Application of Hyper-heuristics in Real-World Spacecraft Trajectory Optimization under Limited Evaluation Constraints using Hyper-heuristics
Hyper-heuristics has proven to be able to solve optimization problems by utilizing its collective low-level heuristics as its component. Although hyper-heuristics has proven to be robust and does not need complex configurations when it is applied to a different domain, the application of hyper-heuristics has yet to be widely used in continuous problem domains. To the best of our knowledge, hyper-heuristics has never been tested on continuous real-world expensive optimization problems and this paper is the first presentation of hyper-heuristics in this problem domain. Thus, hyper-heuristics performance in two real-world spacecraft trajectory design problems is studied in this paper. The two problems can be split into two different models which are multiple assist (MGA) missions and multiple assists with the possibility of using deep space manoeuvres (MGADSM). The first model is a representation of spacecraft trajectory between planets, the spacecraft is equipped with chemical propulsion only for planetocentric phase thrust. The same trajectory model of a spacecraft is used in the second model but with the exception that the chemical propulsion thrust can be used once anywhere between each planet trajectory. Four algorithms DE, GA, PSO, and MVMO are tested together with hyper-heuristics tabu-search biased ranking (HHTSBR). The results obtained for the MGA model show that HHTSBR has the best results among all the algorithms tested while in the second model MGADSM, HHTSBR only manages to obtain the second-best results. From the results obtained, HHTSBR again shows its ability to perform well without major tuning towards the real-world optimization problems.