Evaluating Evolutionary and Gradient-Based Algorithms for Optimal Pathfinding

Pathfinding in complex topographies poses a challenge with applications extending from urban planning to autonomous navigation. While numerous algorithms offer potential solutions, their comparative efficiency and reliability when confronted with nonlinear terrains remain to be systematically evaluated. This study assesses three pathfinding algorithms—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Sequential Quadratic Programming (SQP)—to establish a basis for comparison in terms of efficiency and computational speed. Results from twenty simulations indicate that SQP achieves lower path costs and reduced computational time than GA and PSO. In particular, SQP demonstrates reduced variability in path costs and quicker convergence to optimal paths, proving more effective in nonlinear environments. These results suggest gradient-based SQP as a preferable solution for complex pathfinding tasks. The study offers a framework for algorithm selection where efficiency and promptness are critical, potentially guiding decisions in operational strategies and system architecture.