{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T17:01:10Z","timestamp":1765040470315,"version":"3.37.3"},"reference-count":21,"publisher":"Wiley","license":[{"start":{"date-parts":[[2014,1,1]],"date-time":"2014-01-01T00:00:00Z","timestamp":1388534400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61272119"],"award-info":[{"award-number":["61272119"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Applied Mathematics"],"published-print":{"date-parts":[[2014]]},"abstract":"<jats:p>The constrained optimization problem (COP) is converted into a biobjective optimization problem first, and then a new memetic differential evolution algorithm with dynamic preference is proposed for solving the converted problem. In the memetic algorithm, the global search, which uses differential evolution (DE) as the search scheme, is guided by a novel fitness function based on achievement scalarizing function (ASF). The novel fitness function constructed by a reference point and a weighting vector adjusts preference dynamically towards different objectives during evolution, in which the reference point and weighting vector are determined adapting to the current population. In the local search procedure, simplex crossover (SPX) is used as the search engine, which concentrates on the neighborhood embraced by both the best feasible and infeasible individuals and guides the search approaching the optimal solution from both sides of the boundary of the feasible region. As a result, the search can efficiently explore and exploit the search space. Numerical experiments on 22 well-known benchmark functions are executed, and comparisons with five state-of-the-art algorithms are made. The results illustrate that the proposed algorithm is competitive with and in some cases superior to the compared ones in terms of the quality, efficiency, and the robustness of the obtained results.<\/jats:p>","DOI":"10.1155\/2014\/606019","type":"journal-article","created":{"date-parts":[[2014,6,4]],"date-time":"2014-06-04T17:06:38Z","timestamp":1401901598000},"page":"1-15","source":"Crossref","is-referenced-by-count":8,"title":["A Memetic Differential Evolution Algorithm Based on Dynamic Preference for Constrained Optimization Problems"],"prefix":"10.1155","volume":"2014","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9502-4168","authenticated-orcid":true,"given":"Ning","family":"Dong","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuping","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2012.01.008"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1109\/4235.873238"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/S0045-7825(99)00389-8"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2003.817236"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCA.2009.2013333"},{"issue":"7","key":"7","first-page":"1243","volume":"14","year":"2003","journal-title":"Journal of Software"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2006.872344"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2007.902851"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCC.2004.841906"},{"key":"31","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2004.836819"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2010.11.033"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2011.03.010"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2011.07.049"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2011.02.008"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2013.04.028"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2010.2093582"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2008.02.014"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2012.01.017"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2012.05.013"},{"key":"25","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2012.04.012"},{"first-page":"xxii+298","year":"1999","series-title":"International Series in Operations Research & Management Science, 12","key":"28"}],"container-title":["Journal of Applied Mathematics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/jam\/2014\/606019.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/jam\/2014\/606019.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/jam\/2014\/606019.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2016,8,18]],"date-time":"2016-08-18T17:48:36Z","timestamp":1471542516000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.hindawi.com\/journals\/jam\/2014\/606019\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014]]},"references-count":21,"alternative-id":["606019","606019"],"URL":"https:\/\/doi.org\/10.1155\/2014\/606019","relation":{},"ISSN":["1110-757X","1687-0042"],"issn-type":[{"type":"print","value":"1110-757X"},{"type":"electronic","value":"1687-0042"}],"subject":[],"published":{"date-parts":[[2014]]}}}