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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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In strategy , the relationship between  and the population size  varying with different power value .
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pone-0112634-g005: In strategy , the relationship between and the population size varying with different power value .

Mentions: In strategy and strategy , the values of are dependent on the population size and the size of an optimization problem, and we also analyze the relationship among them. As illustrated in Fig. 5, in strategy the smaller is, the more similar the trajectories of are. As well as strategy –, the range of is enlarged with the increment of the value of and population size . In strategy , as shown in Fig. 6, although the trajectories of are different from each other varying with , they all converge to similar points. These points are independent of the population size .


Adaptive firefly algorithm: parameter analysis and its application.

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

In strategy , the relationship between  and the population size  varying with different power value .
© Copyright Policy
Related In: Results  -  Collection

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

pone-0112634-g005: In strategy , the relationship between and the population size varying with different power value .
Mentions: In strategy and strategy , the values of are dependent on the population size and the size of an optimization problem, and we also analyze the relationship among them. As illustrated in Fig. 5, in strategy the smaller is, the more similar the trajectories of are. As well as strategy –, the range of is enlarged with the increment of the value of and population size . In strategy , as shown in Fig. 6, although the trajectories of are different from each other varying with , they all converge to similar points. These points are independent of the population size .

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