Spatial evolutionary algorithm for large-scale groundwater management

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Large-scale groundwater management problems pose great computational challenges for decision making because of the spatial complexity and heterogeneity. This study describes a modeling framework to solve largescale groundwater management problems using a newly developed spatial evolutionary algorithm (SEA). This method incorporates spatial patterns of the hydrological conditions to facilitate the optimal search of spatial decision variables. The SEA employs a hierarchical tree structure to represent spatial variables in a more efficient way than the data structure used by a regular EA. Furthermore, special crossover, mutation and selection operators are designed in accordance with the tree representation. In this paper, the SEA was applied to searching for the maximum vegetation coverage associated with a distributed groundwater system in an arid region. Computational experiments demonstrate the efficiency of SEA for large-scale spatial optimization problems. The extension of this algorithm for other water resources management problems.

Original languageEnglish (US)
Title of host publicationGenetic and Evolutionary Computing - Proceeding of the Eighth International Conference on Genetic and Evolutionary
EditorsHui Sun, Vaclav Snasel, Chun-Wei Lin, Jeng-Shyang Pan, Ajith Abraham, Ching-Yu Yang, Chun-Wei Lin
Number of pages12
ISBN (Electronic)9783319122854
StatePublished - 2015
Event8th International Conference on Genetic and Evolutionary Computing, ICGEC 2014 - Nanchang, China
Duration: Oct 18 2014Oct 20 2014

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357


Other8th International Conference on Genetic and Evolutionary Computing, ICGEC 2014


  • Decision making
  • Genetic Algorithms
  • Large-Scale
  • Resources allocation
  • Spatial Optimization

ASJC Scopus subject areas

  • Control and Systems Engineering
  • General Computer Science


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