# Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO

## TL;DR

This paper introduces a novel hybrid Beam P-ACO algorithm for optimizing multi-rendezvous spacecraft trajectories, demonstrating superior performance and robustness over traditional methods in complex multi-objective celestial mission planning.

## Contribution

It develops a new hybrid Beam Search and Population-based Ant Colony Optimization algorithm and evaluates its effectiveness for multi-rendezvous spacecraft trajectory optimization.

## Key findings

- Deterministic Beam Search outperforms other variants.
- Beam P-ACO shows lower parameter sensitivity.
- Beam P-ACO is effective as an anytime algorithm.

## 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.