Forgetful Swarm Optimization for Astronomical Observation Scheduling
In this paper, we propose a novel metaheuristic algorithm called Forgetful Swarm Optimization(FSO) for Astronomical Observation Scheduling (AOS), a type of combinatorial optimization problemdefined by the tasks and constraints assigned to the telescopes and other devices involved in astrophysicalres...
| Autores: | , , , , |
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| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2024 |
| País: | España |
| Recursos: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositório: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:2445/217730 |
| Acesso em linha: | https://hdl.handle.net/2445/217730 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Intel·ligència artificial Aprenentatge automàtic Algorismes computacionals Artificial intelligence Machine learning Computer algorithms |
| Resumo: | In this paper, we propose a novel metaheuristic algorithm called Forgetful Swarm Optimization(FSO) for Astronomical Observation Scheduling (AOS), a type of combinatorial optimization problemdefined by the tasks and constraints assigned to the telescopes and other devices involved in astrophysicalresearch. FSO combines local optimization, Destroy and Repair, and Swarm Intelligence methodologies tocreate a flexible and scalable global optimization algorithm to handle the challenges of AOS. The proposalis adapted to the well-justified scenarios of the Ariel Space Mission problem, a particular example of AOS,and compared with previous algorithms that are applied to it including an Evolutionary Algorithm (EA),an Iterated Local Search (ILS), a multi-start metaheuristic, a Tabu Search, and a Hill-Climbing greedyalgorithm. The experimental evaluation demonstrates that FSO consistently outperforms other algorithmsin objective completeness, up to 8.4% on average, for all instances of the problem regardless of dimensionsand complexity. Additionally, it has significantly less computational cost than ILS and the base models of aglobal optimization algorithm such as EA. |
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