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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 |doi=10.1504/ijbic.2010.032124}}</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>{{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> 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>{{cite journal | last1 = Yang | first1 = X. S. | last2 = Hosseini | first2 = S. S. | last3 = Gandomi | first3 = A. H. | year = | title = Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect | url = | journal = Applied Soft Computing | volume = 12 | issue = 3| pages = 1180–1186 | doi=10.1016/j.asoc.2011.09.017}}</ref><ref>{{cite journal | last1 = Abdullah | first1 = A. | last2 = Deris | first2 = S. | last3 = Mohamad | first3 = M. S. | last4 = Hashim | first4 = S. Z. M. | year = 2012 | title = A new hybrid firefly algorithm for complex and nonlinear problem, in: Distributed Computing and Artificial Intelligence | url = | journal = Advances in Intelligent and Soft Computing | volume = 151 | issue = | pages = 673–680 | doi = 10.1007/978-3-642-28765-7_81 }}</ref>
Further improvement on the performance is also possible with promising results.<ref>{{cite journal | last1 = Farahani | first1 = S. M. | last2 = Abshouri | first2 = A. A. | last3 = Nasiri | first3 = B. | last4 = Meybodi | first4 = M. R. | year = 2012 | title = Some hybrid models to improve firefly algorithm performance | url = | journal = Int. J. Artificial Intelligence | volume = 8 | issue = S12| pages = 97–117 }}</ref><ref>{{cite journal | last1 = Nasiri | first1 = B. | last2 = Meybodi | first2 = M. R. | year = 2012 | title = Speciation-based firefly algorithm for optimization in dynamic environments | url = | journal = Int. J. Artificial Intelligence | volume = 8 | issue = S12| pages = 118–132 }}</ref>
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