Development And Application Of Evolutionary Algorithms For Optimal Design Of Groundwater Remediation Systems

Development and application of evolutionary algorithms(EAs)is a very important research topic in the field of groundwater contaminant remediation optimization management.The EAs possessing the characteristics of self organization,adaption,and self learning,are of strong robustness,extensive applicability and overall searching ability.The EAs,regardless of the nature of problems,can handle complex optimization problems in water science that cannot be eradicated by the traditional methods.Thus the EAs have been widely applied in the field of groundwater contaminant remediation optimization management.Generally,the establishment of a groundwater contamianat remediation system consists of three stages:first,the hydrogeological parameter identification stage;second,the groundwater pollution remediation stage based on variety of remediation techniques;third,the contaminant monitoring phase.In this paper,different S/O models during different steps of the groundwater remediation system were established.Furthermore,a variety of EAs were developed to solve different S/O models.Firstly,the research background,research significance and the reviews of researches and applications of EAs in different stages of groundwater remediation management were carried out.Then,a new fast harmony search algorithm(FHS)was developed to solve the hydrogeological parameter identification problems.Applications of FHS to a hypothetical test problem and a field problem proved the superiority of FHS than the traditional single-objective EAs.Next,FHS was extened to a multi-objective fast harmony search algorithm(MOFHS)by adding the Pareto solution set filter and elite individual preservation strategy to guarantee uniformity and integrity of the Pareto front of multi-objective optimization problems.Also,the operation library of individual fitness was introduced to improve calculation speed.Then,the MOFHS was applied to solve groundwater contaminant remediation system optimization management problems based on Pump-and-Treat(PAT)technology.Optimization results of a hypothetical test problem and a field problem showed that MOFHS had great application potential in the field of multi-objective groundwater remediation optimization.Finally,for the optimization of groundwater contaminant monitoring network design,the traditional single-objective groundwater monitoring network design model was extended to a multi-objective optimization model involving four objectives:(i)minimization of total sampling and analysis costs for contaminant plume monitoring,(ii)minimization of mass estimation error of the contaminant plume,(iii)minimization of the first moment estimation error of the contaminant plume,and(iv)minimization of the second moment estimation error of the contaminant plume.Furthermore,the Improved Niched Pareto Genetic Algorithm(INPGA)was used to solve the multi-objective optimization problem associated with groundwater monitoring network design for contaminant plume monitoring.Moreover,application of the proposed methodology to a hypothetical monitoring network design problem showed that the INPGA could achieve more efficient implementation to produce a series of the Pareto optimal solutions,which could facilitate the decision-makers in choosing the most cost-effective monitoring strategy consistent with the actual field conditions.Furthermore,a new multi-objective optimization algorithm called the probabilistic Pareto genetic algorithm(PPGA),was developed to solve the multi-objective optimization model under uncertainty associated with the hydraulic conductivity(K)field of aquifers.Also,Monte Carlo(MC)analysis was used to demonstrate the effectiveness of the proposed methodology.The MC analysis results showed that the proposed PPGA could find Pareto-optimal solutions with low variability and high reliability and was a potentially effective tool for the proposed multi-objective optimization model for optimizing the multi-objective sampling network design of groundwater monitoring under uncertainty.Finally,analytic results throughout the thesis were summarized and concluded.And all the new understandings about the groundwater contaminant remediation management gained in this study were summarized and elaborated.In addition,some suggestions were given to guide the future studies of EAs in groundwater management.