A diagnostic assessment of evolutionary algorithms for multi-objective surface water reservoir control

Abstract

Globally, the pressures of expanding populations, climate change, and increased energy demands are motivating significant investments in re-operationalizing existing reservoirs or designing operating policies for new ones. These challenges require an understanding of the tradeoffs that emerge across the complex suite of multi-sector demands in river basin systems. This study benchmarks our current capabilities to use Evolutionary Multi-Objective Direct Policy Search (EMODPS), a decision analytic framework in which reservoirs' candidate operating policies are represented using parameterized global approximators (e.g., radial basis functions) then those parameterized functions are optimized using multi-objective evolutionary algorithms to discover the Pareto approximate operating policies. We contribute a comprehensive diagnostic assessment of modern MOEAs' abilities to support EMODPS using the Conowingo reservoir in the Lower Susquehanna River Basin, Pennsylvania, USA. Our diagnostic results highlight that EMODPS can be very challenging for some modern MOEAs and that epsilon dominance, time-continuation, and auto-adaptive search are helpful for attaining high levels of performance. The ε-MOEA, the auto-adaptive Borg MOEA, and ε-NSGAII all yielded superior results for the six-objective Lower Susquehanna benchmarking test case. The top algorithms show low sensitivity to different MOEA parameterization choices and high algorithmic reliability in attaining consistent results for different random MOEA trials. Overall, EMODPS poses a promising method for discovering key reservoir management tradeoffs; however algorithmic choice remains a key concern for problems of increasing complexity

Informing the operations of water reservoirs over multiple temporal scales by direct use of hydro-meteorological data

“…In particular, we parameterized the operating policy as Gaussian radial basis functions, because they have been demonstrated to be effective in solving this type of multi-objective policy design problems ( Giuliani et al, 2014a;2014b ), particularly when exogenous information is directly used for conditioning the operations ( Giuliani et al, 2015 ). To perform the optimization, we use the self-adaptive Borg Multi-Objective Evolutionary Algorithm (MOEA) ( Hadka and Reed, 2013 ), which has been shown to be highly robust in solving multi-objective optimal control problems, where it met or exceeded the performance of other state-of-the-art MOEAs ( Zatarain-Salazar et al, 2016 ). Each optimization was run for 2 millions function evaluations over the evaluation horizon 2006-2013.…”

Rival framings: A framework for discovering how problem formulation uncertainties shape risk management trade‐offs in water resources systems

“…Conventionally, water resources managers have attempted to reduce the multisectoral impacts of hydrologic variability through optimized reservoir operations. Given that most river basins now contain multiple reservoirs, optimizing operations is mathematically challenging just considering the competing objectives and the high‐dimensional and stochastic nature of the multireservoir control problem [ Giuliani et al ., ; Zatarain Salazar et al ., ]. While addressing these challenges, this study also confronts the often‐ignored epistemic uncertainties surrounding how to formulate the control problem itself.…”

Climate Adaptation as a Control Problem: Review and Perspectives on Dynamic Water Resources Planning Under Uncertainty

“…However, several important research questions remain related to the computational complexity of using these methods to design and test optimal adaptation policies. The concepts presented in Figure 7 draw some inspiration from prior diagnostic studies of optimization algorithms (e.g., Reed et al, 2013;Zatarain Salazar et al, 2016), here with a specific focus on the dynamic planning problem:…”