The firefly algorithm is applied to the uncapacitated facility location problem which is a well known optimization problem. The light absorption coefficient parameter γ of the firefly algorithm is examined to obtain better performance and suitable values of γ are explored for the uncapacitated facility location problem. Effectiveness of local search in the firefly algorithm is also investigated. In addition, the firefly algorithm equipped with local search is compared with the artificial bee colony algorithm with respect to average relative percent error and hit to optimum rate.
JongK.D., Analysis of the behavior of a class of genetic adaptive systems, Ph.D. Thesis, University of Michigan, 1975.
6.
DorigoM., Optimization, Learning and Natural Algorithms, Ph.D. Thesis, Politecnico di Milano, 1992.
7.
GunerA.R. and SevkliM., A discrete particle swarm optimization algorithm for uncapacitated facility location problem, J of Artificial Evolution and Applications2008(10) (2008), 9.
8.
HollandJ.H., Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, A Bradford Book, 1992.
9.
KarabogaD., An Idea Based on Honey Bee Swarm for Numerical Optimization, Technical Report-TR06, Erciyes University (2005), 1–10.
10.
KennedyJ. and EberhartR., Particle Swarm Optimization, Proceedings of the IEEE international conference on neural networks (1995), 1942–1948.
11.
KennedyJ., EberhartR. and ShiY., Swarm intelligence, Academic Press, 2001.
12.
KoleA., ChakrabartiP. and BhattacharyyaS., An ant colony optimization algorithm for uncapacitated facility location problem, Artificial Intelligence and Applications1(1) (2014), 55–61.
13.
KorteB. and VygenJ., Combinatorial Optimization: Theory and Algorithms, Springer-Verlag, 2007.
14.
KraticaJ., TosicD., FilipovicV. and LjubicI., Solving the simple plant location problem by genetic algorithm, RAIRO - Operations Research35 (2001), 127–142.
15.
LewisS.M. and CratsleyC.K., Flash signal evolution, mate choice and predation in fireflies, Annual Review of Entomology53(2) (2008), 293–321.
16.
RahmanianiR. and GhaderiA., A combined facility location and network design problem with multi-type of capacitated links, Applied Mathematical Modelling37(9) (2013), 6400–6414.
17.
TuncbilekN., TasgetirenF. and EsnafS., Artificial bee colony optimization algorithm for uncapacitated facility location problems, J of Economic and Social Research14(1) (2012), 1–24.
18.
WatanabeY., TakayaM. and YamamuraA., Fitness function in ABC algorithm for uncapacitated facility location problem, Lecture Notes in Computer Science9357 (2015), 129–138.
19.
YangX.S., Biology-Derived Algorithms in Engineering Optimization, Handbook of Bioinspired Algorithms and Applications, Chapman & Hall/CRC, 2005, 589–600.
YangX.S., CuiZ., XiaoR., GandomiA.H. and KaramanogluM., Swarm Intelligence and Bio-Inspired Computation: Theory and Applications, Elsevier, 2013.
25.
YangX.S. and DebS., Cuckoo search via Lévy flights, Proceedings of the World Congress on Nature and Biologically Inspired Computing, IEEE Publications (2009), 210–214.
