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However, when solving high\u2010dimensional optimization problems, the performance of GSA may deteriorate dramatically due to severe interference of redundant dimensional information in the high\u2010dimensional space. To solve this problem, this paper proposes a Manifold\u2010Guided Gravitation Search Algorithm, called MGGSA. First, based on the Isomap, an effective dimension extraction method is designed. In this mechanism, the effective dimension is extracted by comparing the dimension differences of the particles located in the same sorting position both in the original space and the corresponding low\u2010dimensional manifold space. Then, the gravitational adjustment coefficient is designed, so that the particles can be guided to move in a more appropriate direction by increasing the effect of effective dimension, reducing the interference of redundant dimension on particle motion. The performance of the proposed algorithm is tested on 35 high\u2010dimensional (dimension is 1000) benchmark functions from CEC2010 and CEC2013, and compared with eleven state\u2010of\u2010art meta\u2010heuristic algorithms, the original GSA and four latest GSA\u2019s variants, as well as three well\u2010known large\u2010scale global optimization algorithms. The experimental results demonstrate that MGGSA not only has a fast convergence rate but also has high solution accuracy. Besides, MGGSA is applied to three real\u2010world application problems, which verifies the effectiveness of MGGSA on practical applications.<\/jats:p>","DOI":"10.1155\/2024\/5806437","type":"journal-article","created":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T11:23:35Z","timestamp":1725362615000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Manifold\u2010Guided Gravitational Search Algorithm for High\u2010Dimensional Global Optimization Problems"],"prefix":"10.1155","volume":"2024","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0506-6765","authenticated-orcid":false,"given":"Fang","family":"Su","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yance","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shu","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxing","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,9,3]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/tcyb.2016.2628722"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105744"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.38.8.716"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2019.12.047"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119303"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.116332"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2021.11.051"},{"key":"e_1_2_10_8_2","first-page":"T5","article-title":"Vehicles circuits optimization by combining GPS\/GSM information with metaheuristic algorithms","volume":"23","author":"Beldjilali B.","year":"2020","journal-title":"Romanian Journal of Information Science and Technology"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107985"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2021.104479"},{"key":"e_1_2_10_11_2","volume-title":"Adaptation in Natural and Artificial Systems","author":"Holland J. 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