A Hybrid Particle Swarm Optimization-Genetic Algorithm for Multiobjective Reservoir Ecological Dispatching

Abstract

Reservoir ecological dispatching is a complex system problem with multi-objective, multiplecriteria and multiple-phase. This study establishes a multi-objective ecological dispatching model in Changchun city of Yinma River Basin based on the water demand of social economic development, river ecology, and the constraint of reservoir characteristic parameters. Taking the advantages of particle swarm optimisation (PSO) and genetic algorithm (GA), a PSO-GA hybrid algorithm is proposed and applied to solve the schemes of ecological dispatching model considering different ecological flow requirements. The annual mean scheduling results show that the three scheduling schemes basically achieve the objectives of river ecological base flow scheduling. While for the ecological suitable flow, the guarantee rate of Dehui section in RGOS1/2/3 scheme is 79.79%/87.95%/96.08%, and that of Nongan section is 82.98%/90.85%/96.45%. The scheduling results of typical years show that the water security situation in the study area is not optimistic, but the river ecological environment can be greatly improved by reservoir ecological dispatching. Finally, the high quality and stable search performance of the hybrid PSO-GA proposed in this study is verified by comparing with other algorithms. The mean value and standard of the objective function of the 20 simulation results calculated by PSO-GA are 97.75% and 0.11 respectively, which are better than other algorithms.