An auto-adaptive optimization approach for targeting nonpoint source pollution control practices.
An auto-adaptive optimization approach for targeting nonpoint source pollution control practices.
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DOI：
10.1038/srep15393
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发表时间：
2015-10-21
影响因子：
4.6
通讯作者：
Shen Z
中科院分区：
文献类型：
--
作者：
Chen L;Wei G;Shen Z
为解决计算量大、技术复杂的非点源污染控制问题，将传统遗传算法改进为自适应模式，并将其与流域模型和经济模型相结合，提出了一种新的算法框架。虽然概念上简单和全面，所提出的算法将自动搜索这些帕累托最优解，而无需复杂的优化参数的校准。该模型应用于一个典型的流域的三峡库区，中国的案例研究。结果表明，自适应参数的引入改善了优化的进化过程。此外，该算法优于国家的最先进的现有算法的收敛能力和计算效率。在相同的成本水平下，可以找到更大程度减少污染物的解决方案。从科学的角度来看，所提出的算法可以扩展到其他流域，以提供具有成本效益的配置BMP。
To solve computationally intensive and technically complex control of nonpoint source pollution, the traditional genetic algorithm was modified into an auto-adaptive pattern, and a new framework was proposed by integrating this new algorithm with a watershed model and an economic module. Although conceptually simple and comprehensive, the proposed algorithm would search automatically for those Pareto-optimality solutions without a complex calibration of optimization parameters. The model was applied in a case study in a typical watershed of the Three Gorges Reservoir area, China. The results indicated that the evolutionary process of optimization was improved due to the incorporation of auto-adaptive parameters. In addition, the proposed algorithm outperformed the state-of-the-art existing algorithms in terms of convergence ability and computational efficiency. At the same cost level, solutions with greater pollutant reductions could be identified. From a scientific viewpoint, the proposed algorithm could be extended to other watersheds to provide cost-effective configurations of BMPs.