Abstract
Human Learning Optimization (HLO) is an emerging meta-heuristic with promising potential. Although HLO can be directly applied to real-coded problems as a binary algorithm, the search efficiency may be significantly spoiled due to “the curse of dimensionality”. To extend HLO, Continuous HLO (CHLO) is developed to solve real-values problems. However, the research on CHLO is still in its initial stages, and further efforts are needed to exploit the effectiveness of the CHLO. Therefore, this paper proposes a novel continuous human learning optimization with enhanced exploitation (CHLOEE), in which the social learning operator is redesigned to perform global search more efficiently so that the individual learning operator is relieved to focus on performing local search for enhancing the exploitation ability. Finally, the CHLOEE is evaluated on the benchmark problem and compared with CHLO as well as recent state-of-the-art meta-heuristics. The experimental results show that the proposed CHLOEE has better optimization performance.
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References
Wang, L., Ni, H., Yang, R., Fei, M., Ye, W.: A simple human learning optimization algorithm. In: Fei, M., Peng, C., Su, Z., Song, Y., Han, Q. (eds.) LSMS/ICSEE 2014. CCIS, vol. 462, pp. 56–65. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-45261-5_7
Wang, L., Ni, H., Yang, R., et al.: An adaptive simplified human learning optimization algorithm. J. Inf. Sci. 320, 126–139 (2015)
Yang, R., Xu, M., He, J., Ranshous, S., Samatova, N.F.: An intelligent weighted fuzzy time series model based on a sine-cosine adaptive human learning optimization algorithm and its application to financial markets forecasting. In: Cong, G., Peng, W.C., Zhang, W.E., Li, C., Sun, A. (eds.) ADMA 2017. LNCS (LNAI), vol. 10604, pp. 595–607. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69179-4_42
Wang, L., An, L., Pi, J., Fei, M., Pardalos, P.M.: A diverse human learning optimization algorithm. J. Global Optim. 67(1–2), 283–323 (2016). https://doi.org/10.1007/s10898-016-0444-2
Wang, L., Pei, J., Wen, Y., et al.: An improved adaptive human learning algorithm for engineering optimization. J. Appl. Soft Comput. 71, 894–904 (2018)
Li, X., Yao, J., Wang, L., Menhas, M.I.: Application of human learning optimization algorithm for production scheduling optimization. In: Fei, M., Ma, S., Li, X., Sun, X., Jia, L., Su, Z. (eds.) LSMS/ICSEE -2017. CCIS, vol. 761, pp. 242–252. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-6370-1_24
Cao, J., Yan, Z., He, G.: Application of multi-objective human learning optimization method to solve AC/DC multi-objective optimal power flow problem. J. International J. Emerg. Electr. Power Syst. 17(3), 327--337 (2016)
Cao, J., Yan, Z., Xu, X., et al.: Optimal power flow calculation in AC/DC hybrid power system based on adaptive simplified human learning optimization algorithm. J. Modern Power Syst. Clean Energy. 4(4), 690--701 (2016)
Wang, L., Yang, R., Ni, H., et al.: A human learning optimization algorithm and its application to multi-dimensional knapsack problems. J. Appl. Soft Comput. 34, 736–743 (2015)
Bhandari, A.K., Kumar, I.V.: A context sensitive energy thresholding based 3D Otsu function for image segmentation using human learning optimization. J. Appl. Soft Comput. 82, 105–570 (2019)
Wen, Y., Wang, L., Peng, W., Menhas, M.I., Qian, L.: Application of Intelligent Virtual Reference Feedback Tuning to Temperature Control in a Heat Exchanger. In: Li, K., Fei, M., Du, D., Yang, Z., Yang, D. (eds.) ICSEE/IMIOT -2018. CCIS, vol. 924, pp. 311–320. Springer, Singapore (2018). https://doi.org/10.1007/978-981-13-2384-3_29
Ding, H., Gu, X.: Hybrid of human learning optimization algorithm and particle swarm optimization algorithm with scheduling strategies for the flexible job-shop scheduling problem. J. Neurocomput. 414, 313–332 (2020)
Shoja, A., Molla-Alizadeh-Zavardehi, S., Niroomand, S.: Hybrid adaptive simplified human learning optimization algorithms for supply chain network design problem with possibility of direct shipment. J. Appl. Soft Comput. 96, 106–594 (2020)
Alguliyev, R., Aliguliyev, R., Isazade, N.: A sentence selection model and HLO algorithm for extractive text summarization. In: 2016 IEEE 10th International Conference on Application of Information and Communication Technologies (AICT). pp. 1--4 (2016)
Yang, R., He, J., Xu, M., Ni, H., Jones, P., Samatova, N.: An Intelligent and Hybrid Weighted Fuzzy Time Series Model Based on Empirical Mode Decomposition for Financial Markets Forecasting. In: Perner, P. (ed.) ICDM 2018. LNCS (LNAI), vol. 10933, pp. 104–118. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95786-9_8
Wang, L., Pei, J., Menhas, M.I., et al.: A hybrid-coded human learning optimization for mixed-variable optimization problems. J. Knowl. Based Syst. 127, 114–125 (2017)
Ghani, J.A., Choudhury, I.A., Hassan, H.H.: Application of Taguchi method in the optimization of end milling parameters. J. Mater. Proc. Technol. 145(1), 84–92 (2004)
Saremi, S., Mirjalili, S., Lewis, A.: Grasshopper optimisation algorithm: theory and application. J. Adv. Eng. Softw. 105, 30–47 (2017)
Assad, A., Deep, K.: A hybrid harmony search and simulated annealing algorithm for continuous optimization. J. Inf. Sci. 450, 246–266 (2018)
Wang, F., Zhang, H., Li, K., et al.: A hybrid particle swarm optimization algorithm using adaptive learning strategy. J. Inf. Sci. 436, 162–177 (2018)
Dhiman, G., Kumar, V.: Seagull optimization algorithm: theory and its applications for large-scale industrial engineering problems. J. Knowl. Based Syst. 165, 169–196 (2019)
Acknowledgments
This work is supported by National Key Research and Development Program of China (No. 2019YFB1405500), National Natural Science Foundation of China (Grant No. 92067105 & 61833011), Key Project of Science and Technology Commission of Shanghai Municipality under Grant No. 19510750300 & 19500712300, and 111 Project under Grant No. D18003.
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Wang, L., Huang, B., Wu, X., Yang, R. (2021). Continuous Human Learning Optimization with Enhanced Exploitation. In: Han, Q., McLoone, S., Peng, C., Zhang, B. (eds) Intelligent Equipment, Robots, and Vehicles. LSMS ICSEE 2021 2021. Communications in Computer and Information Science, vol 1469. Springer, Singapore. https://doi.org/10.1007/978-981-16-7213-2_46
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