack exploitation local optima stagnation, leading slow convergence rate. In order tackle these problems, strategies combining sine cosine operator, opposition-based learning mechanism, elitism strategy are applied original SSA. operator balance exploration over course iteration; mechanism enhance diversity swarm; adopted find global optima. Then, improved (ISSA) compared with six well-known algorithms on 23 classical benchmark functions. results obtained demonstrate that ISSA outperforms most algorithms. real world. A system, namely Xiluodu Reservoir Xiangjiaba Rservoir, upper Yangtze River China selected case study. show able solve real-world optimization problem constraints. addition, typical 100 return period 1954, maximum generation about 6671 GWh completing task, increasing 1.18% 1.77% than Particle Swarm Optimization (PSO), respectively. Thus, can an alternative tool resources river basin further utilized proposed method cope increasingly serious climate change.