Online surrogate multiobjective optimization algorithm for contaminated groundwater remediation designs
Online surrogate multiobjective optimization algorithm for contaminated groundwater remediation designs
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DOI：
10.1016/j.apm.2019.09.053
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发表时间：
2020-02
影响因子：
5
通讯作者：
Xue Jiang;Jin Na
中科院分区：
文献类型：
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作者：
Xue Jiang;Jin Na
本文提出了一个在线代理模型辅助多目标优化框架，以确定最佳的修复策略，地下水污染的稠密非水相液体。优化包括三个目标：最小化修复成本和时间，最大化污染物去除率。该框架采用多目标可行性增强粒子群优化算法求解优化模型，并使用在线代理模型作为耗时的多相流模型的替代，用于计算污染物去除率在优化过程中。由此产生的方法允许决策者找到一个平衡之间的修复成本，修复时间和污染去除率修复受污染的地下水。新算法与非支配排序遗传算法II，这是一个广泛应用和知名的算法进行了比较。结果表明，新算法得到的Pareto解比非支配排序遗传算法II具有更大的多样性和稳定性，表明新算法比非支配排序遗传算法II更适用于污染地下水修复策略的优化.此外，代理模型和Pareto最优集所得到的建议框架进行了比较与离线代理模型辅助多目标优化框架。结果表明，该框架在代理模型精度和Pareto前沿方面优于离线代理模型辅助优化框架。因此，我们得出结论，该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。
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.