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In this paper, an improved algorithm to solve the above shortcomings, called Gaussian perturbation specular reflection learning and golden\u2010sine\u2010mechanism\u2010based EHO (SRGS\u2010EHO), is proposed. First, specular reflection learning is introduced into the algorithm to enhance the diversity and ergodicity of the initial population and improve the convergence speed. Meanwhile, Gaussian perturbation is used to further increase the diversity of the initial population. Second, the golden sine mechanism is introduced to improve the way of updating the position of the patriarch in each clan, which can make the best\u2010positioned individual in each generation move toward the global optimum and enhance the global exploration and local exploitation ability of the algorithm. To evaluate the effectiveness of the proposed algorithm, tests are performed on 23 benchmark functions. In addition, Wilcoxon rank\u2010sum tests and Friedman tests with 5% are invoked to compare it with other eight metaheuristic algorithms. In addition, sensitivity analysis to parameters and experiments of the different modifications are set up. To further validate the effectiveness of the enhanced algorithm, SRGS\u2010EHO is also applied to solve two classic engineering problems with a constrained search space (pressure\u2010vessel design problem and tension\u2010\/compression\u2010string design problem). The results show that the algorithm can be applied to solve the problems encountered in real production.<\/jats:p>","DOI":"10.1155\/2021\/9922192","type":"journal-article","created":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T21:50:07Z","timestamp":1626126607000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Gaussian Perturbation Specular Reflection Learning and Golden\u2010Sine\u2010Mechanism\u2010Based Elephant Herding Optimization for Global Optimization Problems"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4780-217X","authenticated-orcid":false,"given":"Yuxian","family":"Duan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9872-7982","authenticated-orcid":false,"given":"Changyun","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5668-4689","authenticated-orcid":false,"given":"Song","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6345-0760","authenticated-orcid":false,"given":"Xiangke","family":"Guo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6548-3416","authenticated-orcid":false,"given":"Chunlin","family":"Yang","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,7,12]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/1423930"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-017-9605-z"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1111\/itor.12001"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.106018"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107085"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.114639"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.3390\/sym13020238"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/bf00113892"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.1023\/a:1008202821328"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/tevc.2008.919004"},{"key":"e_1_2_10_11_2","first-page":"15","volume-title":"Evolution Strategy: Optimization of Technical Systems by Means of Bio-Logical Evolution","author":"Rechenberg I.","year":"1973"},{"key":"e_1_2_10_12_2","doi-asserted-by":"publisher","DOI":"10.1142\/s0218213017500221"},{"key":"e_1_2_10_13_2","unstructured":"HsiaoY. 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