Clustering-Based Successive Search Space Pruning in Low-Thrust Transfer Trajectory Optimization Problems

Abstract

The optimization of low-thrust transfer trajectories presents a significant computational challenge due to the vast search space involved. This paper introduces a novel clustering-based successive search space pruning technique to improve low-thrust trajectory optimization. Regions of low ∆V trajectories were extracted from the search space using DBSCAN. Through successive pruning of regions of the search space containing inefficient solutions, effectively zooming in on the most promising areas, the performance of global optimization algorithms was improved. The effectiveness of the search space pruning was evaluated using three interplanetary transfer test cases: Earth-Mars, Earth-Tempel 1, and Earth-Mercury. The performance of three global optimization algorithms, Differential Evolution (DE), Simple Genetic Algorithm (SGA), and Particle Swarm Optimization (PSO) was compared across the original and pruned search spaces. Results show that clustering-based pruning can significantly reduce the search space and subsequently lead to improved optimal transfer trajectories with fewer function evaluations. Differential Evolution outperformed PSO and SGA across all three test cases, demonstrating robustness and efficiency. The results of the optimization highlight the importance of the selection of the population size and number of generations for the optimization algorithms, because insufficient function evaluations resulted in poor convergence for PSO and SGA, including in the reduced search spaces. The results also suggest excessive pruning of the search space can lead to loss of optimal solution. The findings confirmed that clustering-based search space pruning is a promising technique for improving the efficiency of global optimization in low-thrust trajectory design.