Experimental Evaluation of Heuristic Optimization Algorithms: A Tutorial
Journal of Heuristics
A Numerical Comparison of Some Modified Controlled Random SearchAlgorithms
Journal of Global Optimization
Improved Strategies for Radial basis Function Methods for Global Optimization
Journal of Global Optimization
Environmental Modelling & Software
Benchmarking Derivative-Free Optimization Algorithms
SIAM Journal on Optimization
Demonstration of optimization techniques for groundwater plume remediation using iTOUGH2
Environmental Modelling & Software
Expert Systems with Applications: An International Journal
Solving iTOUGH2 simulation and optimization problems using the PEST protocol
Environmental Modelling & Software
Position paper: Characterising performance of environmental models
Environmental Modelling & Software
Telescoping strategies for improved parameter estimation of environmental simulation models
Computers & Geosciences
Environmental Modelling & Software
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Simulation models assist with designing and managing environmental systems. Linking such models with optimization algorithms yields an approach for identifying least-cost solutions while satisfying system constraints. However, selecting the best optimization algorithm for a given problem is non-trivial and the community would benefit from benchmark problems for comparing various alternatives. To this end, we propose a set of six guidelines for developing effective benchmark problems for simulation-based optimization. The proposed guidelines were used to investigate problems involving sorptive landfill liners for containing and treating hazardous waste. Two solution approaches were applied to these types of problems for the first time - a pre-emptive (i.e. terminating simulations early when appropriate) particle swarm optimizer (PSO), and a hybrid discrete variant of the dynamically dimensioned search algorithm (HD-DDS). Model pre-emption yielded computational savings of up to 70% relative to non-pre-emptive counterparts. Furthermore, HD-DDS often identified globally optimal designs while incurring minimal computational expense, relative to alternative algorithms. Results also highlight the usefulness of organizing decision variables in terms of cost values rather than grouping by material type.