Swarm vs Evolutionary Algorithms for Path Optimization on Lunar Surfaces: A Metaheuristic Approach

Path optimization is a critical challenge in autonomous lunar exploration, where efficient traversal of rugged terrain is vital for mission success. Compared to other celestial bodies, the lunar environment presents unique challenges such as extreme temperature variations, high radiation levels, and a lack of atmosphere, making it a critical focus for autonomous exploration. Additionally, unlike other distant planets, the Moon’s proximity to Earth and its potential for future human missions makes it a priority for path optimization research. Traditional machine learning algorithms often fall short in this domain due to their dependency on large datasets and tendency to over-fit. They are incompetent when dealing with dynamically changing environments like lunar terrain. These algorithms also struggle with the unpredictability of rugged terrains, which demand real-time adaptability and robustness—qualities that machine learning techniques often lack. In this study, we address these limitations by employing meta-heuristic algorithms, specifically Differential Evolution (DE), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO). Lunar terrains, characterized by craters, sharp slopes, and high-cost traversal areas, require algorithms like DE, which uses mutation and recombination for exploration, PSO for efficient search through particle velocity updates, and ACO for intelligent path selection based on pheromone trails. Applying these methods to a simulated lunar dataset with varying terrain costs, we compare their effectiveness in path-finding, emphasizing their ability to minimize traversal costs while navigating complex, uncertain environments more reliably than traditional approaches.