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Recent studies shows that the [[firefly algorithm]] is very efficient<ref>X. S. Yang, Firefly algorithms for multimodal optimization, in: Stochastic Algorithms: Foundations and Applications, SAGA 2009, Lecture Notes in Computer Sciences, Vol. 5792, pp. 169-178 (2009). eprint at http://arxiv.org/abs/1003.1466 </ref>, and could outperform other metaheuristic algorithms including [[particle swarm optimization]].<ref>S. Lukasik and S. Zak, Firefly algorithm for continuous constrained optimization task, ICCCI 2009, Lecture Notes in Artificial Intelligence (Eds. N. T. Ngugen, R. Kowalczyk, S. M. Chen), Vol. 5796, 97-100 (2009).</ref>
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>Yang,X.-S., Firefly algorithm, stochastic test functions and design optimisation, Int. J. Bio-inspired Computation, Vol. 2, No. 2, pp. 78-84 (2010). eprint at http://arxiv.org/abs/1003.1409 </ref> very efficiently.
== Discrete Firefly Algorithm (DFA) ==
A discrete version of Firefly Algorithm, namely, Discrete Firefly Algorithm (DFA) proposed recently by M. K. Sayadi, R. Ramezanian and N. Ghaffari-Nasab can efficiently solve NP-hard scheduling problems<ref>M. K. Sayadi, R. Ramezanian and N. Ghaffari-Nasab, A discrete firefly meta-heuristic with local search for makespan minimization in permutation flow shop scheduling problems http://growingscience.com/ijiec/VOL1/IJIEC_2010_7.pdf</ref> DFA outperforms existing algorithms such as the ant colony algorithm.
==See also==
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