Name
Abstract | ||
|---|---|---|
Stochastic colored Petri nets are an established model for the specification and quantitative evaluation of complex systems. Automated design-space optimization for such models can help in the design phase to find good variants and parameter settings. However, since only indirect heuristic optimization based on simulation is usually possible, and the design space may be huge, the computational effort of such an algorithm is often prohibitively high. This paper extends earlier work on accuracy-adaptive simulation to speed up the overall optimization task. A local optimization heuristic in a âdivide-and-conquerâ approach is combined with varying simulation accuracy to save CPU time when the response surface contains local optima. An application example is analyzed with our recently implemented software tool to validate the advantages of the approach. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.5220/0005518000950100 | SIMULTECH |
Field | DocType | Citations |
Probabilistic-based design optimization,Mathematical optimization,Heuristic,Computer science,CPU time,Local optimum,Simulation,Simulation-based optimization,Real-time computing,Multi-swarm optimization,Local search (optimization),Metaheuristic | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christoph Bodenstein | 1 | 0 | 0.34 |
| Thomas Dietrich | 2 | 0 | 1.01 |
| Armin Zimmermann | 3 | 7 | 3.97 |