Abstract
Space debris poses a potentially catastrophic risk to large-scale LEO constellations due to its substantial cascading effects, thereby rendering efficient and timely debris removal a critical research area in contemporary aerospace engineering. This paper delves into the application of the three-dimensional traveling salesman problem with orbital transfer constraints for space debris removal task planning. To overcome the challenges of sparse optimal solutions and vulnerability to local optima in addressing large-scale space debris removal task planning problems, we introduce a new Hierarchical Exploration Artificial Bee Colony (HEABC) optimization algorithm. Initially, we present an innovative two-step search strategy that employs a short-and-long term hybrid approach to optimize search time utilization and enhance solution quality, thereby addressing the sparsity of optimal solutions in the HEABC algorithm. Subsequently, to mitigate the issue of converging to local optima in high-dimensional encoding-based search problems, we devise a dual population update strategy aimed at preserving the innate evolutionary driving force of the search population. This strategy ensures the continuous updating of the population, even in the absence of intrinsic driving forces. Ultimately, experimental results substantiate that our proposed HEABC algorithm attains superior task planning sequences in a reduced time span and exhibits heightened adaptability in comparison to various traditional search algorithms. This is corroborated by numerical experiments conducted on one publicly available and one STK-generated space debris datasets.
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Data availibility
(1) Dataset Iridium33 is avaliable at http://celestrak.org/NORAD/elements/.The authors used data from May 8, 2023. It’s important to note that the amount of space debris diminishes over time, and the data on the site is constantly updated. Dataset Iridium33 from May 8, 2023 is available from the corresponding author on reasonable request. Example from: https://ieeexplore.ieee.org/document/8805506 (2) Dataset Debris20 was generated by the author during the study and is available from the corresponding author on reasonable request.
References
Bonnal C, Ruault J-M, Desjean M-C (2013) Active debris removal: recent progress and current trends. Acta Astronaut 85:51–60
Mark CP, Kamath S (2019) Review of active space debris removal methods. Space Policy 47:194–206
Forshaw JL, Aglietti GS, Fellowes S, Salmon T, Retat I, Hall A, Chabot T, Pisseloup A, Tye D, Bernal C (2020) The active space debris removal mission removedebris. Part 1: from concept to launch. Acta Astronaut 168:293–309
Aglietti GS, Taylor B, Fellowes S, Salmon T, Retat I, Hall A, Chabot T, Pisseloup A, Cox C, Mafficini A (2020) The active space debris removal mission removedebris. Part 2: in orbit operations. Acta Astronaut 168:310–322
Plotino G, Colangeli M, Özyürek T, DeDeus G, Panzetta C, Castagnola R, Grande NM, Marigo L (2021) Evaluation of smear layer and debris removal by stepwise intraoperative activation (sia) of sodium hypochlorite. Clin Oral Invest 25:237–245
Narayanaswamy S, Wu B, Ludivig P, Soboczenski F, Venkataramani K, Damaren CJ (2023) Low-thrust rendezvous trajectory generation for multi-target active space debris removal using the RQ-law. Adv Space Res 71(10):4276–4287
Federici L, Zavoli A, Colasurdo G (2019) A time-dependent tsp formulation for the design of an active debris removal mission using simulated annealing. arXiv preprint arXiv:1909.10427
Kanazaki M, Yamada Y, Nakamiya M (2020) Performance of space debris removal satellite considering total thrust by evolutionary algorithm. In: 2020 IEEE aerospace conference, IEEE, pp 1–9
Barea A, Urrutxua H, Cadarso L (2020) Large-scale object selection and trajectory planning for multi-target space debris removal missions. Acta Astronaut 170:289–301
Izzo D, Getzner I, Hennes D, Simões LF (2015) Evolving solutions to TSP variants for active space debris removal. In: Proceedings of the 2015 annual conference on genetic and evolutionary computation, pp 1207–1214
Chen S, Jiang F, Li H, Baoyin H (2021) Optimization for multitarget, multispacecraft impulsive rendezvous considering J2 perturbation. J Guid Control Dyn 44(10):1811–1822
Yang J, Hu YH, Hou X, Huang H, Zhao N, Fan H (2023) A multi-platform active debris removal mission planning method based on DCOP with chain topology. Acta Astronaut 203:495–509
Zhang N, Zhang Z, Baoyin H (2021) Timeline club: an optimization algorithm for solving multiple debris removal missions of the time-dependent traveling salesman problem model. Astrodynamics 6:1–16
Zhang L, Zhou M, Yang F et al (2023) Elastic tracking operation method for high-speed railway using deep reinforcement learning. IEEE Trans Consum Electron. https://doi.org/10.1109/TCE.2023.3245334
Tong X, Ma D, Wang R, Xie X, Zhang H (2023) Dynamic event-triggered-based integral reinforcement learning algorithm for frequency control of microgrid with stochastic uncertainty. IEEE Trans Consum Electron. https://doi.org/10.1109/TCE.2023.3241684
Qi X, Gan Z, Liu C, Xu Z, Zhang X, Li W, Ouyang C (2021) Collective intelligence evolution using ant colony optimization and neural networks. Neural Comput Appl 33:12721–12735
Nguyen T-H, Jung JJ (2021) Multiple ACO-based method for solving dynamic MSMD traffic routing problem in connected vehicles. Neural Comput Appl 33:6405–6414
Arık OA (2021) Artificial bee colony algorithm including some components of iterated greedy algorithm for permutation flow shop scheduling problems. Neural Comput Appl 33(8):3469–3486
Li H, Baoyin H (2019) Optimization of multiple debris removal missions using an evolving elitist club algorithm. IEEE Trans Aerosp Electron Syst 56(1):773–784
Medioni L, Gary Y, Monclin M, Oosterhof C, Pierre G, Semblanet T, Comte P, Nocentini K (2023) Trajectory optimization for multi-target Active Debris Removal missions[J]. Adv Space Res 72(7):2801–2823
Yang J, Hu YH, Liu Y, Pan Q (2018) A maximal-reward preliminary planning for multi-debris active removal mission in LEO with a greedy heuristic method. Acta Astronaut 149:123–142
Zhang Y, Mei Y, Huang S, Zheng X, Zhang C (2022) A route clustering and search heuristic for large-scale multidepot-capacitated arc routing problem. IEEE Trans Cybern 52(8):8286–8299. https://doi.org/10.1109/TCYB.2020.3043265
Gao X, Chen MZQ, Zhang L (2022) A new edge removal strategy for complex networks based on an improved tabu search algorithm. In: 2022 41st Chinese control conference (CCC), pp 965–970. https://doi.org/10.23919/CCC55666.2022.9902152
Xiao J, Zhang T, Du J, Zhang X (2021) An evolutionary multiobjective route grouping-based heuristic algorithm for large-scale capacitated vehicle routing problems. IEEE Trans Cybern 51(8):4173–4186. https://doi.org/10.1109/TCYB.2019.2950626
He X, Zhou Y, Chen Z, Zhang J, Chen W-N (2021) Large-scale evolution strategy based on search direction adaptation. IEEE Trans Cybern 51(3):1651–1665. https://doi.org/10.1109/TCYB.2019.2928563
Li H, Baoyin H (2021) Sequence optimization for multiple asteroids rendezvous via cluster analysis and probability-based beam search. Sci China Technol Sci 64(1):122–130
Alipour MM, Razavi SN, Feizi Derakhshi MR, Balafar MA (2018) A hybrid algorithm using a genetic algorithm and multiagent reinforcement learning heuristic to solve the traveling salesman problem. Neural Comput Appl 30:2935–2951
Biswas A, Tripathy SP, Pal T (2022) On multi-objective covering salesman problem. Neural Comput Appl 34:1–14
Sahin M (2023) Solving TSP by using combinatorial bees algorithm with nearest neighbor method. Neural Comput Appl 35(2):1863–1879
Pandiri V, Singh A, Rossi A (2020) Two hybrid metaheuristic approaches for the covering salesman problem. Neural Comput Appl 32:15643–15663
Wu Z, Wu J, Zhao M, Feng L, Liu K (2021) Two-layered ant colony system to improve engraving robot’s efficiency based on a large-scale tsp model. Neural Comput Appl 33:6939–6949
Wu J, Yang H, Zeng Y, Wu Z, Liu J, Feng L (2023) A twin learning framework for traveling salesman problem based on autoencoder, graph filter, and transfer learning. IEEE Trans Consum Electron. https://doi.org/10.1109/TCE.2023.3269071
Liu Z, Li X, Khojandi A (2022) The flying sidekick traveling salesman problem with stochastic travel time: a reinforcement learning approach. Transp Res Part E Logist Transp Rev 164:102816
Zhu X, Qiu T, Qu W, Zhou X, Wang Y, Wu O (2021) Path planning for adaptive CSI map construction with A3C in dynamic environments. IEEE Trans Mob Comput. https://doi.org/10.1109/TMC.2021.3131318
Diallo M, Quintero A, Pierre S (2019) An efficient approach based on ant colony optimization and tabu search for a resource embedding across multiple cloud providers. IEEE Trans Cloud Comput 9(3):896–909
İlhan İ, Gökmen G (2022) A list-based simulated annealing algorithm with crossover operator for the traveling salesman problem. Neural Comput Appl 34:1–26
Zhang J (2021) An improved genetic algorithm with 2-opt local search for the traveling salesman problem. In: application of intelligent systems in multi-modal information analytics: 2021 international conference on multi-modal information analytics (MMIA 2021), vol 2. Springer, pp 404–409
Khan I, Maiti MK, Basuli K (2020) Multi-objective traveling salesman problem: an ABC approach. Appl Intell 50:3942–3960
Shehab M, Abualigah L, Al Hamad H, Alabool H, Alshinwan M, Khasawneh AM (2020) Moth-flame optimization algorithm: variants and applications. Neural Comput Appl 32:9859–9884
Abualigah L, Diabat A, Mirjalili S, Abd Elaziz M, Gandomi AH (2021) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609
Stützle T, Hoos HH (2000) Max–min ant system. Futur Gener Comput Syst 16(8):889–914
Yu C, Cai Z, Ye X, Wang M, Zhao X, Liang G, Chen H, Li C (2020) Quantum-like mutation-induced dragonfly-inspired optimization approach. Math Comput Simul 178:259–289
Van der Maaten L, Hinton G (2008) Visualizing data using t-SNE[J]. J Mach Learn Res 9(11)
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Funding was provided by Science Center Program of the National Natural Science Foundation of China (Grant No. 62188101) and National Natural Science Foundation of China (Grant No. 61833009)
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Xia, Q., Qiu, S., Liu, M. et al. Task planning of space debris removal based on a hierarchical exploration artificial bee colony algorithm. Neural Comput & Applic 36, 6597–6612 (2024). https://doi.org/10.1007/s00521-023-09399-8
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DOI: https://doi.org/10.1007/s00521-023-09399-8