A Hybrid Multi-Objective Evolutionary Algorithm for Optimal Groundwater Management under Variable Density Conditions

In this paper, a new hybrid multi-objective evolutionary algorithm （MOEA）, the niched Pareto tabu search combined with a genetic algorithm （NPTSGA）, is proposed for the management of groundwater resources under variable density conditions. Relatively few MOEAs can possess global search ability conte...

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Published in:	Acta geologica Sinica (Beijing) Vol. 86; no. 1; pp. 246 - 255
Main Authors:	Yun, YANG, Jianfeng, WU, Xiaomin, SUN, Jin, LIN, Jichun, WU
Format:	Journal Article
Language:	English
Published:	Oxford, UK Blackwell Publishing Ltd 01.02.2012 Wiley Subscription Services, Inc Department of Hydrosciences, School of Earth Sciences and Engineering, Nanjing University, Nanjing,Jiangsu 210093, China%Nanfing Hydraulic Research Institute, Nanfing, Jiangsu 210029, China
Edition:	English ed.
Subjects:	Algorithms Coastal aquifers Density Evolutionary algorithms genetic algorithm Genetic algorithms Groundwater Groundwater flow Groundwater management Heuristic multi-objective optimization niched Pareto tabu search niched Pareto tabu search combined with genetic algorithm Optimization Pareto optimality Saline water intrusion Searching Seawater seawater intrusion Solute transport Tabu search Water resources 优化全局搜索能力地下水管理地下水资源管理多目标进化算法度条件混合禁忌搜索 genetic algorithm multi-objective optimization niched Pareto tabu search combined with genetic algorithm niched Pareto tabu search seawater intrusion
ISSN:	1000-9515, 1755-6724
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Abstract	In this paper, a new hybrid multi-objective evolutionary algorithm （MOEA）, the niched Pareto tabu search combined with a genetic algorithm （NPTSGA）, is proposed for the management of groundwater resources under variable density conditions. Relatively few MOEAs can possess global search ability contenting with intensified search in a local area. Moreover, the overall searching ability of tabu search （TS） based MOEAs is very sensitive to the neighborhood step size. The NPTSGA is developed on the thought of integrating the genetic algorithm （GA） with a TS based MOEA, the niched Pareto tabu search （NPTS）, which helps to alleviate both of the above difficulties. Here, the global search ability of the NPTS is improved by the diversification of candidate solutions arising from the evolving genetic algorithm population. Furthermore, the proposed methodology coupled with a density-dependent groundwater flow and solute transport simulator, SEAWAT, is developed and its performance is evaluated through a synthetic seawater intrusion management problem. Optimization results indicate that the NPTSGA offers a tradeoff between the two conflicting objectives. A key conclusion of this study is that the NPTSGA keeps the balance between the intensification of nondomination and the diversification of near Pareto-optimal solutions along the tradeoff curves and is a stable and robust method for implementing the multi-objective design of variable-density groundwater resources.
AbstractList	In this paper, a new hybrid multi‐objective evolutionary algorithm (MOEA), the niched Pareto tabu search combined with a genetic algorithm (NPTSGA), is proposed for the management of groundwater resources under variable density conditions. Relatively few MOEAs can possess global search ability contenting with intensified search in a local area. Moreover, the overall searching ability of tabu search (TS) based MOEAs is very sensitive to the neighborhood step size. The NPTSGA is developed on the thought of integrating the genetic algorithm (GA) with a TS based MOEA, the niched Pareto tabu search (NPTS), which helps to alleviate both of the above difficulties. Here, the global search ability of the NPTS is improved by the diversification of candidate solutions arising from the evolving genetic algorithm population. Furthermore, the proposed methodology coupled with a density‐dependent groundwater flow and solute transport simulator, SEAWAT, is developed and its performance is evaluated through a synthetic seawater intrusion management problem. Optimization results indicate that the NPTSGA offers a tradeoff between the two conflicting objectives. A key conclusion of this study is that the NPTSGA keeps the balance between the intensification of nondomination and the diversification of near Pareto‐optimal solutions along the tradeoff curves and is a stable and robust method for implementing the multi‐objective design of variable‐density groundwater resources. In this paper, a new hybrid multi-objective evolutionary algorithm (MOEA), the niched Pareto tabu search combined with a genetic algorithm (NPTSGA), is proposed for the management of groundwater resources under variable density conditions. Relatively few MOEAs can possess global search ability contenting with intensified search in a local area. Moreover, the overall searching ability of tabu search (TS) based MOEAs is very sensitive to the neighborhood step size. The NPTSGA is developed on the thought of integrating the genetic algorithm (GA) with a TS based MOEA, the niched Pareto tabu search (NPTS), which helps to alleviate both of the above difficulties. Here, the global search ability of the NPTS is improved by the diversification of candidate solutions arising from the evolving genetic algorithm population. Furthermore, the proposed methodology coupled with a density-dependent groundwater flow and solute transport simulator, SEAWAT, is developed and its performance is evaluated through a synthetic seawater intrusion management problem. Optimization results indicate that the NPTSGA offers a tradeoff between the two conflicting objectives. A key conclusion of this study is that the NPTSGA keeps the balance between the intensification of nondomination and the diversification of near Pareto-optimal solutions along the tradeoff curves and is a stable and robust method for implementing the multi-objective design of variable-density groundwater resources. [PUBLICATION ABSTRACT] In this paper, a new hybrid multi-objective evolutionary algorithm （MOEA）, the niched Pareto tabu search combined with a genetic algorithm （NPTSGA）, is proposed for the management of groundwater resources under variable density conditions. Relatively few MOEAs can possess global search ability contenting with intensified search in a local area. Moreover, the overall searching ability of tabu search （TS） based MOEAs is very sensitive to the neighborhood step size. The NPTSGA is developed on the thought of integrating the genetic algorithm （GA） with a TS based MOEA, the niched Pareto tabu search （NPTS）, which helps to alleviate both of the above difficulties. Here, the global search ability of the NPTS is improved by the diversification of candidate solutions arising from the evolving genetic algorithm population. Furthermore, the proposed methodology coupled with a density-dependent groundwater flow and solute transport simulator, SEAWAT, is developed and its performance is evaluated through a synthetic seawater intrusion management problem. Optimization results indicate that the NPTSGA offers a tradeoff between the two conflicting objectives. A key conclusion of this study is that the NPTSGA keeps the balance between the intensification of nondomination and the diversification of near Pareto-optimal solutions along the tradeoff curves and is a stable and robust method for implementing the multi-objective design of variable-density groundwater resources. : In this paper, a new hybrid multi‐objective evolutionary algorithm (MOEA), the niched Pareto tabu search combined with a genetic algorithm (NPTSGA), is proposed for the management of groundwater resources under variable density conditions. Relatively few MOEAs can possess global search ability contenting with intensified search in a local area. Moreover, the overall searching ability of tabu search (TS) based MOEAs is very sensitive to the neighborhood step size. The NPTSGA is developed on the thought of integrating the genetic algorithm (GA) with a TS based MOEA, the niched Pareto tabu search (NPTS), which helps to alleviate both of the above difficulties. Here, the global search ability of the NPTS is improved by the diversification of candidate solutions arising from the evolving genetic algorithm population. Furthermore, the proposed methodology coupled with a density‐dependent groundwater flow and solute transport simulator, SEAWAT, is developed and its performance is evaluated through a synthetic seawater intrusion management problem. Optimization results indicate that the NPTSGA offers a tradeoff between the two conflicting objectives. A key conclusion of this study is that the NPTSGA keeps the balance between the intensification of nondomination and the diversification of near Pareto‐optimal solutions along the tradeoff curves and is a stable and robust method for implementing the multi‐objective design of variable‐density groundwater resources. P641.8; In this paper,a new hybrid multi-objective evolutionary algorithm (MOEA),the niched Pareto tabu search combined with a genetic algorithm (NPTSGA),is proposed for the management of groundwater resources under variable density conditions.Relatively few MOEAs can possess global search ability contenting with intensified search in a local area.Moreover,the overall searching ability of tabu search (TS) based MOEAs is very sensitive to the neighborhood step size.The NPTSGA is developed on the thought of integrating the genetic algorithm (GA) with a TS based MOEA,the niched Pareto tabu search (NPTS),which helps to alleviate both of the above difficulties.Here,the global search ability of the NPTS is improved by the diversification of candidate solutions arising from the evolving genetic algorithm population.Furthermore,the proposed methodology coupled with a density-dependent groundwater flow and solute transport simulator,SEAWAT,is developed and its performance is evaluated through a synthetic seawater intrusion management problem.Optimization results indicate that the NPTSGA offers a tradeoff between the two conflicting objectives.A key conclusion of this study is that the NPTSGA keeps the balance between the intensification of nondomination and the diversification of near Pareto-optimal solutions along the tradeoff curves and is a stable and robust method for implementing the multi-objective design of variable-density groundwater resources.
Author	Xiaomin, SUN Jin, LIN Jianfeng, WU Yun, YANG Jichun, WU
AuthorAffiliation	Department of Hydrosciences, School of Earth Sciences and Engineering, Nanjing University, Nanjing, Jiangsu 210093, China Nanjing Hydraulic Research Institute, Nanjing, Jiangsu 210029, China
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Notes	seawater intrusion, multi-objective optimization, niched Pareto tabu search combined with genetic algorithm, niched Pareto tabu search, genetic algorithm 11-2001/P In this paper, a new hybrid multi-objective evolutionary algorithm （MOEA）, the niched Pareto tabu search combined with a genetic algorithm （NPTSGA）, is proposed for the management of groundwater resources under variable density conditions. Relatively few MOEAs can possess global search ability contenting with intensified search in a local area. Moreover, the overall searching ability of tabu search （TS） based MOEAs is very sensitive to the neighborhood step size. The NPTSGA is developed on the thought of integrating the genetic algorithm （GA） with a TS based MOEA, the niched Pareto tabu search （NPTS）, which helps to alleviate both of the above difficulties. Here, the global search ability of the NPTS is improved by the diversification of candidate solutions arising from the evolving genetic algorithm population. Furthermore, the proposed methodology coupled with a density-dependent groundwater flow and solute transport simulator, SEAWAT, is developed and its performance is evaluated through a synthetic seawater intrusion management problem. Optimization results indicate that the NPTSGA offers a tradeoff between the two conflicting objectives. A key conclusion of this study is that the NPTSGA keeps the balance between the intensification of nondomination and the diversification of near Pareto-optimal solutions along the tradeoff curves and is a stable and robust method for implementing the multi-objective design of variable-density groundwater resources. ark:/67375/WNG-P1FHCVGW-7 istex:12946AE45380E67EDFEAE6991DFDECB4308FD459 ArticleID:ACGS625 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23
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Snippet	In this paper, a new hybrid multi-objective evolutionary algorithm （MOEA）, the niched Pareto tabu search combined with a genetic algorithm （NPTSGA）, is... : In this paper, a new hybrid multi‐objective evolutionary algorithm (MOEA), the niched Pareto tabu search combined with a genetic algorithm (NPTSGA), is... In this paper, a new hybrid multi‐objective evolutionary algorithm (MOEA), the niched Pareto tabu search combined with a genetic algorithm (NPTSGA), is... In this paper, a new hybrid multi-objective evolutionary algorithm (MOEA), the niched Pareto tabu search combined with a genetic algorithm (NPTSGA), is... P641.8; In this paper,a new hybrid multi-objective evolutionary algorithm (MOEA),the niched Pareto tabu search combined with a genetic algorithm (NPTSGA),is...
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SubjectTerms	Algorithms Coastal aquifers Density Evolutionary algorithms genetic algorithm Genetic algorithms Groundwater Groundwater flow Groundwater management Heuristic multi-objective optimization niched Pareto tabu search niched Pareto tabu search combined with genetic algorithm Optimization Pareto optimality Saline water intrusion Searching Seawater seawater intrusion Solute transport Tabu search Water resources 优化全局搜索能力地下水管理地下水资源管理多目标进化算法度条件混合禁忌搜索
Title	A Hybrid Multi-Objective Evolutionary Algorithm for Optimal Groundwater Management under Variable Density Conditions
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