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Adaptive firefly algorithm: parameter analysis and its application.

Cheung NJ, Ding XM, Shen HB - PLoS ONE (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: 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.

ABSTRACT
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.

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Different strategies for : (a) strategy , (b) strategy , (c) strategy  varying with different .
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pone-0112634-g004: Different strategies for : (a) strategy , (b) strategy , (c) strategy varying with different .

Mentions: In strategies and of (Eq. (16)–Eq. (18)), can be defined by user before the optimization. How to select is important to . We thus analyze the relationship between and , which is illustrated in Fig. 4. As shown in Fig. 4, the larger is, the larger the range of is. It is interesting to note that there is a tail in strategy at the latter of generations, which can enhance the search abilities of the fireflies in exploitation region at latter search process.


Adaptive firefly algorithm: parameter analysis and its application.

Cheung NJ, Ding XM, Shen HB - PLoS ONE (2014)

Different strategies for : (a) strategy , (b) strategy , (c) strategy  varying with different .
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4232507&req=5

pone-0112634-g004: Different strategies for : (a) strategy , (b) strategy , (c) strategy varying with different .
Mentions: In strategies and of (Eq. (16)–Eq. (18)), can be defined by user before the optimization. How to select is important to . We thus analyze the relationship between and , which is illustrated in Fig. 4. As shown in Fig. 4, the larger is, the larger the range of is. It is interesting to note that there is a tail in strategy at the latter of generations, which can enhance the search abilities of the fireflies in exploitation region at latter search process.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: 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.

ABSTRACT
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.

Show MeSH