Firefly algorithm: Difference between revisions

Revision as of 01:41, 2 May 2015

Revision as of 09:57, 24 May 2015

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Most metaheuristic algorithms may have difficulty in dealing with stochastic test functions, and it seems that firefly algorithm can deal with stochastic test functions<ref>{{cite journal |last=Yang |first=X.-S. |title=Firefly algorithm, stochastic test functions and design optimisation |journal=Int. J. Bio-inspired Computation |volume=2 |issue=2 |pages=78–84 |year=2010 |arxiv=1003.1409 }}</ref> very efficiently. In addition, FA is also better for dealing with noisy optimization problems with ease of implementation.<ref>{{cite journal |first=N. |last=Chai-ead |first2=P. |last2=Aungkulanon |first3=P. |last3=Luangpaiboon |title=Bees and firefly algorithms for noisy non-linear optimisation problems |work=Prof. Int. Multiconference of Engineers and Computer Scientists 2011 |year=2011 |volume=2 |pages=1449–1454 }}</ref><ref>{{cite journal |first=P. |last=Aungkulanon |first2=N. |last2=Chai-ead |first3=P. |last3=Luangpaiboon |title=Simulated manufacturing process improvement via particle swarm optimisation and firefly algorithms |work=Prof. Int. Multiconference of Engineers and Computer Scientists 2011 |volume=2 |pages=1123–1128 |year=2011 }}</ref>

Chatterjee et al.<ref>A. Chatterjee, G. K. Mahanti, and A. Chatterjee, Design of a fully digital controlled reconfigurable switched beam conconcentric ring array antenna using firefly and particle swarm optimization algorithm, Progress in Elelectromagnetic Research B, Vol. 36, 113-131(2012)</ref> showed that the firefly algorithm can be superior to particle swarm optimization in their applications, the effectiveness of the firefly algorithm was further tested in later studies. In addition, firefly algorithm can efficiently solve non-convex problems with complex nonlinear constraints.<ref>X. S. Yang, S. S. Hosseini, A. H. Gandomi, "Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect, ''Applied Soft Computing'', Vol. 12(3), 1180-1186(2012)</ref><ref>A. Abdullah, S. Deris, M. S. Mohamad and S. Z. M. Hashim, A new hybrid firefly algorithm for complex and nonlinear problem, in: Distributed Computing and Artificial Intelligence, Advances in Intelligent and Soft Computing, 2012, Volume 151/2012, 673-680, {{doi|10.1007/978-3-642-28765-7_81}}</ref>

Further improvement on the performance is also possible with promising results.<ref>S. M. Farahani, A. A. Abshouri, B. Nasiri, M. R. Meybodi, "Some hybrid models to improve firefly algorithm performance, ''Int. J. Artificial Intelligence'', Vol. 8 S(12), 97-117 (2012)</ref><ref>B. Nasiri, M. R. Meybodi, "Speciation-based firefly algorithm for optimization in dynamic environments, ''Int. J. Artificial Intelligence'', Vol. 8 (S12), 118-132 (2012)</ref>

== 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>I. Fister, I. Fister Jr., X. S. Yang, J. Brest, "A comprehensive review of firefly algorithms, ''Swarm and Evolutionary Computation'', vol. 13, no. 1, pp. 34-46 (2013).</ref> There are more than twenty variants:

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

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

The multiobjective firefly algorithm (MOFA) has been used for the design optimization of a 90&nbsp;nm CMOS based operational amplifier (OP-AMP) which could perform simultaneous power minimization and slew rate maximization within 500 iterations.<ref>G. Zheng, S. P. Mohanty, E. Kougianos, and O. Okobiah, “"[http://www.cse.unt.edu/~smohanty/Publications_Conferences/2013/Mohanty_ASAP2013_iVAMS-OPAMP.pdf iVAMS: Intelligent Metamodel-Integrated Verilog-AMS for Circuit-Accurate System-Level Mixed-Signal Design Exploration]”", in Proceedings of the 24th IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP), 2013, pp. 75--78.</ref>

=== Feature selection and fault detection ===

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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 ===