{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T14:25:39Z","timestamp":1776781539481,"version":"3.51.2"},"reference-count":30,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T00:00:00Z","timestamp":1715212800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Open Foundation of Key Laboratory of Software Engineering of Yunnan Province","award":["2020SE308"],"award-info":[{"award-number":["2020SE308"]}]},{"name":"Open Foundation of Key Laboratory of Software Engineering of Yunnan Province","award":["2020SE309"],"award-info":[{"award-number":["2020SE309"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>One of the limitations of the dung beetle optimization (DBO) is its susceptibility to local optima and its relatively low search accuracy. Several strategies have been utilized to improve the diversity, search precision, and outcomes of the DBO. However, the equilibrium between exploration and exploitation has not been achieved optimally. This paper presents a novel algorithm called the ODBO, which incorporates cat map and an opposition-based learning strategy, which is based on symmetry theory. In addition, in order to enhance the performance of the dung ball rolling phase, this paper combines the global search strategy of the osprey optimization algorithm with the position update strategy of the DBO. Additionally, we enhance the population\u2019s diversity during the foraging phase of the DBO by incorporating vertical and horizontal crossover of individuals. This introduction of asymmetry in the crossover operation increases the exploration capability of the algorithm, allowing it to effectively escape local optima and facilitate global search.<\/jats:p>","DOI":"10.3390\/sym16050586","type":"journal-article","created":{"date-parts":[[2024,5,10]],"date-time":"2024-05-10T03:21:04Z","timestamp":1715311264000},"page":"586","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["An Improved Dung Beetle Optimization Algorithm for High-Dimension Optimization and Its Engineering Applications"],"prefix":"10.3390","volume":"16","author":[{"given":"Xu","family":"Wang","sequence":"first","affiliation":[{"name":"School of Software, Yunnan University, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5466-7092","authenticated-orcid":false,"given":"Hongwei","family":"Kang","sequence":"additional","affiliation":[{"name":"School of Software, Yunnan University, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2430-8486","authenticated-orcid":false,"given":"Yong","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Software, Yunnan University, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingping","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Software, Yunnan University, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingyi","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Software, Yunnan University, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7305","DOI":"10.1007\/s11227-022-04959-6","article-title":"Dung beetle optimizer: A new meta-heuristic algorithm for global optimization","volume":"79","author":"Xue","year":"2022","journal-title":"J. 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