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{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,22]],"date-time":"2024-09-22T04:17:54Z","timestamp":1726978674462},"reference-count":22,"publisher":"European Alliance for Innovation n.o.","license":[{"start":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T00:00:00Z","timestamp":1693785600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ICST Transactions on Scalable Information Systems"],"abstract":"<jats:p>INTRODUCTION: With today's intelligent technology in full swing, many companies and enterprises need to catch up to the reality of internal management. To make the company better adapt to society and realize its sustainable development, it is essential to optimize the internal management means and improve the management efficiency of the enterprise.\r\nOBJECTIVES: To find the optimal staff allocation scheme and the best decision path by utilizing the improved fruit fly algorithm and establishing the enterprise's regular task and staff allocation model.\r\nMETHODS: We analyze the standard swarm intelligence algorithms, and then we compare the differences between the basic fruit fly algorithm, the optimized fruit fly algorithm, and the above swarm intelligence algorithms. The fruit fly algorithm is utilized, and the algorithm is optimized to fit the actual enterprise management model. At the same time, the influence of Levy's flight on the Drosophila algorithm in enterprise management efficiency improvement is studied. Finally, it points out the application fields, the optimized Drosophila algorithm's current situation, and the existing problems and shortcomings.\r\nRESULTS: By comparing the performance of the Drosophila algorithm with that of the other three optimization Drosophila algorithms, the influence factors of Levy flight on the enterprise's internal management allocation are obtained, making the algorithm a good fit for the model. The results of the optimal allocation scheme under known conditions and the best decision were obtained.\r\nCONCLUSION: The experimental results show that the optimized fruit fly algorithm can solve the multi-parameter allocation problem of enterprise management, which has high reference value and significance for general enterprise internal management.<\/jats:p>","DOI":"10.4108\/eetsis.3832","type":"journal-article","created":{"date-parts":[[2023,9,5]],"date-time":"2023-09-05T10:31:03Z","timestamp":1693909863000},"source":"Crossref","is-referenced-by-count":0,"title":["Role Mechanism of Enterprise Management Efficiency Improvement Based on Improved Drosophila Algorithm"],"prefix":"10.4108","author":[{"given":"Hui","family":"Wang","sequence":"first","affiliation":[]}],"member":"2587","published-online":{"date-parts":[[2023,9,4]]},"reference":[{"key":"31547","doi-asserted-by":"crossref","unstructured":"Akenbor, A. S., & Nwandu, P. I. Forecasting cotton lint exports in Nigeria using the autoregressive integrated moving average model. Journal of Agriculture and Food Sciences, (2021). 19(1), 150\u2013162. https:\/\/doi.org\/10.4314\/jafs.v19i1.11","DOI":"10.4314\/jafs.v19i1.11"},{"key":"31548","doi-asserted-by":"crossref","unstructured":"Akhtar, M. M., Ahamad, D., & Alamhameed, S. Optimisation algorithm-based recurrent neural network for big data classification. International Journal of Intelligent Information and Database Systems, (2021). 14(2), 153-.","DOI":"10.1504\/IJIIDS.2021.114527"},{"key":"31549","doi-asserted-by":"crossref","unstructured":"Auwul, M. R., Zhang, C., & Shahjaman, Md. A Robust Procedure for Machine Learning Algorithms Using Gene Expression Data. Biointerface Research in Applied Chemistry, (2021). 12(2), 2422\u20132439. https:\/\/doi.org\/10.33263\/BRIAC122.24222439","DOI":"10.33263\/BRIAC122.24222439"},{"key":"31550","doi-asserted-by":"crossref","unstructured":"B, O. K. A., B, F. S. A., C, C. M., & C, M. M. History, current status, and future directions of artificial intelligence\u2014ScienceDirect. Precision Medicine and Artificial Intelligence, (2021). 7(3), 1\u201338.","DOI":"10.1016\/B978-0-12-820239-5.00002-4"},{"key":"31551","doi-asserted-by":"crossref","unstructured":"Dudycz, H., Stefaniak, P., & Pyda, P. Problems and Challenges Related to Advanced Data Analysis in Multi-Site Enterprises. Vietnam Journal of Computer Science, (2021). 12(3), 1\u201317.","DOI":"10.1142\/S2196888822500063"},{"key":"31552","doi-asserted-by":"crossref","unstructured":"Haiying, W. Machine learning method based on improved drosophila optimization algorithm. International Journal of Information and Communication Technology, (2021). 2, 18.","DOI":"10.1504\/IJICT.2021.10034318"},{"key":"31553","doi-asserted-by":"crossref","unstructured":"Huang, W., & Zhang, W. Multi-objective optimization based on an adaptive competitive swarm optimizer. 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(2021). 13, 4(3), 12\u201323."},{"key":"31557","doi-asserted-by":"crossref","unstructured":"Li, C., Liang, X., Cheng, S., Wen, Y., Pan, C., Zhang, H., Chen, Y., Zhang, J., Zhang, Z., & Yang, X. A multi-environments-gene interaction study of anxiety, depression and self-harm in the UK Biobank cohort. Journal of Psychiatric Research, (2022). 147(12), 59\u201366. https:\/\/doi.org\/10.1016\/j.jpsychires.2022.01.009","DOI":"10.1016\/j.jpsychires.2022.01.009"},{"key":"31558","doi-asserted-by":"crossref","unstructured":"Li, J., Shi, W., & Yang, D. Clothing Image Classification with a Dragonfly Algorithm Optimised Online Sequential Extreme Learning Machine. Fibres & Textiles in Eastern Europe, (2021). 2(3), 29.","DOI":"10.5604\/01.3001.0014.7793"},{"key":"31559","doi-asserted-by":"crossref","unstructured":"Oszust, M., Sroka, G., & Cymerys, K. A hybridization approach with predicted solution candidates for improving population-based optimization algorithms. 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International Journal of Computer Aided Engineering and Technology,2021,13(1).","DOI":"10.1504\/IJCAET.2021.10018474"}],"container-title":["ICST Transactions on Scalable Information Systems"],"original-title":[],"link":[{"URL":"https:\/\/publications.eai.eu\/index.php\/sis\/article\/download\/3832\/2487","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/publications.eai.eu\/index.php\/sis\/article\/download\/3832\/2487","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,21]],"date-time":"2024-09-21T16:54:27Z","timestamp":1726937667000},"score":1,"resource":{"primary":{"URL":"https:\/\/publications.eai.eu\/index.php\/sis\/article\/view\/3832"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,4]]},"references-count":22,"URL":"http:\/\/dx.doi.org\/10.4108\/eetsis.3832","relation":{},"ISSN":["2032-9407"],"issn-type":[{"type":"electronic","value":"2032-9407"}],"subject":[],"published":{"date-parts":[[2023,9,4]]}}}