Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.envsoft.2012.02.002 | Environmental Modelling and Software |
Keywords | Field | DocType |
computational saving,benchmark problems,optimization algorithms yield,alternative algorithm,effective benchmark problem,environmental model,optimization algorithm,model pre-emption,dynamically dimensioned search algorithm,minimal computational expense,sorptive barrier design,simulation-based optimization,benchmark problem,dynamically dimensioned search,decision variable,benchmarking framework,particle swarm optimization | Particle swarm optimization,Mathematical optimization,Search algorithm,Computer science,Simulation-based optimization,Multi-objective optimization,Multi-swarm optimization,Optimal design,Optimization problem,Metaheuristic | Journal |
Volume | Issue | ISSN |
35 | C | 1364-8152 |
Citations | PageRank | References |
13 | 1.10 | 18 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| L. Shawn Matott | 1 | 32 | 4.75 |
| Bryan A. Tolson | 2 | 81 | 6.66 |
| Masoud Asadzadeh | 3 | 22 | 1.96 |