Adjusted Harris Hawks Optimization for the Minimum Weight Triangulation

Triangulation is a vital concern in computational geometry, and it presents the base in work with complex geometric objects. This issue is utilized in diverse fields, such as terrain modelling, finite element mesh generation, image processing, and computer vision. Constructing triangulated random network models of land contours is a well-known NP-hard problem called Minimum Weight Triangulation (MWT). As it is an NP-hard problem, the time required for an exhaustive search technique proliferates as soon as the number of points on a plane increases. In order to solve this problem, nature-inspired swarm intelligence algorithms are being exploited as efficient optimization techniques. In this paper, we have adapted the recently devised Harris Hawks Optimization (HHO) approach for seeking the minimum-weight triangulation of a planar point set. We have experimented with our adjusted HHO approach on various randomly generated instances of 2D points, and the outcomes indicate that our method is robust. Our approach performs better in almost all cases than other nature-inspired algorithms.