An auto-adaptive optimization approach for targeting nonpoint source pollution control practices.

摘要: To solve computationally intensive and technically complex control of nonpoint source pollution, the traditional genetic algorithm was modified into an auto-adaptive pattern, a new framework proposed by integrating this with watershed model economic module. Although conceptually simple comprehensive, would search automatically for those Pareto-optimality solutions without calibration optimization parameters. The applied in case study typical Three Gorges Reservoir area, China. results indicated that evolutionary process improved due to incorporation In addition, outperformed state-of-the-art existing algorithms terms convergence ability computational efficiency. At same cost level, greater pollutant reductions could be identified. From scientific viewpoint, extended other watersheds provide cost-effective configurations BMPs.