Performance Comparison of Optimization Algorithms for Shortest Path Determination on Facial Edge Coordinates

Optimization is a computational process to find the best solution by minimizing or maximizing an objective function. This research compares the performance of three optimization algorithms Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA) in determining the shortest path optimization between coordinate points extracted from detected facial edge images. Using the Canny edge detection method, 10 single facial images with a resolution of 120 x 120 pixels were processed to produce (X, Y) coordinates as input for the optimization process. The results show that GA consistently outperforms the other methods, achieving the shortest total distance in 9 out of 10 datasets. The results of the first image data comparison, the GA method produced the shortest total distance with a value of 18,199.59 pixels, while (SA) 21,072.28 pixels had a performance 15.78% worse than GA, and (PSO) 28,381.58 (pixels) showed a performance 55.94% worse than GA. SA showed competitive and optimal results in one dataset, while PSO consistently provided the longest distance, indicating its lower efficiency in this research.