摘要
B样条曲线拟合问题中,将节点作为自由变量可大幅提高拟合精度,但这就使曲线拟合问题转化为求解困难的连续多峰值、多变量非线性优化问题,当待拟合的曲线是不连续、有尖点情况,就更为困难。针对这一问题,基于混沌蚂蚁群优化算法CASO,提出了一种新的B样条曲线拟合算法CASO-DF。该算法结合B样条曲线拟合原理,通过蚁群中蚂蚁个体的混沌行为,调整自由节点位置,通过蚁群的自组织行为自适应地调整内部节点数目,解决了B样条曲线拟合问题。仿真结果表明了CASO-DF算法能够有效实现自由节点B样条曲线拟合,且性能优于其他同类算法。
Data fitting through B-splines improves the accuracy of the solution dramatically if the knots are treated as free variables. However, in this case the problem becomes a very difficult continuous multimodal and multivariate nonlinear optimization problem, especially the unknown functions are discontinuous and cusps. To this end, a Chaotic Ant Swarm Optimization（CASO）based curve fitting with B-splines, called CASO-DF, is proposed to implement the smoothness fitting quickly. The approach is devised based on the curve fitting with B-splines using chaotic coordination of a single ant and self-organizing capacity of the whole ant colony. CASO-DF can adaptively adjust knots placement and choose the number of internal knots. Simulation results show that the proposed approach can perform effectively as well as efficiently, and this algorithm has better performance than other similar algorithms.
出处
《计算机工程与应用》
CSCD
2014年第16期177-182,264,共7页
Computer Engineering and Applications
基金
安徽省自然科学基金(No.11040606M151)
