Name
Abstract | ||
|---|---|---|
Finding a polygon to approximate the contour curve with theminimal approximation error εunder thepre-specified number of vertices, is termed min-εproblem. It is an important issue in image analysis and patternrecognition. A discrete version of particle swarm optimization(PSO) algorithm is proposed to solve this problem. In this method,the position of each particle is represented as a binary stringwhich corresponds to an approximating polygon. Many particles forma swarm to fly through the solution space to seek the best one. Forthose particles which fly out of the feasible region, thetraditional split and merge techniques are applied to adjust theirposition which can not only move the particles from the infeasiblesolution space to the feasible region, but also relocate it in abetter site. The experimental results show that the proposedPSO-based method has the higher performance over the GA-basedmethods. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-87732-5_98 | ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks |
Keywords | Field | DocType |
particle swarm optimization,pso-based method,forthose particle,contour curve,proposedpso-based method,abetter site,infeasiblesolution space,approximating polygon,solution space,feasible region,binary stringwhich corresponds,closed contour curves | Swarm behaviour,Feasible region,Artificial intelligence,Binary number,Particle swarm optimization,Mathematical optimization,Polygon,Vertex (geometry),Pattern recognition,Algorithm,Particle,Approximation error,Mathematics | Conference |
Volume | Issue | ISSN |
5263 LNCS | PART 1 | 16113349 |
Citations | PageRank | References |
0 | 0.34 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bin Wang | 1 | 8 | 3.91 |
| Chaojian Shi | 2 | 24 | 6.74 |
| Jing Li | 3 | 28 | 3.68 |