Optimizing hydropower reservoir operation using hybrid genetic algorithm and chaos

Genetic algorithms (GA) have been widely used to solve water resources system optimization. However, when applying GAs to solve large-scale and complex water reservoir system problems, premature convergence is one of the most frequently encountered difficulties and takes a large number of iterations to reach the global optimal solution and the optimization may get stuck at a local optimum. Therefore, a novel chaos genetic algorithm (CGA) based on the chaos optimization algorithm (COA) and genetic algorithm (GA), which makes use of the ergodicity and internal randomness of chaos iterations, is presented to overcome premature local optimum and increase the convergence speed of genetic algorithm. CGA integrates powerful global searching capability of the GA with that of powerful local searching capability of the COA. Two measures are adopted in order to improve the performance of the GA. The first one is the adoption of chaos optimization of the initialization to improve species quality and to maintain the population diversity. The second is the utilization of annealing chaotic mutation operation to replace standard mutation operator in order to avoid the search being trapped in local optimum. The global optimum of the Rosenbrock function is employed to examine the performance of the GA. The result indicates that it can improve convergence speed and solution accuracy. A series of monthly inflow of 38 years is employed to simulate the hydropower reservoir optimization operation. The results show that the long term average annual energy based CGA is the best and its convergent speed not only is faster than dynamic programming largely, but also overpasses the standard GA. Thus, the proposed approach is feasible and effective in optimal operations of This is the Pre-Published Version