Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method

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@Article{Ouyang:2017:JCH,
  author =       "Qi Ouyang and Wenxi Lu and Zeyu Hou and Yu Zhang and 
                 Shuai Li and Jiannan Luo",
  title =        "Chance-constrained multi-objective optimization of
                 groundwater remediation design at DNAPLs-contaminated
                 sites using a multi-algorithm genetically adaptive
                 method",
  journal =      "Journal of Contaminant Hydrology",
  volume =       "200",
  pages =        "15--23",
  year =         "2017",
  ISSN =         "0169-7722",
  DOI =          "doi:10.1016/j.jconhyd.2017.03.004",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0169772216300845",
  abstract =     "In this paper, a multi-algorithm genetically adaptive
                 multi-objective (AMALGAM) method is proposed as a
                 multi-objective optimization solver. It was implemented
                 in the multi-objective optimization of a groundwater
                 remediation design at sites contaminated by dense
                 non-aqueous phase liquids. In this study, there were
                 two objectives: minimization of the total remediation
                 cost, and minimization of the remediation time. A
                 non-dominated sorting genetic algorithm II (NSGA-II)
                 was adopted to compare with the proposed method. For
                 efficiency, the time-consuming surfactant-enhanced
                 aquifer remediation simulation model was replaced by a
                 surrogate model constructed by a multi-gene genetic
                 programming (MGGP) technique. Similarly, two other
                 surrogate modeling methods-support vector regression
                 (SVR) and Kriging (KRG)-were employed to make
                 comparisons with MGGP. In addition, the
                 surrogate-modeling uncertainty was incorporated in the
                 optimization model by chance-constrained programming
                 (CCP). The results showed that, for the problem
                 considered in this study, (1) the solutions obtained by
                 AMALGAM incurred less remediation cost and required
                 less time than those of NSGA-II, indicating that
                 AMALGAM outperformed NSGA-II. It was additionally shown
                 that (2) the MGGP surrogate model was more accurate
                 than SVR and KRG; and (3) the remediation cost and time
                 increased with the confidence level, which can enable
                 decision makers to make a suitable choice by
                 considering the given budget, remediation time, and
                 reliability.",
  keywords =     "genetic algorithms, genetic programming,
                 Chance-constrained programming, Groundwater
                 remediation, Multi-algorithm method, Multi-objective
                 optimization, Surrogate model",
}

Genetic Programming entries for Qi Ouyang Wenxi Lu Zeyu Hou Yu Zhang Shuai Li Jiannan Luo

Citations