Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Environmental Modelling & Software
A new multi-objective algorithm, pareto archived DDS
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Environmental Modelling & Software
Demonstration of optimization techniques for groundwater plume remediation using iTOUGH2
Environmental Modelling & Software
The value of online adaptive search: a performance comparison of NSGAII, ε-NSGAII and εMOEA
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Improving PSO-Based multi-objective optimization using crowding, mutation and ∈-dominance
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A benchmarking framework for simulation-based optimization of environmental models
Environmental Modelling & Software
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A Generic Framework for Constrained Optimization Using Genetic Algorithms
IEEE Transactions on Evolutionary Computation
Dominance-Based Multiobjective Simulated Annealing
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Environmental Modelling & Software
Position paper: Characterising performance of environmental models
Environmental Modelling & Software
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A wide variety of environmental management problems are solved with a computationally intensive simulation-optimization framework. In this study, the ''model pre-emption'' strategy is introduced for increasing the efficiency of solving such multi-objective optimization problems. This strategy makes the optimization algorithm avoid the full evaluation of predictably inferior solutions, is applicable to many optimization algorithms, and does not impact the optimization results. Multi-objective pre-emption is used to optimize a new regulation plan for Lake Superior. The new plan is designed to mitigate extreme water levels and increase the total regulation benefits. The rule curve parameters defining the plan are obtained from a multi-objective, multi-scenario optimization problem. Results show that model pre-emption drastically increases the efficiency by up to 75%. The optimized regulation plan outperforms the current plan under the historical scenario. Notably, the optimized plan successfully handles an extremely dry scenario in which the current plan fails to maintain reasonable lake levels.