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Because of its excellent performance, DE variants can be applied in a wide range of applications in science and engineering. However, the performance of DE is sensitive to the choice of trial vector generation strategy and the associated control parameters. Therefore, it is necessary to choose appropriate mutation strategy and control parameters when tackling optimization applications. In this paper, an adaptive update mechanism is proposed to update control parameters\n                    <jats:italic>F<\/jats:italic>\n                    and\n                    <jats:italic>Cr<\/jats:italic>\n                    . The experimental results are verified on the CEC 2013 test suite which contains 28 benchmark functions for the evaluation of single objective real parameter optimization. The proposed algorithm is compared with jDE, iwPSO and ccPSO, and experiment results show its good performance.\n                  <\/jats:p>","DOI":"10.3233\/jifs-179665","type":"journal-article","created":{"date-parts":[[2020,2,28]],"date-time":"2020-02-28T11:15:55Z","timestamp":1582888555000},"page":"5775-5786","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":7,"title":["A parameter adaptive DE algorithm on real-parameter optimization"],"prefix":"10.1177","volume":"38","author":[{"given":"Jeng-Shyang","family":"Pan","sequence":"first","affiliation":[{"name":"College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China"},{"name":"Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cheng","family":"Yang","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence, Fujian University of Technology, Fuzhou, China"},{"name":"Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fanjia","family":"Meng","sequence":"additional","affiliation":[{"name":"Guanzhuang Central Primary School of Zhangqiu District, Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxin","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence, Fujian University of Technology, Fuzhou, China"},{"name":"Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenyu","family":"Meng","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence, Fujian University of Technology, Fuzhou, China"},{"name":"Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2020,2,28]]},"reference":[{"issue":"1","key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TEVC.2010.2059031","article-title":"Differential evolution: A survey of the state-of-the-art","volume":"15","author":"Das S.","year":"2010","unstructured":"DasS. and SuganthanP.N., Differential evolution: A survey of the state-of-the-art, IEEE Transactions on Evolutionary Computation15(1) (2010), 4\u201331.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.01.006"},{"key":"e_1_3_2_4_2","first-page":"1942","volume-title":"Proceedings of the IEEE international conference on neural networks","volume":"4","author":"Eberhart R.","year":"1995","unstructured":"EberhartR. and KennedyJ., Particle swarm optimization. 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