A New Continuous Optimization Method for Mixed Integer Space Travelling Salesman Problem

TL;DR

This paper introduces a novel continuous optimization approach for the complex space trajectory TSP, transforming the combinatorial problem into a gradient-based continuous problem for improved solution efficiency.

Contribution

The paper develops a continuous mapping technique and a gradient-based optimizer to solve the mixed integer space TSP, offering a new method beyond traditional combinatorial approaches.

Findings

Effective in solving space trajectory TSP with debris rendezvous.

Demonstrates competitive performance on static TSP benchmarks.

Introduces a continuous optimization framework for mixed integer problems.

Abstract

The travelling salesman problem (TSP) of space trajectory design is complicated by its complex structure design space. The graph based tree search and stochastic seeding combinatorial approaches are commonly employed to tackle the time-dependent TSP due to their combinatorial nature. In this paper, a new continuous optimization strategy for the mixed integer combinatorial problem is proposed. The space trajectory combinatorial problem is tackled using continuous gradient based method. A continuous mapping technique is developed to map the integer type ID of targets on the sequence to a set of continuous design variables. Expected flyby targets are introduced as references and used as priori to select the candidate target to fly by. Bayesian based analysis is employed to model accumulated posterior of the sequence and a new objective function with quadratic form constraints is constructed. The new introduced auxiliary design variables of expected targets together with the original design variables are set to be optimized. A gradient based optimizer is used to search optimal sequence parameter. Performances of the proposed algorithm are demonstrated through a multiple debris rendezvous problem and a static TSP benchmark.