| _version_ | 1866901637308612608 |
|---|---|
| author | Zhang, Jincheng |
| author_facet | Zhang, Jincheng |
| contents | <p><span>With the rapid development of artificial intelligence, big data, and distributed systems, data has become a core resource for modern intelligent decision-making. However, during the collection, transmission, processing, and utilization of data, it may be affected by problems such as tampering, forgery, omission, or invalidity, which directly impacts the accuracy and reliability of decision-making results. Therefore, data provenance and trustworthiness have become a key research focus. Traditional optimization algorithms, when solving complex optimization problems, often rely on the numerical information of the objective function while ignoring the source and trustworthiness of the data. This can lead to low-quality data interfering with the search process and reducing the reliability of the optimization results.</span></p> <p> </p> <p><span>This paper proposes a novel Data Provenance-Driven Heuristic Optimization (DPDHO) algorithm. This algorithm combines data trustworthiness assessment, heuristic weighting of traceability information, and a dynamic feedback control mechanism to comprehensively consider the source and trustworthiness of data throughout the entire process of candidate solution selection, search, and iterative update. This paper details the algorithm's mathematical modeling, trustworthiness feedback loop mechanism, traceability constraint search mechanism, and weighted objective function design, and provides candidate solution update formulas and iterative processes. This algorithm features adaptive shrinkage of the search space, historical source tracing incentives, and dynamic decay of data timeliness, providing a theoretical foundation and methodological guidance for optimization problems driven by highly reliable data.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17595573 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Data Provenance-Driven Heuristic Optimization Algorithm Zhang, Jincheng <p><span>With the rapid development of artificial intelligence, big data, and distributed systems, data has become a core resource for modern intelligent decision-making. However, during the collection, transmission, processing, and utilization of data, it may be affected by problems such as tampering, forgery, omission, or invalidity, which directly impacts the accuracy and reliability of decision-making results. Therefore, data provenance and trustworthiness have become a key research focus. Traditional optimization algorithms, when solving complex optimization problems, often rely on the numerical information of the objective function while ignoring the source and trustworthiness of the data. This can lead to low-quality data interfering with the search process and reducing the reliability of the optimization results.</span></p> <p> </p> <p><span>This paper proposes a novel Data Provenance-Driven Heuristic Optimization (DPDHO) algorithm. This algorithm combines data trustworthiness assessment, heuristic weighting of traceability information, and a dynamic feedback control mechanism to comprehensively consider the source and trustworthiness of data throughout the entire process of candidate solution selection, search, and iterative update. This paper details the algorithm's mathematical modeling, trustworthiness feedback loop mechanism, traceability constraint search mechanism, and weighted objective function design, and provides candidate solution update formulas and iterative processes. This algorithm features adaptive shrinkage of the search space, historical source tracing incentives, and dynamic decay of data timeliness, providing a theoretical foundation and methodological guidance for optimization problems driven by highly reliable data.</span></p> |
| title | Data Provenance-Driven Heuristic Optimization Algorithm |
| url | https://doi.org/10.5281/zenodo.17595573 |