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<p>This study proposes an Improved Hiking Optimization Algorithm (IHOA) for optimizing pump-and-treat systems in heterogeneous aquifers under parameter uncertainty. The key innovation lies in an adaptive sweep factor (SF) mechanism that dynamically adjusts the search radius based on convergence progress, effectively balancing exploration and exploitation. Comprehensive numerical experiments were conducted using a trichloroethylene (TCE)-contaminated site in southeastern Zhejiang Province, China. Results demonstrate that IHOA outperforms conventional genetic algorithm (GA), Improved GA (IGA), and original Hiking Optimization Algorithm (HOA) in both solution quality and computational efficiency, reducing optimization time by 73.75% while achieving lower objective function values. Sensitivity analyses reveal that increasing log-hydraulic conductivity variance from 0.25 to 1.00 systematically degrades remediation performance, with mean residual contaminant mass increasing by 181% and its standard deviation by 189%. Furthermore, dividing the 180-day remediation period into six stress periods (30&#xa0;days each) reduces residual mass by 43% compared to a single stress period, with particularly pronounced improvements at plume boundaries (33–38% lower concentrations). Most significantly, IHOA-optimized solutions exhibit robust cross-field adaptability, maintaining effectiveness even when applied to conductivity fields substantially different from those used in optimization. These findings provide both theoretical insights and practical tools for designing efficient groundwater remediation systems under uncertainty, with important implications for reducing site characterization costs while ensuring regulatory compliance.</p>

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Pumping Optimization for Groundwater Remediation Under Uncertainty Using an Improved Hiking Optimization Algorithm

  • 期刊论文

  • 发表日期：

    2026年4月2日

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  • Xuemin Xia,
  • Yujie Yang,
  • Simin Jiang,
  • Xianwen Li

摘要

This study proposes an Improved Hiking Optimization Algorithm (IHOA) for optimizing pump-and-treat systems in heterogeneous aquifers under parameter uncertainty. The key innovation lies in an adaptive sweep factor (SF) mechanism that dynamically adjusts the search radius based on convergence progress, effectively balancing exploration and exploitation. Comprehensive numerical experiments were conducted using a trichloroethylene (TCE)-contaminated site in southeastern Zhejiang Province, China. Results demonstrate that IHOA outperforms conventional genetic algorithm (GA), Improved GA (IGA), and original Hiking Optimization Algorithm (HOA) in both solution quality and computational efficiency, reducing optimization time by 73.75% while achieving lower objective function values. Sensitivity analyses reveal that increasing log-hydraulic conductivity variance from 0.25 to 1.00 systematically degrades remediation performance, with mean residual contaminant mass increasing by 181% and its standard deviation by 189%. Furthermore, dividing the 180-day remediation period into six stress periods (30 days each) reduces residual mass by 43% compared to a single stress period, with particularly pronounced improvements at plume boundaries (33–38% lower concentrations). Most significantly, IHOA-optimized solutions exhibit robust cross-field adaptability, maintaining effectiveness even when applied to conductivity fields substantially different from those used in optimization. These findings provide both theoretical insights and practical tools for designing efficient groundwater remediation systems under uncertainty, with important implications for reducing site characterization costs while ensuring regulatory compliance.