# B-Spline Curve Fitting of Hungry Predation Optimization on Ship Line Design
## 2 Related Works
### Research Status  
At present, many companies have developed relatively mature ship CAD software systems such as AVEVA's TRIBON Solution, DASSAULT's Catia, NAPA's NAPA system, etc. In addition, [PERSON] [25], [PERSON] [26] and [PERSON] et al. [27] also developed a special ship design system. The dedicated ship CAD system effectively improves the efficiency and quality of ship design. However, it has not achieved a better solution to the problem of discrete point processing in the offset table. For example, in the research on the reconstruction of the segmented outer plate of the ship surface, [PERSON] et al. [28] carried out the rapid inverse calculation of the non-uniform B-spline curve, but the inverse calculation process is to interpolate the discrete points, which cannot avoid errors. Nowadays, researchers prefer B-spline approximation. Moreover, the knot placement problem is a popular content that has been studied by many scholars in recent years [29]. The existing research methods can be divided into the static knot method and dynamic knot method according to whether the knot can be adjusted after selection. We first introduce the relevant static knot methods. [PERSON] and [PERSON] [23] first proposed an averaging technique and knot placement technique (KTP) in 1978, and knots are selected to reflect the distribution of data points. On this basis, [PERSON] and [PERSON] put forward NKTP technology [30]. [PERSON] proposed a knot removal method (KRM) in 1988 [31; 32]. However, this method cannot capture the internal characteristics of data points, and the amount of calculation is huge. In 1999, [PERSON] [33] proposed to use the shape information of data points for knot placement. In the research method of [PERSON] and [PERSON] [34], the idea of knot adaptation was proposed. [PERSON], [PERSON] et al. [35] further proposed a feature point including the crease point, inflection point and curvature maximum point. The influence of curvature on fitting has also become a more concerned factor [36; 37; 38; 39]. The static knot method is usually simple to calculate and has higher efficiency; the knot calculation comes from artificial assumptions and cannot be moved after placement. Compared with the dynamic knot method, the static knot method requires more knots under the same precision requirement. The dynamic knot method is mainly a combination of B-spline curve fitting theory and optimization algorithm, and the overall algorithm performance largely depends on the optimization algorithm. There are hundreds of existing optimization algorithms, and the nature-inspired optimization algorithm [40; 41; 42; 43; 44; 45; 46; 47] is one of the most practical branches. According to the algorithm principle, it can be divided into three categories, as shown in Figure 1. [PERSON] et al. [17] and [PERSON] [18] proposed the application of genetic algorithm. [PERSON] et al. [19] combined the grey wolf algorithm. [PERSON] et al. [20] combined the differential evolution algorithm. [PERSON] et al. [21] combined the invasive weed optimization (IWO). [PERSON] et al. [22] proposed the combination of immune algorithm and B-spline curve fitting and improved the parameter optimization and algorithm complexity. The dynamic knot method realizes the adaptive adjustment of knot dimension and position in each iteration; it does not rely on artificial assumptions and carries out forward calculation through multiple groups of initial random knots. The fitting effect of dynamic knot method is often better than the static knot method, but its operation efficiency is low.