Firefly algorithm: Difference between revisions

Revision as of 10:12, 24 May 2015

Revision as of 10:13, 24 May 2015

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== Variants of Firefly Algorithm ==

A recent, comprehensive review showed that the firefly algorithm and its variants have been used in almost all areas of science<ref>{{cite journal | last1 = Fister | first1 = I. | last2 = Fister | first2 = Jr. | last3 = Yang | first3 = X. S. | last4 = Brest | first4 = J. | year = 2013 | title = A comprehensive review of firefly algorithms | url = | journal = Swarm and Evolutionary Computation | volume = 13 | issue = 1| pages = 34–46 | doi=10.1016/j.swevo.2013.06.001}}</ref> There are more than twenty variants:

=== Discrete Firefly Algorithm (DFA) ===

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=== Hybrid Algorithms ===

A [[hybrid algorithm|hybrid]] intelligent scheme has been developed by combining the firefly algorithm with the ant colony optimization.<ref>{{cite journal | last1 = Giannakouris | first1 = G. | last2 = Vassiliadis | first2 = V. | last3 = Dounias | first3 = G. | year = 2010 | title = Experimental study on a hybrid nature-inspired algorithm for financial portfolio optimization | url = | journal = SETN | volume = 6040 | issue = | pages = 101–111 | doi=10.1007/978-3-642-12842-4_14}}</ref>

=== Firefly Algorithm Based Memetic Algorithm===

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=== Digital Image Compression and Image Processing ===

Very recently, an FF-LBG algorithm for vector quantization of digital image compression was based on the firefly algorithm, which proves to be faster than other algorithms such as [[Particle swarm optimization|PSO]]-LBG and HBMO-LBG (particle swarm optimization and honey-bee mating optimization; variations on the [[Linde–Buzo–Gray algorithm]]).<ref>{{cite journal | last1 = Horng | first1 = M.-H. | last2 = Jiang | first2 = T. W. | year = 2010 | title = The codebook design of image vector quantization based on the firefly algorithm | url = | journal = Computational Collective Intelligence, Technologies and Applications, LNCS | volume = 6423 | issue = | pages = 438–447 | doi=10.1007/978-3-642-16696-9_47}}</ref>

<ref>{{cite journal | last1 = Horng | first1 = M.-H. | year = 2011 | title = vector quantization using the firefly algorithm for image compression | url = | journal = Expert Systems with Applications | volume = 38 | issue = | page = }}</ref> For minimum cross entropy thresholding, firefly-based algorithm uses the least computation time<ref>{{cite journal | last1 = Horng | first1 = M.-H. | last2 = Liou | first2 = R.-J | year = 2011 | title = Multilevel minimum cross entropy threshold selection based on the firefly algorithm | url = | journal = Expert Systems with Applications | volume = 38 | issue = 12| pages = 14805–14811 | doi=10.1016/j.eswa.2011.05.069}}</ref> Also, for gel electrophoresis images, FA-based method is very efficient.<ref>M. H. M. Noor, A. R. Ahmad, Z. Hussain, K. A. Ahmad, A. R. Ainihayati, Multilevel thresholding of gel electrophoresis images using firefly algorithm, in: Proceedings of Control System, Computing and Engineering (ICCSCE2011), pp. 18-21 (2011).</ref>

=== Eigenvalue optimization ===

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=== Feature selection and fault detection ===

Feature selection can be also carried out successfully using firefly algorithm.<ref>{{cite journal | last1 = Banati | first1 = H. | last2 = Bajaj | first2 = M. | year = 2011 | title = Firefly based feature selection approach | url = | journal = Int. J. Computer Science Issues | volume = 8 | issue = 2| pages = 473–480 }}</ref> Real-time fault identification in large systems becomes viable, based on the recent work on fault identification with binary adaptive firefly optimization.<ref>R. Falcon, M. Almeida and A. Nayak, Fault identification with binary adaptive fireflies in parallel and distributed systems, IEEE Congress on Evolutionary Computation, (2011).</ref>

A hybrid filter-wrapper feature selection for load forecasting is proposed based on Firefly Algorithm.<ref>{{cite journal | last1 = Hu | first1 = Z. | last2 = Bao | first2 = Y. | last3 = Xiong | first3 = T. | last4 = Chiong | first4 = R. | year = 2015 | title = Hybrid filter–wrapper feature selection for short-term load forecasting | url = | journal = Engineering Applications of Artificial Intelligence | volume = 40 | issue = | pages = 17–27 | doi=10.1016/j.engappai.2014.12.014}}</ref>

===Antenna Design===

Firefly algorithms outperforms ABC for optimal design of linear array of isotropic sources <ref>{{cite journal | last1 = Basu | first1 = B. | last2 = Mahanti | first2 = G. K. | year = 2011 | title = Firefly and artificial bees colony algorithm for synthesis of scanned and broadside linear array antenna | url = | journal = Progress in Electromagnetic Research B. | volume = 32 | issue = | pages = 169–190 | doi=10.2528/pierb11053108}}</ref> and digital controllable array antenna.<ref>{{cite journal | last1 = Chatterjee | first1 = A. | last2 = Mahanti | first2 = G. K. | last3 = Chatterjee | first3 = A. | year = 2012 | title = Design of a fully digital controlled reconfigurable switched beam conconcentric ring array antenna using firefly and particle swarm optimization algorithm | url = | journal = Progress in Elelectromagnetic Research B | volume = 36 | issue = | pages = 113–131 | doi=10.2528/pierb11083005}}</ref> It has found applications in synthesis of satellite footprint patterns as well.<ref>{{cite journal | last1 = Chatterjee | first1 = Anirban | last2 = Kumar Mahanti | first2 = Gautam | last3 = Ghatak | first3 = Gourab | year = 2014 | title = Synthesis of satellite footprint patterns from rectangular planar array antenna by using swarm-based optimization algorithms | url = | journal = Int. J. Satell. Commun. Network | volume = 32 | issue = | pages = 25–47 | doi=10.1002/sat.1055}}</ref>

===Structural Design===