Using interactive archives in evolutionary multiobjective optimization: A case study for long-term groundwater monitoring design

Monitoring complex environmental systems is extremely challenging because it requires environmental professionals to capture impacted systems' governing processes, elucidate human and ecologic risks, limit monitoring costs, and satisfy the interests of multiple stakeholders (e.g., site owners, regulators, and public advocates). Evolutionary multiobjective optimization (EMO) has tremendous potential to help resolve these issues by providing environmental stakeholders with a direct understanding of their monitoring tradeoffs. This paper demonstrates how ɛ-dominance archiving and automatic parameterization techniques can be used to significantly improve the ease-of-use and efficiency of EMO algorithms. Results are presented for a four-objective groundwater monitoring design problem in which the archiving and parameterization techniques are combined to reduce computational demands by more than 90% relative to prior published results. The methods of this paper can be easily generalized to other multiobjective applications to minimize computational times as well as trial-and-error parameter analysis.