Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO

Abstract

The design of spacecraft trajectories for missions visiting multiple celestial bodies is here framed as a multi-objective bilevel optimization problem. A comparative study is performed to assess the performance of different Beam Search algorithms at tackling the combinatorial problem of finding the ideal sequence of bodies. Special focus is placed on the development of a new hybridization between Beam Search and the Population-based Ant Colony Optimization algorithm. An experimental evaluation shows all algorithms achieving exceptional performance on a hard benchmark problem. It is found that a properly tuned deterministic Beam Search always outperforms the remaining variants. Beam P-ACO, however, demonstrates lower parameter sensitivity, while offering superior worst-case performance. Being an anytime algorithm, it is then found to be the preferable choice for certain practical applications.

arXiv commentCode available at https://github.com/lfsimoes/beam_paco__gtoc5

References in corpus (1)

• Revisiting Lambert's Problem

Cited by in corpus (6)

• Solar-Sail Trajectory Design for Multiple Near Earth Asteroid Exploration Based on Deep Neural Networks
• A Time-Dependent TSP Formulation for the Design of an Active Debris Removal Mission using Simulated Annealing
• Asteroid Mining: ACT&Friends' Results for the GTOC 12 Problem
• The Fellowship of the Dyson Ring: ACT&Friends' Results and Methods for GTOC 11
• GTOC X: Solution Approach of Team Sapienza-PoliTo
• A New Continuous Optimization Method for Mixed Integer Space Travelling Salesman Problem