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Online surrogate multiobjective optimization algorithm for contaminated groundwater remediation designs

机译：污染地下水修复设计的在线替代多目标优化算法

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This paper proposes an online surrogate model-assisted multiobjective optimization framework to identify optimal remediation strategies for groundwater contaminated with dense non-aqueous phase liquids. The optimization involves three objectives: minimizing the remediation cost and duration and maximizing the contamination removal rate. The proposed framework adopts a multiobjective feasibility-enhanced particle swarm optimization algorithm to solve the optimization model and uses an online surrogate model as a substitute for the time-consuming multiphase flow model for calculating contamination removal rates during the optimization process. The resulting approach allows decision makers to find a balance among the remediation cost, remediation duration and contamination removal rate for remediating contaminated groundwater. The new algorithm is compared with the nondominated sorting genetic algorithm II, which is an extensively applied and well-known algorithm. The results show that the Pareto solutions obtained by the new algorithm have greater diversity and stability than those obtained by the nondominated sorting genetic algorithm II, indicating that the new algorithm is more applicable than the nondominated sorting genetic algorithm II for optimizing remediation strategies for contaminated groundwater. Additionally, the surrogate model and Pareto optimal set obtained by the proposed framework are compared with those of the offline surrogate model-assisted multiobjective optimization framework. The results indicate that the surrogate model accuracy and Pareto front achieved by the proposed framework outperform those of the offline surrogate model-assisted optimization framework. Thus, we conclude that the proposed framework can effectively enhance the surrogate model accuracy and further extend the comprehensive performance of Pareto solutions. (C) 2019 Elsevier Inc. All rights reserved.
机译：本文提出了一种在线替代模型辅助的多目标优化框架，以识别被浓非水相液体污染的地下水的最佳修复策略。优化涉及三个目标：最小化修复成本和持续时间，以及最大化污染物去除率。提出的框架采用多目标可行性增强的粒子群优化算法求解优化模型，并使用在线替代模型替代费时的多相流模型来计算优化过程中的污染物去除率。由此产生的方法使决策者可以在修复成本，修复持续时间和污染物去除率之间找到平衡，以修复受污染的地下水。将该新算法与广泛应用且众所周知的算法非支配排序遗传算法II进行了比较。结果表明，与非支配分类遗传算法II相比，新算法获得的Pareto解具有更大的多样性和稳定性，表明该算法比非支配分类遗传算法II更适用于优化地下水污染修复策略。 。此外，将所提出的框架获得的代理模型和帕累托最优集与离线代理模型辅助的多目标优化框架进行了比较。结果表明，所提出的框架所实现的替代模型精度和帕累托锋均优于离线替代模型辅助优化框架。因此，我们得出结论，提出的框架可以有效地提高代理模型的准确性，并进一步扩展Pareto解决方案的综合性能。 （C）2019 Elsevier Inc.保留所有权利。

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