Multi-Objective Reservoir Operation using Multi-Objective Teaching Learning Based Optimization

Reservoir operation is complex engineering problem due to multi-objective, non-linear, stochastic and non-convex in nature. This paper presents a robust and effective metaheuristic algorithm namely, multi-objective teaching learning based optimization (MO-TLBO) to obtain an optimal reservoir operation. The advantage of TLBO, it does not require any algorithm specific controlling parameters. The MO-TLBO is applied to Ukai reservoir, India, which serves as multi-purpose reservoir namely power generation, irrigation and flood control. The results were compared with non-dominated sorting genetic algorithm II (NSGA-II), multi-objective differential evolutionary (MODE) and multi-objective particle swarm optimization (MOPSO). Based on the results it is found that MO-TLBO is an effective and alternative method to solve multi-objective reservoir operation.