Abstract
Modern optimization has in its disposal an immense variety of heuristic algorithms which can effectively deal with both continuous and combinatorial optimization problems. Recent years brought in this area fast development of unconventional methods inspired by phenomena found in nature. Flower Pollination Algorithm based on pollination mechanisms of flowering plants constitutes an example of such technique. The paper presents first a detailed description of this algorithm. Then results of experimental study of its properties for selected benchmark continuous optimization problems are given. Finally, the performance the algorithm is discussed, predominantly in comparison with the well-known Particle Swarm Optimization Algorithm.
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References
Simon, D.: Evolutionary Optimization Algorithms. Wiley, New York (2013)
Clerc, M.: Particle Swarm Optimization. Wiley, New York (2006)
Łukasik, S., Żak, S.: Firefly algorithm for continuous constrained optimization tasks. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS, vol. 5796, pp. 97–106. Springer, Heidelberg (2009)
Cuevas, E., Cienfuegos, M.: A new algorithm inspired in the behavior of the social-spider for constrained optimization. Expert Systems with Applications 41(2), 412–425 (2014)
Gandomi, A., Alavi, A.: Krill herd: A new bio-inspired optimization algorithm. Communications in Nonlinear Science and Numerical Simulation 17(12), 4831–4845 (2012)
Yang, X.-S.: Flower pollination algorithm for global optimization. In: Durand-Lose, J., Jonoska, N. (eds.) UCNC 2012. LNCS, vol. 7445, pp. 240–249. Springer, Heidelberg (2012)
Encyclopædia Britannica Online: Pollination (2014) (Online; accessed June 27, 2014)
Takhtajan, A.: Flowering Plants. Springer, New York (2009)
Cronk, J., Fennessy, M., Siobhan, M.: Wetland plants: biology and ecology. CRC Press/Lewis Publishers, Boca Raton (2001)
Maiti, R., Satya, P., Rajkumar, D., Ramaswam, A.: Crop plant anatomy. CABI, Wallingford (2012)
Yang, X.S., Karamanoglu, M., He, X.: Multi-objective flower algorithm for optimization. Procedia Computer Science 18, 861–868 (2013)
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. IV, pp. 1942–1948 (1995)
Yang, X.: Nature-Inspired Optimization Algorithms. Elsevier, London (2014)
Liang, J., Qu, B., Suganthan, P., Hernandez-Diaz, A.: Problem Definitions and Evaluation Criteria for the CEC 2013 Special Session and Competition on Real-Parameter Optimization. Technical Report 201212, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore (2013)
Bratton, D., Kennedy, J.: Defining a standard for particle swarm optimization. In: Swarm Intelligence Symposium, SIS 2007, pp. 120–127. IEEE (2007)
Łukasik, S., Kowalski, P.: Flower Pollination Algorithm – facts, conjectures and improvements (in preparation, 2014)
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Łukasik, S., Kowalski, P.A. (2015). Study of Flower Pollination Algorithm for Continuous Optimization. In: Angelov, P., et al. Intelligent Systems'2014. Advances in Intelligent Systems and Computing, vol 322. Springer, Cham. https://doi.org/10.1007/978-3-319-11313-5_40
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DOI: https://doi.org/10.1007/978-3-319-11313-5_40
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11312-8
Online ISBN: 978-3-319-11313-5
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