Comparison of multi-objective genetic algorithms for optimization of cascade reservoir systems

Abstract

Abstract Multi-objective genetic algorithms (MOGAs) are widely used for multi-reservoir systems’ optimization due to their high efficiency and fast convergence. However, the computational cost grows exponentially with the expansion of multi-reservoir systems and the increased dimensions of optimization problems, posing a great challenge to multi-reservoir operations. Therefore, it is important to find a suitable and efficient multi-objective algorithm for multi-reservoir system optimization. In this study, three representative MOGAs were selected, namely the Non-dominated Sorting Genetic Algorithm II (NSGA-II), Non-Dominated Sorting Genetic Algorithm III (NSGA-III), and Reference Vector Guided Evolutionary Algorithm (RVEA), and three multi-objective optimization models were then developed accordingly. Numerical experiments were conducted to evaluate the performance of these three algorithms for the optimization of a cascaded reservoir system. The results show that for the two-objective model, the non-dominant size generation rate (NDSGR) of NSGA-II is 1.66 and 4.01 times that of NSGA-III and RVEA, respectively. NSGA-II is more suitable for solving the optimization operation problem with two objectives. Based on the comprehensive evaluation, NSGA-III seems to be more appropriate for more than two objectives. These results emphasize the importance of using appropriate algorithms and provide further insights into the choice of algorithms for the optimization of hydropower systems.