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Artificial Hummingbird Algorithm with Parallel Compact Strategy

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Advances in Smart Vehicular Technology, Transportation, Communication and Applications

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 347))

Abstract

This paper improves the Artificial Hummingbird Algorithm (AHA). First, we introduce a compact scheme to reduce computer storage capacity and speed up computation. Second, the parallel strategy is added to improve the optimization ability of the algorithm. Third, we improve the original algorithm’s territorial and migration foraging strategies. The purpose of enhancing the territorial foraging strategy is to optimize the algorithm to be more directional. We removed the migration-foraging strategy, which is more suitable for combining with the compact scheme. Finally, we tested the improved algorithm on the cec2013 test set, which showed good performance.

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References

  1. Zhang, F., Wu, T.Y., Wang, Y., Xiong, R., Ding, G., Mei, P., Liu, L.: Application of quantum genetic optimization of lvq neural network in smart city traffic network prediction. IEEE Access 8, 104,555–104,564 (2020)

    Google Scholar 

  2. Kang, L., Chen, R.S., Chen, Y.C., Wang, C.C., Li, X., Wu, T.Y.: Using cache optimization method to reduce network traffic in communication systems based on cloud computing. IEEE Access 7, 124,397–124,409 (2019)

    Google Scholar 

  3. Kang, L., Chen, R.S., Xiong, N., Chen, Y.C., Hu, Y.X., Chen, C.M.: Selecting hyper-parameters of gaussian process regression based on non-inertial particle swarm optimization in internet of things. IEEE Access 7, 59504–59513 (2019)

    Article  Google Scholar 

  4. Kumari, A., Kumar, V., Abbasi, M.Y., Kumari, S., Chaudhary, P., Chen, C.M.: Csef: cloud-based secure and efficient framework for smart medical system using ecc. IEEE Access 8, 107,838–107,852 (2020)

    Google Scholar 

  5. Xu, L., Li, T.J., Ling, Y.F., Lu, J., Cai, Z.M.: Gsgc: An improved path planning optimization.

    Google Scholar 

  6. Holland, J.H.: Genetic algorithms. Sci. Am. 267(1), 66–73 (1992)

    Article  Google Scholar 

  7. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN’95-international conference on neural networks, vol. 4, pp. 1942–1948. IEEE (1995)

    Google Scholar 

  8. Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst., Man, Cybern., Part B (Cybern.) 26(1), 29–41 (1996)

    Google Scholar 

  9. Yang, X.S., Deb, S.: Cuckoo search via l´evy flights. In: 2009 World congress on nature & biologically inspired computing (NaBIC), pp. 210–214. IEEE (2009)

    Google Scholar 

  10. Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)

    Article  Google Scholar 

  11. Mirjalili, S.: Sca: a sine cosine algorithm for solving optimization problems. Knowl.-Based Syst. 96, 120–133 (2016)

    Article  Google Scholar 

  12. Faramarzi, A., Heidarinejad, M., Stephens, B., Mirjalili, S.: Equilibrium optimizer: A novel optimization algorithm. Knowl.-Based Syst. 191, 105,190 (2020)

    Google Scholar 

  13. Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A.A., Al-Qaness, M.A., Gandomi, A.H.: Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput. & Ind. Eng. 157, 107,250 (2021)

    Google Scholar 

  14. Song, P.C., Chu, S.C., Pan, J.S., Yang, H.: Phasmatodea population evolution algorithm and its application in length-changeable incremental extreme learning machine. In: 2020 2nd international conference on industrial artificial intelligence (IAI), pp. 1–5. IEEE (2020)

    Google Scholar 

  15. Heidari, A.A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., Chen, H.: Harris hawks optimization: Algorithm and applications. Futur. Gener. Comput. Syst. 97, 849–872 (2019)

    Article  Google Scholar 

  16. Pan, J.S., Zhang, L.G., Wang, R.B., Sn´aˇsel, V., Chu, S.C.: Gannet optimization algorithm: A new metaheuristic algorithm for solving engineering optimization problems. Math. Comput. Simul. 202, 343–373 (2022)

    Google Scholar 

  17. Zhao, W., Wang, L., Mirjalili, S.: Artificial hummingbird algorithm: A new bioinspired optimizer with its engineering applications. Comput. Methods Appl. Mech. Eng. 388, 114,194 (2022)

    Google Scholar 

  18. Jaddi, N.S., Abdullah, S.: Optimization of neural network using kidney-inspired algorithm with control of filtration rate and chaotic map for real-world rainfall forecasting. Eng. Appl. Artif. Intell. 67, 246–259 (2018)

    Article  Google Scholar 

  19. Nguyen, T.T., Qiao, Y., Pan, J.S., Dao, T.K., Nguyen, T.T.T., Weng, C.J.: An improvement of embedding efficiency for watermarking based on genetic algorithm. J. Inf. Hiding Multim. Signal Process. 11(2), 79–89 (2020)

    Google Scholar 

  20. Pan, J.S., Nguyen, T.L.P., Ngo, T.G., Dao, T.K., Nguyen, T.T.T., Nguyen, T.T.: An optimizing cross-entropy thresholding for image segmentation based on improved cockroach colony optimization. J. Inf. Hiding Multim. Signal Process. 11(4), 162–171 (2020)

    Google Scholar 

  21. Wu, J., Xu, M., Liu, F.F., Huang, M., Ma, L., Lu, Z.M.: Solar wireless sensor network routing algorithm based on multi-objective particle swarm optimization. J. Inf. Hiding Multim. Signal Process. 12(1), 1–11 (2021)

    Google Scholar 

  22. Zhou, J.L., Chu, S.C., Tian, A.Q., Peng, Y.J., Pan, J.S.: Intelligent neural network with parallel salp swarm algorithm for power load prediction. J. Internet Technol. 23(4), 643–657 (2022)

    Article  Google Scholar 

  23. Xue, X., Chen, J., Chen, D.: Matching biomedical ontologies through compact hybrid evolutionary algorithm. J. Inf. Hiding Multim. Signal Process. 10(1), 110–117 (2019)

    Google Scholar 

  24. Wu, C.M., Gong, H.Q., Yang, J.H., Song, Q.H., Wang, Y.J.: An improved FOA to optimize GRNN method for wind turbine fault diagnosis. J. Inf. Hiding Multim. Signal Process. 9(1), 1–10 (2018)

    Google Scholar 

  25. Wang, J., Pan, B., Wang, Q.R., Ding, Q.: A chaotic key expansion algorithm based on genetic algorithm. J. Inf. Hiding Multim. Signal Process. 10(2), 289–299 (2019)

    Google Scholar 

  26. Xi, J., Chen, Y., Liu, X., Chen, X.: Whale optimization algorithm based on nonlinear adjustment and random walk strategy (2022)

    Google Scholar 

  27. Shao, Z.Y., Pan, J.S., Hu, P., Chu, S.C.: Equilibrium optimizer of interswarm interactive learning strategy. Enterp. Inf. Syst. 1–25 (2021)

    Google Scholar 

  28. Neri, F., Mininno, E., Iacca, G.: Compact particle swarm optimization. Inf. Sci. 239, 96–121 (2013)

    Article  MathSciNet  Google Scholar 

  29. Kong, L., Pan, J.S., Tsai, P.W., Vaclav, S., Ho, J.H.: A balanced power consumption algorithm based on enhanced parallel cat swarm optimization for wireless sensor network. Int. J. Distrib. Sens. Netw. 11(3), 729,680 (2015)

    Google Scholar 

  30. Song, P.C., Pan, J.S., Chu, S.C.: A parallel compact cuckoo search algorithm for three-dimensional path planning. Appl. Soft Comput. 94, 106,443 (2020)

    Google Scholar 

  31. Zhang, S., Fan, F., Li, W., Chu, S.C., Pan, J.S.: A parallel compact sine cosine algorithm for TDOA localization of wireless sensor network. Telecommun. Syst. 78(2), 213–223 (2021)

    Article  Google Scholar 

  32. Zhao, M., Pan, J.S., Chen, S.T.: Entropy-based audio watermarking via the point of view on the compact particle swarm optimization. J. Internet Technol. 16(3), 485–493 (2015)

    Google Scholar 

  33. Xue, X., Pan, J.S.: A compact co-evolutionary algorithm for sensor ontology metamatching. Knowl. Inf. Syst. 56(2), 335–353 (2018)

    Article  Google Scholar 

  34. LarraËœnaga, P., Lozano, J.A.: Estimation of distribution algorithms: A new tool for evolutionary computation, vol. 2. Springer Science & Business Media (2001)

    Google Scholar 

  35. Bronshtein, I.N., Semendyayev, K.A.: Handbook of mathematics. Springer Sci. Bus. Media (2013)

    Google Scholar 

  36. Yang, X.S.: Bat algorithm: literature review and applications. arXiv preprintarXiv:1308.3900 (2013)

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Correspondence to Xiaoqing Zhang .

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Chu, SC., Shao, ZY., Shieh, CS., Zhang, X. (2023). Artificial Hummingbird Algorithm with Parallel Compact Strategy. In: Ni, S., Wu, TY., Geng, J., Chu, SC., Tsihrintzis, G.A. (eds) Advances in Smart Vehicular Technology, Transportation, Communication and Applications. Smart Innovation, Systems and Technologies, vol 347. Springer, Singapore. https://doi.org/10.1007/978-981-99-0848-6_27

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  • DOI: https://doi.org/10.1007/978-981-99-0848-6_27

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-0847-9

  • Online ISBN: 978-981-99-0848-6

  • eBook Packages: EngineeringEngineering (R0)

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