Firefly algorithm with chaos

► Novel Chaotic Improved Firefly Algorithms (CFAs) are presented for global optimization. ► Twelve different chaotic maps are utilized to improve the attraction term of the algorithm. ► Comparing the new chaotic algorithms with the standard FA demonstrates superiority of the CFAs for the benchmark f...

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Bibliographic Details
Published inCommunications in nonlinear science & numerical simulation Vol. 18; no. 1; pp. 89 - 98
Main Authors Gandomi, A.H., Yang, X.-S., Talatahari, S., Alavi, A.H.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.01.2013
Subjects
Algorithms
Attraction
Benchmarking
Chaos
Chaos theory
Fireflies
Firefly algorithm
Global optimization
Metaheuristic algorithm
Optimization
Chaos
Global optimization
Metaheuristic algorithm
Firefly algorithm
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Summary:► Novel Chaotic Improved Firefly Algorithms (CFAs) are presented for global optimization. ► Twelve different chaotic maps are utilized to improve the attraction term of the algorithm. ► Comparing the new chaotic algorithms with the standard FA demonstrates superiority of the CFAs for the benchmark functions. A recently developed metaheuristic optimization algorithm, firefly algorithm (FA), mimics the social behavior of fireflies based on the flashing and attraction characteristics of fireflies. In the present study, we will introduce chaos into FA so as to increase its global search mobility for robust global optimization. Detailed studies are carried out on benchmark problems with different chaotic maps. Here, 12 different chaotic maps are utilized to tune the attractive movement of the fireflies in the algorithm. The results show that some chaotic FAs can clearly outperform the standard FA.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1007-5704
1878-7274
DOI:10.1016/j.cnsns.2012.06.009