Abstract
Firefly Algorithm (FA) is one of the recent swarm intelligence methods developed by Xin-She Yang in 2008 [12]. FA is a stochastic, nature-inspired, meta-heuristic algorithm that can be applied for solving the hardest optimization problems. The main goal of this paper is to analyze the influence of changing some parameters of the FA when solving bound constrained optimization problems. One of the most important aspects of this algorithm is how far is the distance between the points and the way they are drawn to the optimal solution. In this work, we aim to analyze other ways of calculating the distance between the points and also other functions to compute the attractiveness of fireflies.
To show the performance of the proposed modified FAs a set of 30 benchmark global optimization test problems are used. Preliminary experiments reveal that the obtained results are competitive when comparing with the original FA version.
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References
Ali, M.M., Khompatraporn, C., Zabinsky, Z.B.: A numerical evaluation of several stochastic algorithms on selected continuous global optimization test problems. J. Global Optim. 31, 635–672 (2005)
Dolan, E.D., Doré, J.J.: Benchmarking Optimization Software with Performance Profiles. Preprint ANL/MCS-P861-1200 (2001)
Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press (2004)
Dorigo, M., Caro, G.D., Gambardella, L.M.: Ant algorithms for discrete optimization. Université Libre de Bruxelles, Belgium (1999)
Eberhart, R.C., Kennedy, J., Shi, Y.: Swarm optimization. Academic Press (2001)
Eberhart, R.C., Kennedy, J.: Particle Swarm optimization. In: Proc. of IEEE International Conference on Neural Networks, Piscataway, NJ, pp. 1942–1948 (1995)
Goldber, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Reading (1989)
Heppner, F., Grenander, U.: A stochastic nonlinear model for coordinated bird flocks. The Ubiquity of Chaos. AAAS Publications, Washington DC (1990)
Łukasik, S., Żak, S.: Firefly algorithm for continuous constrained optimization tasks. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS (LNAI), vol. 5796, pp. 97–106. Springer, Heidelberg (2009)
Reynolds, C.W.: Flocks, herds and schools: a distributed behavioral model. Comp. Graph., 25–34 (1987)
Rocha, A.M.C., Fernandes, E.M.G.P., Martins, T.F.M.C.: Novel Fish swarm heuristics for bound constrained global optimization problems. J. Comput. Appl. Math. 235(16), 4611–4620 (2011)
Yang, X.-S.: Firefly Algorithm, Stochastic Test Functions and Design Optimization. Int. J. Bio-Inspired Computation 2(2), 78–84 (2010)
Yang, X.-S.: Nature-Inspired Metaheuristic Algorithms, 2nd edn. Luniver Press, Beckington (2010)
Yang, X.-S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds.) SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer, Heidelberg (2009)
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Francisco, R.B., Costa, M.F.P., Rocha, A.M.A.C. (2014). Experiments with Firefly Algorithm. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2014. ICCSA 2014. Lecture Notes in Computer Science, vol 8580. Springer, Cham. https://doi.org/10.1007/978-3-319-09129-7_17
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DOI: https://doi.org/10.1007/978-3-319-09129-7_17
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