Name
Title | ||
|---|---|---|
Research on flatness errors evaluation based on artificial fish swarm algorithm and Powell method |
Abstract | ||
|---|---|---|
In this paper, based on the analysis of existent evaluation methods for form errors, a hybrid evaluation method is provided. The optimum model and the calculation process are introduced in detail. The hybrid optimisation algorithm based on artificial fish swarm algorithm AFSA and Powell optimisation method. As a new heuristic intelligent optimisation algorithm, AFSA has better performances such as good global convergence, strong robustness, insensitive to initial values, simplicity of implementation and faster convergent speed with random initial values compared with genetic algorithm. The Powell method is a classical powerful local descent algorithm, and its advantages are simple and efficient. By integrating Powell optimisation search the precision of AFSA optimisation result is evidently improved. The flatness error is discussed as an example. Finally, a control experiment is carried out, and the simulation result shows that the hybrid evaluation method is feasible and satisfactory in the evaluation of flatness errors. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1504/IJCSM.2013.058060 | IJCSM |
Keywords | Field | DocType |
metrology, flatness error, minimum zone solution, artificial fish swarm optimisation algorithm, AFSA, Powell method | Convergence (routing),Flatness (systems theory),Mathematical optimization,Heuristic,Swarm behaviour,Metrology,Algorithm,Robustness (computer science),Genetic algorithm,Mathematics | Journal |
Volume | Issue | ISSN |
4 | 4 | 1752-5055 |
Citations | PageRank | References |
3 | 0.43 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ke Zhang | 1 | 7 | 6.11 |
| Jianping Luo | 2 | 6 | 1.22 |