基于禁忌搜索与粒子群优化算法的地下水污染源信息辨识
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徐津(1992—),男,副教授,博士,主要从事水力学及河流动力学研究。E-mail:hhu_xj@hhu.edu.cn

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国家重点研发计划项目(2022YFC3202600);国家自然科学基金项目(52479062,52309086);江苏省科学技术基础研究计划青年基金项目(BK20241516);中央高校基本科研业务费专项资金项目(B240201185);龙溪河流域洪水与环境风险预警系统开发咨询服务项目(3704-PRC)


Groundwater contaminant source information identification based on tabu search and particle swarm optimization algorithm
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    摘要:

    为准确辨识地下水污染源位置、污染物释放过程等关键信息,采用模拟-优化理论框架,将需要同步辨识多种污染源信息的地下水反演问题概化为包含离散型、连续型变量的混合变量优化问题,并提出了一种基于禁忌搜索与粒子群优化算法的两阶段组合优化(TS-PSO)算法,该算法采用禁忌搜索策略确定污染源位置,利用粒子群优化算法识别污染物的释放强度及释放过程。算例验证结果表明:与传统演化算法(GA、PSO算法)相比,TS-PSO算法的求解效率更高,计算结果更可靠,计算精度更高;对于多个污染源的反演问题,TS-PSO算法可快速、有效地辨识污染源位置、污染物释放强度和释放过程。

    Abstract:

    To accurately identify key information such as the groundwater contaminant source location and contaminant release process, the simulation-optimization theoretical framework was used. The groundwater inversion problem that requires simultaneous identification of information from multiple contaminant sources was generalized as a mixed-variable optimization problem involving discrete and continuous variables. A two-stage combinatorial optimization algorithm based on tabu search and particle swarm optimization (TS-PSO) algorithm was proposed. The algorithm applied the tabu search method to locate contaminant sources and then used the particle swarm optimization algorithm to determine the contaminant release intensity and process. The verification results of the numerical examples show that compared with traditional evolutionary algorithms(GA and PSO algorithm), TS-PSO algorithm has higher solution efficiency, more reliable calculation results, and higher calculation accuracy. For the inversion problem of multiple contaminant sources, TS-PSO algorithm can quickly and effectively identify the location of contaminant sources, as well as the release intensity and release process of contaminants.

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徐津,伍梦天,李凯,等.基于禁忌搜索与粒子群优化算法的地下水污染源信息辨识[J].河海大学学报(自然科学版),2026,54(1):36-42.(Xu Jin, Wu Mengtian, Li Kai, et al. Groundwater contaminant source information identification based on tabu search and particle swarm optimization algorithm[J]. Journal of Hohai University (Natural Sciences),2026,54(1):36-42.(in Chinese))

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  • 收稿日期:2024-11-25
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  • 在线发布日期: 2026-01-29
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