A hybrid multi-start metaheuristic scheduler for astronomical observations

In this paper, we investigate Astronomical Observations Scheduling which is a type of Multi-Objective Combinatorial Optimization Problem, and detail its specific challenges and requirements and propose the Hybrid Accumulative Planner (HAP), a hybrid multi-start metaheuristic scheduler able to adapt...

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Veröffentlicht in:Engineering applications of artificial intelligence Jg. 126; S. 106856
Hauptverfasser: Nakhjiri, Nariman, Salamó, Maria, Sànchez-Marrè, Miquel, Morales, Juan Carlos
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.11.2023
Schlagworte:
Artificial intelligence
Metaheuristic
Multi-start algorithm
Scheduling repair
Telescope scheduling
Multi-start algorithm
Metaheuristic
Telescope scheduling
Scheduling repair
Artificial intelligence
ISSN:0952-1976
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Zusammenfassung:In this paper, we investigate Astronomical Observations Scheduling which is a type of Multi-Objective Combinatorial Optimization Problem, and detail its specific challenges and requirements and propose the Hybrid Accumulative Planner (HAP), a hybrid multi-start metaheuristic scheduler able to adapt to the different variations and demands of the problem. To illustrate the capabilities of the proposal in a real-world scenario, HAP is tested on the Atmospheric Remote-sensing Infrared Exoplanet Large-survey (Ariel) mission of the European Space Agency (ESA), and compared with other studies on this subject including an Evolutionary Algorithm (EA) approach. The results show that the proposal outperforms the other methods in the evaluation and achieves better scientific goals than its peers. The consistency of HAP in obtaining better results on the available datasets for Ariel, with various sizes and constraints, demonstrates its competence in scalability and adaptability to different conditions of the problem.
ISSN:0952-1976
DOI:10.1016/j.engappai.2023.106856