Abstract
Flower pollination algorithm (FPA) is one of the well-known evolutionary techniques used extensively to solve optimization problems. Despite its efficiency and wide use, the identical search behaviors may lead the algorithm to converge to local optima. In this paper, an adaptive FPA based on chaotic map (CAFPA) is proposed. The proposed algorithm first used the ergodicity of the logistic chaos mechanism, and chaotic mapping of the initial population to make the initial iterative population more evenly distributed in the solution space. Then at the self-pollination stage, the over-random condition of the gamete renewal was improved, the traction force of contemporary optimal position was given, and adaptive logarithmic inertia weight was introduced to adjust the proportion between the contemporary pollen position and disturbance to improve the performance of the algorithm. By comparing the new algorithm with three famous optimization algorithms, the accuracy and performance of the proposed approach are evaluated by 14 well-known benchmark functions. Statistical comparisons of experimental results show that CAFPA is superior to FPA, PSO, and BOA in terms of convergence speed and robustness.
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References
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). https://doi.org/10.1007/978-3-642-32894-7_27
Yang, X.S., Deb, S.: Cuckoo search via Lévy flights. In: 2009 World Congress on Nature & Biologically Inspired Computing(NaBIC), pp. 210–214. IEEE, Coimbatore (2010)
Yang, X.S.: A new metaheuristic bat-inspired algorithm. Comput. Knowl. Technol. 284, 65–74 (2010)
Li, Y., Guo, Q., Liu, J.: Improved bat algorithm for vehicle routing problem. Int. J. Perform. Eng. 15(1), 317–325 (2019)
Yang, X.S., Karamanoglu, M., He, X.: Multi-objective flower algorithm for optimization. Int. Conf. Comput. Sci. 18(1), 861–868 (2013)
Binh, H.T.T., Hanh, N.T., Van Quan, L., Dey, N.: Improved Cuckoo search and chaotic flower pollination optimization algorithm for maximizing area coverage in Wireless Sensor Networks. Neural Comput. Appl. 30(7), 2305–2317 (2018)
Peesapati, R., Yadav, V.K., Kumar, N.: Flower pollination algorithm based multi-objective congestion management considering optimal capacities of distributed generations. Energy 147, 980–994 (2018)
Sayed, A.E.F., Nabil, E., Badr, A.: A binary clonal flower pollination algorithm for feature selection. Pattern Recogn. Lett. 77, 21–27 (2016)
Zhou, Y.Q., Zhang, S., Luo, Q.F., Wen, C.M.: Using flower pollination algorithm and atomic potential function for shape matching. Neural Comput. Appl. 29(6), 21–40 (2018)
Singh, U., Salgotra, R.: Synthesis of linear antenna array using flower pollination algorithm. Neural Comput. Appl. 29(2), 435–445 (2018)
Xu, S., Wang, Y., Liu, X.: Parameter estimation for chaotic systems via a hybrid flower pollination algorithm. Neural Comput. Appl. 30(8), 2607–2623 (2018)
Zhang, W., Yang, Y., Shuai, Z., Yu, D., Li, Y.: Correlation-aware manufacturing service composition model using an extended flower pollination algorithm. Int. J. Prod. Res. 56(14), 4676–4691 (2018)
Abdel-Basset, M., El-Shahat, D., El-Henawy, I.: Solving 0–1 knapsack problem by binary flower pollination algorithm. Neural Comput. Appl. 1–19 (2018). https://doi.org/10.1007/s00521-018-3375-7
Li, Y., Pei, Y., Liu, J.: Bat optimal algorithm combined uniform mutation with Gaussian mutation. Control Decis. 32(10), 1775–1781 (2017)
Abdel-Raouf, O., Abdel-Baset, M., El-henawy, I.: A new hybrid flower pollination algorithm for solving constrained global optimization problems. Int. J. Appl. Oper. Res. 4(2), 1–13 (2014)
Lenin, K., Reddy, B.R., Kalavathi, M.S.: Shrinkage of active power loss by hybridization of flower pollination algorithm with chaotic harmony search algorithm. Control Theory Inform. 4(8), 31–38 (2014)
Salgotra, R., Singh, U.: A novel bat flower pollination algorithm for synthesis of linear antenna arrays. Neural Comput. Appl. 30(7), 2269–2282 (2018)
Nabil, E.: A modified flower pollination algorithm for global optimization. Expert Syst. Appl. 57, 192–203 (2016)
Wang, R., Zhou, Y.Q.: Flower pollination algorithm with dimension by dimension improvement. Math. Prob. Eng. 4, 1–9 (2014)
Xiao, H.H., Wan, C.X., Duan, Y.M., Tan, Q.L.: Flower pollination algorithm based on gravity search mechanism. Acta Automatica Sinica 43(04), 576–592+491+493+594 (2017)
Salgotra, R., Singh, U.: Application of mutation operators to flower pollination algorithm. Expert Syst. Appl. 79, 112–129 (2017)
Draa, A.: On the performances of the flower pollination algorithm – qualitative and quantitative analyses. Appl. Soft Comput. 34, 349–371 (2015)
Haupt, R.L., Haupt, S.E.: Practical Genetic Algorithms, 2nd edn. Discrete Applied Mathematics, New York (2004)
Li, R., Liu, Q., Liu, L.: Novel image encryption algorithm based on improved logistic map. IET Image Proc. 13, 125–134 (2018)
Fan, J.L., Zhang, X.F.: Piecewise logistic chaotic map and its performance analysis. Acta Electronica Sinica 37(04), 720–725 (2009)
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the 6th Int Symposium on Micro Machine and Human Science, pp. 39–43. IEEE, Piscataway (1995)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference Neural Networks, vol. 4, no. 8, pp. 1942–1948 (2011)
Arora, S., Singh, S.: Butterfly algorithm with Lèvy flights for global optimization. In: International Conference on Signal Processing, Computing and Control, pp. 220–224. IEEE, Waknaghat (2015)
Acknowledgments
This study is supported by the National Natural Science Foundation of China (No. 71601071), the Science & Technology Program of Henan Province, China (No. 182102310886 and 162102110109), and an MOE Youth Foundation Project of Humanities and Social Sciences (No. 15YJC630079). We are particularly grateful to the suggestions of the editor and the anonymous reviewers which is greatly improved the quality of the paper.
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Li, Y., Zheng, J., Zhao, Yr. (2019). Adaptive Flower Pollination Algorithm Based on Chaotic Map. In: Mao, R., Wang, H., Xie, X., Lu, Z. (eds) Data Science. ICPCSEE 2019. Communications in Computer and Information Science, vol 1059. Springer, Singapore. https://doi.org/10.1007/978-981-15-0121-0_34
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