Adaptive firefly algorithm: parameter analysis and its application.
As a nature-inspired search algorithm, firefly algorithm (FA) has several control parameters, which may have great effects on its performance. In this study, we investigate the parameter selection and adaptation strategies in a modified firefly algorithm - adaptive firefly algorithm (AdaFa). There are three strategies in AdaFa including (1) a distance-based light absorption coefficient; (2) a gray coefficient enhancing fireflies to share difference information from attractive ones efficiently; and (3) five different dynamic strategies for the randomization parameter. Promising selections of parameters in the strategies are analyzed to guarantee the efficient performance of AdaFa. AdaFa is validated over widely used benchmark functions, and the numerical experiments and statistical tests yield useful conclusions on the strategies and the parameter selections affecting the performance of AdaFa. When applied to the real-world problem - protein tertiary structure prediction, the results demonstrated improved variants can rebuild the tertiary structure with the average root mean square deviation less than 0.4Å and 1.5Å from the native constrains with noise free and 10% Gaussian white noise.
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China.
Ngaam J
NJ
Cheung
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China.
Xue-Ming
XM
Ding
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China.
Hong-Bin
HB
Shen
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001522:Behavior, Animal / Q000502:physiology; D048888:Fireflies / Q000502:physiology; D011487:Protein Conformation; D011506:Proteins / Q000032:analysis*; D057386:Search Engine
D011506:Proteins