{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:30:31Z","timestamp":1777703431298,"version":"3.51.4"},"reference-count":34,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2015,7,29]],"date-time":"2015-07-29T00:00:00Z","timestamp":1438128000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2015,7,29]]},"abstract":"<jats:p>\n                    Differential evolution algorithm (DE) has yielded promising results for solving nonlinear, non-differentiable and multi-modal optimization issues. Due to its simple structure, fast convergence and strong robustness, DE has received increasing attention and wide application in a variety of fields. We propose a novel differential evolution approach (SE-DE) which uses an external archive for opposition-based learning, by this way, more high quality solutions can be selected for candidate solutions. In addition, the mutation factor (\n                    <jats:italic>F<\/jats:italic>\n                    ) is adaptively controlled based on the success of offspring\/trial solutions generated. An optimization factor\n                    <jats:italic>\u03b1<\/jats:italic>\n                    is proposed to select the crossover strategy, a combination of binomial and exponential crossover can effectively balance the exploration and exploitation ability of the algorithm. The performance of SE-DE is compared with the other five DE algorithms including DE, SADE, ODE, NDE and MDE-pBX. The comparison is carried out for a set of 30-, 50- and 100-dimensional test functions from CEC2005. The results show that our algorithm is better than, or at least comparable to, the algorithms from other literature.\n                  <\/jats:p>","DOI":"10.3233\/ifs-151695","type":"journal-article","created":{"date-parts":[[2015,11,10]],"date-time":"2015-11-10T11:36:42Z","timestamp":1447155402000},"page":"2193-2204","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["A self-adaptive differential evolution algorithm with an external archive for unconstrained optimization problems"],"prefix":"10.1177","volume":"29","author":[{"given":"Xinqiu","family":"Zhao","sequence":"first","affiliation":[{"name":"National Engineering Research Center for Equipment and Technology of Cold Strip Rolling, Yanshan University, Qinghuangdao, Hebei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Wang","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Equipment and Technology of Cold Strip Rolling, Yanshan University, Qinghuangdao, Hebei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Sun","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Equipment and Technology of Cold Strip Rolling, Yanshan University, Qinghuangdao, Hebei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liping","family":"Wang","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Equipment and Technology of Cold Strip Rolling, Yanshan University, Qinghuangdao, Hebei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingming","family":"Ma","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Equipment and Technology of Cold Strip Rolling, Yanshan University, Qinghuangdao, Hebei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2015,8,21]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","unstructured":"Back Thomas Fogel David B. 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