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However, the high dimensionality of HSIs still leads to the inefficient operation of SIEAs. In addition, many SIEAs exist, but few studies have conducted a comparative analysis of them for HSI FS. Thus, our study has two goals: (1) to propose a new filter\u2013wrapper (F\u2013W) framework that can improve the SIEAs\u2019 performance; and (2) to apply ten SIEAs under the F\u2013W framework (F\u2013W\u2013SIEAs) to optimize the support vector machine (SVM) and compare their performance concerning five aspects, namely the accuracy, the number of selected bands, the convergence rate, and the relative runtime. Based on three HSIs (i.e., Indian Pines, Salinas, and Kennedy Space Center (KSC)), we demonstrate how the proposed framework helps improve these SIEAs\u2019 performances. The five aspects of the ten algorithms are different, but some have similar optimization capacities. On average, the F\u2013W\u2013Genetic Algorithm (F\u2013W\u2013GA) and F\u2013W\u2013Grey Wolf Optimizer (F\u2013W\u2013GWO) have the strongest optimization abilities, while the F\u2013W\u2013GWO requires the least runtime among the ten. The F\u2013W\u2013Marine Predators Algorithm (F\u2013W\u2013MPA) is second only to the two and slightly better than F\u2013W\u2013Differential Evolution (F\u2013W\u2013DE). The F\u2013W\u2013Ant Lion Optimizer (F\u2013W\u2013ALO), F\u2013W\u2013I-Ching Divination Evolutionary Algorithm (F\u2013W\u2013IDEA), and F\u2013W\u2013Whale Optimization Algorithm (F\u2013W\u2013WOA) have the middle optimization abilities, and F\u2013W\u2013IDEA takes the most runtime. Moreover, the F\u2013W\u2013SIEAs outperform other commonly used FS techniques in accuracy overall, especially in complex scenes.<\/jats:p>","DOI":"10.3390\/rs14133019","type":"journal-article","created":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T22:43:00Z","timestamp":1656024180000},"page":"3019","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["A Comparative Analysis of Swarm Intelligence and Evolutionary Algorithms for Feature Selection in SVM-Based Hyperspectral Image Classification"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0166-5130","authenticated-orcid":false,"given":"Yiqun","family":"Shang","sequence":"first","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8850-0912","authenticated-orcid":false,"given":"Xinqi","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3363-4072","authenticated-orcid":false,"given":"Jiayang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Beijing 100083, China"}]},{"given":"Dongya","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Beijing 100083, China"}]},{"given":"Peipei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"14118","DOI":"10.1109\/ACCESS.2018.2812999","article-title":"Modern Trends in Hyperspectral Image Analysis: A Review","volume":"6","author":"Khan","year":"2018","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"107962","DOI":"10.1016\/j.foodcont.2021.107962","article-title":"Hyperspectral image classification using CNN: Application to industrial food packaging","volume":"125","author":"Medus","year":"2021","journal-title":"Food Control"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1109\/MGRS.2017.2762087","article-title":"Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art","volume":"5","author":"Ghamisi","year":"2017","journal-title":"IEEE Geosci. 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