Firefly algorithm: Difference between revisions

Revision as of 10:16, 24 May 2015

Revision as of 10:19, 24 May 2015

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=== Eigenvalue optimization ===

Eigenvalue optimization of isospectral systems has solved by FA and multiple optimum points have been found efficiently.<ref>R.{{cite journal | last1 = Dutta, | first1 = R. | last2 = Ganguli and| Vfirst2 = R. | last3 = Mani, | first3 = V. | year = 2011 | title = Exploring isospectral spring-mass systems with firefly algorithm, | url = http://rspa.royalsocietypublishing.org/content/early/2011/06/16/rspa.2011.0119.abstract | journal = Proc. Roy. Soc. A., | volume = 467 | issue = | page = }}</ref>

Vol. 467, (2011)http://rspa.royalsocietypublishing.org/content/early/2011/06/16/rspa.2011.0119.abstract</ref>

=== Nanoelectronic Integrated Circuit and System Design ===

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=== Protein Structure Prediction ===

BPrediction of protein structures is NP-hard, and a recent study by Maher et al.<ref>{{cite journal | last1 = Maher, A| first1 = B. | last2 = Albrecht, M| first2 = A. | last3 = Loomes, | first3 = M. | last4 = Yang | first4 = X. S. Yang,| last5 = Steinhofel | first5 = K. Steinhofel,| year = 2014 | title = A firefly-inspired method for protein structure prediction in lattice models, | url = | journal = Biomolucules, vol.| volume = 4, no.| issue = 1,| pp.pages 56-75= (2014).56–75 }}</ref> shows that firefly-based methods can speed up the predictions.

Prediction of protein structures is NP-hard, and a recent study by Maher et al.<ref>

B. Maher, A. Albrecht, M. Loomes, X. S. Yang, K. Steinhofel, A firefly-inspired method for protein structure prediction in lattice models, Biomolucules, vol. 4, no. 1, pp. 56-75 (2014).</ref> shows that firefly-based methods can speed up the predictions.

Firefly algorithm can solve two dimensional HP model. In their experiment, they took 14 sequences of different chain lengths from 18 to 100 as the dataset and compared the FA with standard genetic algorithm and immune genetic algorithm. The averaged energy convergence results show that FA achieves the lowest values.<ref>{{cite journal|last1=Yudong|first1=Zhang|last2=Lenan|first2=Wu|last3=Shuihua|first3=Wang|title=Solving Two-Dimensional HP model by Firefly Algorithm and Simplified Energy Function|journal=Mathematical Problems in Engineering|date=2013|volume=2013|doi=10.1155/2013/398141|url=http://www.hindawi.com/journals/mpe/2013/398141/|pages=1–9}}</ref>

=== Parameter Optimization of SVM ===

Firefly algorithm (FA)is applied to determine the paraemters of MSVR (Multiple-output support vector regression) in interval-valued stock price index forecasting.<ref>Tao{{cite journal | last1 = Xiong, Yukun| first1 = Tao | last2 = Bao, Zhongyi| first2 = Yukun | last3 = Hu: | first3 = Zhongyi | year = 2014 | title = Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting. Knowl.-Based| Syst.url 55: 87-100 (2014),= http://www.sciencedirect.com/science/article/pii/S0950705113003237 | journal = Knowl.-Based Syst. | volume = 55 | issue = | pages = 87–100 | doi=10.1016/j.knosys.2013.10.012}}</ref>

Meanwhile, a firefly algorithm (FA) based memetic algorithm (FA-MA) is proposed to appropriately determine the parameters of SVR forecasting model for electricity load forecasting. In the proposed FA-MA algorithm, the FA algorithm is applied to explore the solution space, and the pattern search is used to conduct individual learning and thus enhance the exploitation of FA.<ref>Zhongyi Hu, Yukun Bao, and Tao Xiong, Electricity Load Forecasting using Support Vector Regression with Memetic Algorithms, The Scientific World Journal, 2014, http://www.hindawi.com/journals/tswj/aip/292575/</ref>