Ameliorated Snake Optimizer-Based Approximate Merging of Disk Wang-Ball Curves

Area of Science:

• Computer-Aided Geometric Design (CAGD)
• Computational Geometry
• Optimization Algorithms

Background:

• Merging curves is crucial for simplifying complex shapes in CAGD.
• Traditional methods struggle with merging Disk Wang-Ball (DWB) curves due to their complexity.
• Efficient data compression and transfer of product shapes remain a challenge.

Purpose of the Study:

• To propose an effective method for the approximate merging of DWB curves.
• To address the difficulties encountered in merging DWB curves.
• To establish an optimization model for minimizing merging errors.

Main Methods:

• Developed an approximate merging model for DWB curves, treating it as an optimization problem.
• Introduced the Bi-directional Evolutionary Snake Optimizer (BEESO) for enhanced convergence.
• BEESO integrates snake optimizer (SO) with bi-directional search, evolutionary population dynamics, and elite opposition-based learning.

Main Results:

• BEESO demonstrated effectiveness in solving the approximate merging model.
• Numerical examples validated the method's ability to minimize merging errors.
• The proposed approach offers a novel solution for DWB curve merging.

Conclusions:

• The BEESO-based method provides an efficient solution for approximate DWB curve merging.
• This technique facilitates improved compression and transfer of product shape data in CAGD.
• The study offers a new computational approach for geometric data manipulation.