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| Format: | Recurso digital |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.17845170 |
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| _version_ | 1866901321134637056 |
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| author | Zhang, Jincheng |
| author_facet | Zhang, Jincheng |
| contents | <p><span>With the advent of the big data era in modern astronomy, astronomical observation projects have generated massive amounts of multimodal and multi-band data. Traditional optimization methods often struggle to achieve efficient, globally optimal solutions in high-dimensional, sparse, and nonlinear data analysis problems. Therefore, designing a heuristic optimization algorithm that incorporates the characteristics of astronomical data science is particularly important. This paper proposes an Astro-Informed Multi-scale Optimization (AIMO) algorithm, which innovatively introduces three mechanisms: astronomical data feature-driven optimization, multi-scale gravity-photometric fusion, and sparse event adaptive perturbation. The algorithm uses pure mathematical formulas to characterize the update process of the search particle in multi-band space, balancing global exploration and local exploitation while enhancing its responsiveness to sparse events. This paper details the algorithm structure, core mathematical formulas, and mechanism design, providing a theoretical foundation for the optimization and analysis of astronomical big data.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17845170 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Astro-Informed Multi-scale Optimization Algorithm in Astroinformatics: Mechanisms and Mathematical Framework Zhang, Jincheng <p><span>With the advent of the big data era in modern astronomy, astronomical observation projects have generated massive amounts of multimodal and multi-band data. Traditional optimization methods often struggle to achieve efficient, globally optimal solutions in high-dimensional, sparse, and nonlinear data analysis problems. Therefore, designing a heuristic optimization algorithm that incorporates the characteristics of astronomical data science is particularly important. This paper proposes an Astro-Informed Multi-scale Optimization (AIMO) algorithm, which innovatively introduces three mechanisms: astronomical data feature-driven optimization, multi-scale gravity-photometric fusion, and sparse event adaptive perturbation. The algorithm uses pure mathematical formulas to characterize the update process of the search particle in multi-band space, balancing global exploration and local exploitation while enhancing its responsiveness to sparse events. This paper details the algorithm structure, core mathematical formulas, and mechanism design, providing a theoretical foundation for the optimization and analysis of astronomical big data.</span></p> |
| title | Astro-Informed Multi-scale Optimization Algorithm in Astroinformatics: Mechanisms and Mathematical Framework |
| url | https://doi.org/10.5281/zenodo.17845170 |