A Systematic Study on Solving Aerospace Problems Using Metaheuristics
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916468306739200 |
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| author | Junior, Carlos Alberto da Silva Pereira, Marconi de Arruda Passaro, Angelo |
| author_facet | Junior, Carlos Alberto da Silva Pereira, Marconi de Arruda Passaro, Angelo |
| contents | Complex engineering problems can be modelled as optimisation problems. For instance, optimising engines, materials, components, structure, aerodynamics, navigation, control, logistics, and planning is essential in aerospace. Metaheuristics are applied to solve these optimisation problems. The present paper presents a systematic study on applying metaheuristics in aerospace based on the literature. Relevant scientific repositories were consulted, and a structured methodology was used to filter the papers. Articles published until March 2022 associating metaheuristics and aerospace applications were selected. The most used algorithms and the most relevant hybridizations were identified. This work also analyses the main types of problems addressed in the aerospace context and which classes of algorithms are most used in each problem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_02574 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Systematic Study on Solving Aerospace Problems Using Metaheuristics Junior, Carlos Alberto da Silva Pereira, Marconi de Arruda Passaro, Angelo Neural and Evolutionary Computing Optimization and Control Complex engineering problems can be modelled as optimisation problems. For instance, optimising engines, materials, components, structure, aerodynamics, navigation, control, logistics, and planning is essential in aerospace. Metaheuristics are applied to solve these optimisation problems. The present paper presents a systematic study on applying metaheuristics in aerospace based on the literature. Relevant scientific repositories were consulted, and a structured methodology was used to filter the papers. Articles published until March 2022 associating metaheuristics and aerospace applications were selected. The most used algorithms and the most relevant hybridizations were identified. This work also analyses the main types of problems addressed in the aerospace context and which classes of algorithms are most used in each problem. |
| title | A Systematic Study on Solving Aerospace Problems Using Metaheuristics |
| topic | Neural and Evolutionary Computing Optimization and Control |
| url | https://arxiv.org/abs/2411.02574 |