Path Planning Based on Genetic Algorithm

In this paper, we employ genetic algorithms for path optimization. As an optimization technique, genetic algorithms simulate natural selection and genetic evolution processes to identify optimal solutions. Compared to alternative path optimization methods, genetic algorithms exhibit enhanced optimization capabilities by effectively escaping local optima through mechanisms like crossover, mutation, and selection operations. Our research demonstrates that genetic algorithm-based path optimization achieves superior results across multiple scenarios, particularly in complex path planning problems involving obstacle avoidance and multi-objective constraints. The implementation typically involves chromosome encoding of waypoints, fitness function design incorporating path length and smoothness metrics, and iterative population evolution. Therefore, we strongly recommend genetic algorithms as a viable methodology for path optimization tasks, especially when integrated with techniques like elite preservation and adaptive parameter tuning for improved convergence.