Abstract
The bat algorithm (BA) has fast convergence, a simple structure, and strong search ability. However, the standard BA has poor local search ability in the late evolution stage because it references the historical speed; its population diversity also declines rapidly. Moreover, since it lacks a mutation mechanism, it easily falls into local optima. To improve its performance, this paper develops a hybrid approach to improving its evolution mechanism, local search mechanism, mutation mechanism, and other mechanisms. First, the quantum computing mechanism (QCM) is used to update the searching position in the BA to improve its global convergence. Secondly, the X-condition cloud generator is used to help individuals with better fitness values to increase the rate of convergence, with the sorting of individuals after a particular number of iterations; the individuals with poor fitness values are used to implement a 3D cat mapping chaotic disturbance mechanism to increase population diversity and thereby enable the BA to jump out of a local optimum. Thus, a hybrid optimization algorithm—the chaotic cloud quantum bats algorithm (CCQBA)—is proposed. To test the performance of the proposed CCQBA, it is compared with alternative algorithms. The evaluation functions are nine classical comparative functions. The results of the comparison demonstrate that the convergent accuracy and convergent speed of the proposed CCQBA are significantly better than those of the other algorithms. Thus, the proposed CCQBA represents a better method than others for solving complex problems.
Similar content being viewed by others
References
Li, M.W., Geng, J., Han, D.F., Zheng, T.J.: Ship motion prediction using dynamic seasonal Rv-SVR with phase space reconstruction and the chaos adaptive efficient FOA. Neurocomputing 174, 661–680 (2016). https://doi.org/10.1016/j.neucom.2015.09.089
Hong, W.C.: Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model. Energy Convers. Manag. 50, 105–117 (2009). https://doi.org/10.1016/j.enconman.2008.08.031
Li, M.W., Hong, W.C., Kang, H.G.: Urban traffic flow forecasting using Gauss–SVR with cat mapping, cloud model and PSO hybrid algorithm. Neurocomputing 99, 230–240 (2013). https://doi.org/10.1016/j.neucom.2012.08.002
Jaberipour, M., Khorram, E.: Two improved harmony search algorithms for solving engineering optimization problems. Commun. Nonlinear Sci. Numer. Simul. 15, 3316–3331 (2010). https://doi.org/10.1016/j.cnsns.2010.01.009
Fliege, J., Graña Drummond, L.M., Svaiter, B.F.: Newton’s method for multiobjective optimization. SIAM J. Optim. 20, 602–626 (2009). https://doi.org/10.1137/08071692X
Andrei, N.: A scaled nonlinear conjugate gradient algorithm for unconstrained optimization. Optimization 57, 549–570 (2008). https://doi.org/10.1080/02331930601127909
Dong, J.Y., Li, W., Deng, L.L., Xu, J.J., Griffin, J.L., Chen, Z.: New variable scaling method for NMR-based metabolomics data analysis. Chem. J. Chin. Univ. 32, 262–268 (2011). http://caod.oriprobe.com/articles/26066259/New_Variable_Scaling_Method_for_NMR_based_Metabolomics_Data_Analysis.htm
Wang, D.D., Chen, Y.J., Pang, H.J.: Rational function functional networks based on function approximation. Appl. Mech. Mater. 220–223, 2264–2268 (2012). https://doi.org/10.4028/www.scientific.net/AMM.220-223.2264
Liu, D.C., Nocedal, J.: On the limited memory BFGS method for large scale optimization. Math. Program. 45, 503–528 (1989). https://doi.org/10.1007/BF01589116
Ignaccolo, M., Inturri, G., García-Melón, M., Giuffrida, N., Pira, M.L., Torrisi, V.: Combining analytic hierarchy process (AHP) with role-playing games for stakeholder engagement in complex transport decisions. Transp. Res. Procedia 27, 500–507 (2017). https://doi.org/10.1016/j.trpro.2017.12.069
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks (IJCNN 95), pp. 1942–1948, Perth, Western Australia, December 1995. https://doi.org/10.1109/ICNN.1995.488968
Mirjalili, S.: Evolutionary Algorithms and Neural Networks. Springer, Berlin (2019). https://doi.org/10.1007/978-3-319-93025-1
Dorigo, M., Caro, G.D.: Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 Congress on Evolutionary Computation (CEC 99), IEEE, Washington, DC, USA, July 1999, pp. 1470–1477. https://doi.org/10.1109/CEC.1999.782657
Gandomi, A.H., Yang, X.S., Alavi, A.H.: Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng. Comput. 29, 17–35 (2011). https://doi.org/10.1007/s00366-011-0241-y
Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pp. 65–74. Springer, Berlin (2010). https://doi.org/10.1007/978-3-642-12538-6_6
Natarajan, A., Subramanian, S., Premalatha, K.: A comparative study of cuckoo search and bat algorithm for bloom filter optimisation in spam filtering. Int. J. Bio-Inspired Comput. 4, 89–99 (2012). https://doi.org/10.1504/IJBIC.2012.047179
Mafarja, M., Mirjalili, S.: Whale optimization approaches for wrapper feature selection. Appl. Soft Comput. 62, 441–453 (2018). https://doi.org/10.1016/j.asoc.2017.11.006
Armaghani, D.J., Hasanipanah, M., Mahdiyar, A., Majid, M.Z.A., Amnieh, H.B., Tahir, M.M.D.: Airblast prediction through a hybrid genetic algorithm-ann model. Neural Comput. Appl. 29, 619–629 (2018). https://doi.org/10.1007/s00521-016-2598-8
Kang, M., Kim, J., Kim, J.M.: Reliable fault diagnosis for incipient low-speed bearings using feature analysis based on a binary bat algorithm. Inf. Sci. 294, 423–438 (2015). https://doi.org/10.1016/j.ins.2014.10.014
Gandomi, A.H., Yang, X.S., Alavi, A.H., Talatahari, S.: Bat algorithm for constrained optimization tasks. Neural Comput. Appl. 22, 1239–1255 (2013). https://doi.org/10.1007/s00521-012-1028-9
Xie, J., Zhou, Y., Chen, H.: A novel bat algorithm based on differential operator and Levy flights trajectory. Computational Intelligence and Neuroscience 2013, Article ID 453812 (2013). https://doi.org/10.1155/2013/453812
Zhu, B., Zhu, W., Liu, Z., Duan, Q., Cao, L.: A novel quantum-behaved bat algorithm with mean best position directed for numerical optimization. Computational Intelligence and Neuroscience 2016, Article ID 6097484 (2016). https://doi.org/10.1155/2016/6097484
Yilmaz, S., Kucuksille, E.U.: Improved bat algorithm (iba) on continuous optimization problems. Lect. Notes Softw. Eng. 1, 279–283 (2013). https://doi.org/10.7763/LNSE.2013.V1.61
Li, D., Du, Y.: Artificial Intelligence with Uncertainty, 2nd edn. CRC Press, Beijing (2017)
Sun, J., Feng, B., Xu, W.: Particle swarm optimization with particles having quantum behavior. In: Proceedings of the 2004 Congress on Evolutionary Computation (CEC 2004), pp 325–331. Portland, USA, (2004). https://doi.org/10.1109/CEC.2004.1330875
Nepomuceno, E.G., Lima, A.M., Arias-García, J., Perc, M., Repnik, R.: Minimal digital chaotic system. Chaos Solitons Fractals 120, 62–66 (2019). https://doi.org/10.1016/j.chaos.2019.01.019
Sang, T., Wang, R., Yan, Y.: Generating binary bernoulli sequences based on a class of even-symmetric chaotic maps. IEEE Trans. Commun. 49(4), 620–623 (2001). https://doi.org/10.1109/26.917768
Tutueva, A.V., Nepomuceno, E.G., Karimov, A.I., Andreev, V.S., Butusov, D.N.: Adaptive chaotic maps and their application to pseudo-random numbers generation. Chaos Solitons Fractals 133, 109615 (2019). https://doi.org/10.1016/j.chaos.2020.109615
Gandomi, A.H., Yang, X.S.: Chaotic bat algorithm. J. Comput. Sci. 5(2), 224–232 (2014). https://doi.org/10.1016/j.jocs.2013.10.002
Wang, F., Dai, Y., Wang, S.: Chaos-genetic algorithm based on the cat map and its application on seismic wavelet estimation. The proceeding of 2009 International Workshop on Chaos-Fractals Theories and Applications, Shenyang, China, 6-8 Nov. 2009. https://doi.org/10.1109/IWCFTA.2009.31
Liu, Y., Tang, W.K.S., Kwok, H.S.: Formulation and analysis of high-dimensional chaotic maps. In: Proceedings of 2008 IEEE International Symposium on Circuits and Systems (ISCAS 2008). Seattle, WA, USA (2008). https://doi.org/10.1109/ISCAS.2008.4541532
Chen, G., Mao, Y., Chui, C.K.: A symmetric image encryption scheme based on 3D chaotic cat maps. Chaos Solut. Fractals 21, 749–761 (2004). https://doi.org/10.1016/j.chaos.2003.12.022
Bao, J., Yang, Q.: Period of the discrete arnold cat map and general cat map. Nonlinear Dyn. 70(2), 1365–1375 (2012). https://doi.org/10.1007/s11071-012-0539-3
Li, D., Meng, H., Shi, X.: Membership clouds and membership cloud generators. Journal of Computer Res. Develop. 32, 15–20 (1995). http://en.cnki.com.cn/Article_en/CJFDTOTAL-JFYZ506.002.htm
Li, M.W., Kang, H., Zhou, P., Hong, W.C.: Hybrid optimization algorithm based on chaos, cloud and particle swarm optimization algorithm. J. Syst. Eng. Electron. 24, 324–334 (2013). https://doi.org/10.1109/JSEE.2013.00041
Andrei, N.: An unconstrained optimization test functions collection. Environ. Sci. Technol. 10(1), 6552–6558 (2008). https://doi.org/10.1021/es702781x
Acknowledgements
The work is supported by the following project grants: National Natural Science Foundation of China (No.51509056); Heilongjiang Province Natural Science Fund (No. E2017028); Fundamental Research Funds for the Central Universities (No. HEUCFG201813); Open Fund of the State Key Laboratory of Coastal and Offshore Engineering (No. LP1610); Heilongjiang Sanjiang Project Administration Scientific Research and Experiments (No. SGZL/KY-08); and Ministry of Science and Technology, Taiwan (MOST 108-2410-H-161-004).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Li, MW., Wang, YT., Geng, J. et al. Chaos cloud quantum bat hybrid optimization algorithm. Nonlinear Dyn 103, 1167–1193 (2021). https://doi.org/10.1007/s11071-020-06111-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11071-020-06111-6