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The SMFO is inspired by a moth swarm encircling a flame at night, in which each flame is simultaneously encircled by multiple moths for a greater exploitation, whereas the flame with a higher brightness (i.e. a smaller fitness function) will attract more moths than those of its adjacent flames. In order to achieve a wider exploration, a ring network is then constructed among the flames such that the moths may be guided to search for a brighter flame more effectively. Three case studies are undertaken, which verify that an improved global convergence, more optimal power tracking and enhanced FRT capability may be achieved by SMFO compared with those of existing meta-heuristic techniques.<\/jats:p>","DOI":"10.1177\/0142331217712091","type":"journal-article","created":{"date-parts":[[2017,6,26]],"date-time":"2017-06-26T10:40:41Z","timestamp":1498473641000},"page":"1491-1503","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":15,"title":["Optimal power tracking of doubly fed induction generator-based wind turbine using swarm moth\u2013flame optimizer"],"prefix":"10.1177","volume":"41","author":[{"given":"LN","family":"Huang","sequence":"first","affiliation":[{"name":"College of Electric Power, South China University of Technology, 510640 Guangzhou, 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