NURBS CURVE FITTING USING ARTIFICIAL IMMUNE SYSTEM

Non-Uniform Rational B-spline (NURBS) is an industrial standard for Computer Aided Design (CAD) model data representation. For constructing an CAD model from a physical part by curve modeling and dimensional measure, the NURBS design often results in a multi-objective optimization (MOO) problem which cannot be handled as such by traditional single objective optimization algorithms. For large data, this problem needs to be dealt with non-deterministic optimization algorithms achieving global optimum and at the same time getting to the desired solution in an iterative fashion. In order to find a good NURBS model from large number of data, generally the knots, control points and weights are respected as variables. In this paper, the minimization of the fitting error is aimed in order to find a smooth curve and the optimization of the NURBS weights and the knot vector for curve fitting is worked. The heuristic of Artificial Immune System (AIS) was used as a new methodology. The best model was searched among the candidate models by using the Akaike's Information Criteria (AIC). Numerical examples were given in order to show the efficiency of our method.