Name
Title | ||
|---|---|---|
Improving the efficiency of evolutionary algorithms for large-scale optimization with multi-fidelity models. |
Abstract | ||
|---|---|---|
Large scale optimization problems are often with complex systems and large solution space, which significantly increase their computing cost. The idea of ordinal transformation (OT) is proposed in the method MO2TOS which can improve the efficiency of solving optimization problems with limited scale solution space by using multi-fidelity models. In this paper, we integrate OT with evolutionary algorithms to speed up the solving of large-scale problems. Evolutionary algorithms are employed to search the solutions of low-fidelity model from a large solution space and provide a good direction to the OT procedure. Meanwhile, evolutionary algorithms need to determine how to select solutions from multi-fidelity models after the OT procedure to update the next generation. We theoretically show the improvement by using multi-fidelity models and employ genetic algorithm (GA) as an example to exhibit the detailed implementation procedure. The numerical experiments demonstrate that the new method can lead to significant improvement. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/WSC.2016.7822144 | Winter Simulation Conference |
Keywords | Field | DocType |
large scale optimization,complex systems,ordinal transformation,MO2TOS,optimization,multi-fidelity models,OT,low-fidelity model,genetic algorithm | Complex system,Mathematical optimization,Fidelity,Evolutionary algorithm,Computer science,Evolutionary computation,Evolutionary programming,Optimization problem,Genetic algorithm,Speedup | Conference |
ISBN | Citations | PageRank |
978-1-5090-4487-0 | 0 | 0.34 |
References | Authors | |
3 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| chunchih chiu | 1 | 9 | 2.14 |
| Si Zhang | 2 | 49 | 6.18 |
| J. T. Lin | 3 | 11 | 2.56 |
| Lu Zhen | 4 | 320 | 29.76 |
| Edward Huang | 5 | 64 | 7.87 |