{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T15:29:02Z","timestamp":1773761342896,"version":"3.50.1"},"reference-count":21,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2012,11,11]],"date-time":"2012-11-11T00:00:00Z","timestamp":1352592000000},"content-version":"vor","delay-in-days":315,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"funder":[{"DOI":"10.13039\/501100004595","name":"Universiti Sains Malaysia","doi-asserted-by":"publisher","award":["304\/PMATHS\/6311126"],"award-info":[{"award-number":["304\/PMATHS\/6311126"]}],"id":[{"id":"10.13039\/501100004595","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Applied Mathematics"],"published-print":{"date-parts":[[2012,1]]},"abstract":"<jats:p>Firefly algorithm is one of the new metaheuristic algorithms for optimization problems. The algorithm is inspired by the flashing behavior of fireflies. In the algorithm, randomly generated solutions will be considered as fireflies, and brightness is assigned depending on their performance on the objective function. One of the rules used to construct the algorithm is, a firefly will be attracted to a brighter firefly, and if there is no brighter firefly, it will move randomly. In this paper we modify this random movement of the brighter firefly by generating random directions in order to determine the best direction in which the brightness increases. If such a direction is not generated, it will remain in its current position. Furthermore the assignment of attractiveness is modified in such a way that the effect of the objective function is magnified. From the simulation result it is shown that the modified firefly algorithm performs better than the standard one in finding the best solution with smaller CPU time.<\/jats:p>","DOI":"10.1155\/2012\/467631","type":"journal-article","created":{"date-parts":[[2012,11,11]],"date-time":"2012-11-11T21:01:26Z","timestamp":1352667686000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":72,"title":["Modified Firefly Algorithm"],"prefix":"10.1155","volume":"2012","author":[{"given":"Surafel Luleseged","family":"Tilahun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong Choon","family":"Ong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2012,11,11]]},"reference":[{"key":"e_1_2_7_1_2","first-page":"41","article-title":"Load optimization in training the olympic rowing (Skiff) champion from Beijing, 2008: case study","volume":"5","author":"Svilen N.","year":"2011","journal-title":"Serbian Journal of Sports Sciences"},{"key":"e_1_2_7_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2010.2101069"},{"key":"e_1_2_7_3_2","doi-asserted-by":"publisher","DOI":"10.1002\/9780470549124"},{"key":"e_1_2_7_4_2","volume-title":"Subgame Consistent Economic Optimization: An Advanced Cooperative Dynamic Game Analysis","author":"Yeung D. 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