Multi-Objective Optimization of Engineered Injection and Extraction to 
Enhance In Situ Remediation of Contaminated Groundwater

Multi-objective evolutionary algorithms (MOEAs) are used to optimize the performance of 
solutions to complex design problems based on objectives established by decision-makers. 
This study contributes an iterative problem reformulation technique for MOEA decision 
support.  Problem formulations consist of objectives, decision variables, and constraints, 
and directly influence the results generated by the MOEA. Typically, design problems are 
optimized based on a single problem formulation established a priori.  In this paper, we 
demonstrate an approach to perform iterative optimization using problem formulations 
updated from analyses of results from prior rounds of optimization, which often reveal 
design components that were not initially considered. To demonstrate the approach, we 
consider a novel groundwater remediation technique, Engineered Injection and Extraction 
(EIE), which has never been optimized in the literature. Iterative problem reformulation 
enabled the MOEA to generate EIE solutions with better performance than the 
heuristically-developed solution used in prior work.