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Population Diversity Guided Dimension Perturbation for Artificial Bee Colony Algorithm

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Neural Computing for Advanced Applications (NCAA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1449))

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Abstract

In the original artificial bee colony (ABC), only one dimension of the solution is updated each time and this leads to little differences between the offspring and the parent solution. Then, it affects the convergence speed. In order to accelerate the convergence speed, we can update multiple dimensions of the solution at the same time, and the information of the global optimal solution can be used for guidance. However, using these two methods will reduce the population diversity at the initial stage. This is not conducive to search of multimodal functions. In this paper, a population diversity guided dimension perturbation for artificial bee colony algorithm (called PDDPABC) is proposed, in which population diversity is used to control the number of dimension perturbations. Then, it can maintain a certain population diversity, and does not affect the convergence speed. In order to verify the performance of PDDPABC, we tested its performance on 22 classic problems and CEC 2013 benchmark set. Compared with several other ABC variants, our approach can achieve better results.

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References

  1. Liu, N.X., Pan, J.S., Sun, C.L., Chu, S.C.: An efficient surrogate-assisted quasi-affine transformation evolutionary algorithm for expensive optimization problems. Knowl.-Based Syst. 209, 106418 (2020)

    Article  Google Scholar 

  2. Du, Z.G., Pan, J.S., Chu, S.C., Luo, H.J., Hu, P.: Quasi-affine transformation evolutionary algorithm with communication schemes for application of RSSI in wireless sensor networks. IEEE Access 8, 8583–8594 (2020)

    Article  Google Scholar 

  3. Pan, J.S., Liu, N., Chu, S.C.: A hybrid differential evolution algorithm and its application in unmanned combat aerial vehicle path planning. IEEE Access 8, 17691–17712 (2020)

    Article  Google Scholar 

  4. Tavakkoli-Moghaddam, R., Safari, J., Sassani, F.: Reliability optimization of series-parallel systems with a choice of redundancy strategies using a genetic algorithm. Reliab. Eng. Syst. Saf. 93(4), 550–556 (2008)

    Article  Google Scholar 

  5. Long, Q.: A constraint handling technique for constrained multi-objective genetic algorithm. Swarm Evol. Comput. 15, 66–79 (2014)

    Article  Google Scholar 

  6. Xiao, S.Y., Wang, W.J., Wang, H., Zhou, X.Y.: A new artificial bee colony based on multiple search strategies and dimension selection. IEEE Access 7, 133982–133995 (2019)

    Article  Google Scholar 

  7. Wang, H., Wang, W.: A new multi-strategy ensemble artificial bee colony algorithm for water demand prediction. In: Peng, H., Deng, C., Wu, Z., Liu, Y. (eds.) ISICA 2018. CCIS, vol. 986, pp. 63–70. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-6473-0_6

    Chapter  Google Scholar 

  8. Wang, H., et al.: Multi-strategy and dimension perturbation ensemble of artificial bee colony. In: IEEE Congress on Evolutionary Computation, pp. 697–704 (2019)

    Google Scholar 

  9. Wang, H., Wang, W., Cui, Z.: A new artificial bee colony algorithm for solving large-scale optimization problems. In: Vaidya, J., Li, J. (eds.) ICA3PP 2018. LNCS, vol. 11335, pp. 329–337. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-05054-2_26

    Chapter  Google Scholar 

  10. Hu, P., Pan, J.S., Chu, S.C.: Improved binary grey wolf optimizer and its application for feature selection. Knowl.-Based Syst. 195, 105746 (2020)

    Article  Google Scholar 

  11. Pan, J.S., Zhuang, J., Luo, H., Chu, S.C.: Multi-group flower pollination algorithm based on novel communication strategies. J. Internet Technol. 22, 257–269 (2021)

    Google Scholar 

  12. Du, Z.G., Pan, J.S., Chu, S.C., Chiu, Y.J.: Improved binary symbiotic organism search algorithm with transfer functions for feature selection. IEEE Access 8, 225730–225744 (2020)

    Article  Google Scholar 

  13. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global Optim. 39(3), 459–471 (2007). https://doi.org/10.1007/s10898-007-9149-x

    Article  MathSciNet  MATH  Google Scholar 

  14. Wang, H., Wang, W., Xiao, S., Cui, Z., Zhou, X.: Improving artificial bee colony algorithm using a new neighborhood selection mechanism. Inf. Sci. 527, 227–240 (2020)

    Article  MathSciNet  Google Scholar 

  15. Cui, L., et al.: A ranking-based adaptive artificial bee colony algorithm for global numerical optimization. Inf. Sci. 417, 169–185 (2017)

    Article  Google Scholar 

  16. Zhu, G., Kwong, S.: Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl. Math. Comput. 217(7), 3166–3173 (2010)

    MathSciNet  MATH  Google Scholar 

  17. Xue, Y., Jiang, J., Zhao, B., Ma, T.: A self-adaptive artificial bee colony algorithm based on global best for global optimization. Soft. Comput. 22(9), 2935–2952 (2018). https://doi.org/10.1007/s00500-017-2547-1

    Article  Google Scholar 

  18. Gao, W., Liu, S.: Improved artificial bee colony algorithm for global optimization. Inf. Process. Lett. 111(17), 871–882 (2011)

    Article  MathSciNet  Google Scholar 

  19. Cui, L., et al.: A novel artificial bee colony algorithm with depth-first search framework and elite-guided search equation. Inf. Sci. 367–368, 1012–1044 (2016)

    Article  Google Scholar 

  20. Wang, H., Wang, W., Zhou, X., Zhao, J., Xu, M.: Artificial bee colony algorithm based on knowledge fusion. Complex Intell. Syst. 7(3), 1139–1152 (2021)

    Google Scholar 

  21. Wang, H., Wu, Z., Rahnamayan, S., Sun, H., Liu, Y., Pan, J.S.: Multi-strategy ensemble artificial bee colony algorithm. Inf. Sci. 279, 587–603 (2014)

    Article  MathSciNet  Google Scholar 

  22. Wang, H., et al.: Multi-strategy and dimension perturbation ensemble of artificial bee colony. In: IEEE Congress on Evolutionary Computation (CEC 2019), pp. 697–704. IEEE, Wellington (2019)

    Google Scholar 

  23. Yu, G., Zhou, H., Wang, H.: Improving artificial bee colony algorithm using a dynamic reduction strategy for dimension perturbation. Math. Probl. Eng. 2019, 3419410 (2019)

    Google Scholar 

  24. Gao, W., Liu, S., Huang, L.: Enhancing artificial bee colony algorithm using more information-based search equations. Inf. Sci. 270, 112–133 (2014)

    Article  MathSciNet  Google Scholar 

  25. Gao, W.F., Liu, S.Y.: A modified artificial bee colony algorithm. Comput. Oper. Res. 39(3), 687–697 (2012)

    Article  Google Scholar 

  26. Xu, Y., Ping, F., Ling, Y.: A simple and efficient artificial bee colony algorithm. Math. Probl. Eng. 2013, 526315 (2013)

    Google Scholar 

  27. Sharma, T.K., Gupta, P.: Opposition learning based phases in artificial bee colony. Int. J. Syst. Assur. Eng. Manag. 9(1), 1–12 (2018). https://doi.org/10.1007/s13198-016-0545-9

    Article  Google Scholar 

  28. Akay, B., Karaboga, D.: A modified artificial bee colony algorithm for real-parameter optimization. Inf. Sci. 192, 120–142 (2012)

    Article  Google Scholar 

  29. Cao, Y., Lu, Y., Pan, X., Sun, N.: An improved global best guided artificial bee colony algorithm for continuous optimization problems. Cluster Comput. 22(2), 3011–3019 (2019). https://doi.org/10.1007/s10586-018-1817-8

    Article  Google Scholar 

  30. Xiao, S., Wang, W., Wang, H., Zhou, X.: A new artificial bee colony based on multiple search strategies and dimension selection. IEEE Access 7, 133982–133995 (2019)

    Article  Google Scholar 

  31. Liang, J.J., Qu, B.Y., Suganthan, P.N.: Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. Technical report, Computational Intelligence Laboratory, Zhengzhou University (2013)

    Google Scholar 

  32. Wang, H., Rahnamayan, S., Sun, H., Omran, M.G.H.: Gaussian bare-bones differential evolution. IEEE Trans. Cybern. 43(2), 634–647 (2013)

    Article  Google Scholar 

  33. Wang, H., Wu, Z.J., Rahnamayan, S., Liu, Y., Ventresca, M.: Enhancing particle swarm optimization using generalized opposition-based learning. Inf. Sci. 181(20), 4699–4714 (2011)

    Article  MathSciNet  Google Scholar 

  34. Xiao, S., et al.: An improved artificial bee colony algorithm based on elite strategy and dimension learning. Mathematics 7(3), 289 (2019)

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China

    Tao Zeng, Tingyu Ye, Luqi Zhang, Minyang Xu, Hui Wang & Min Hu

Authors
  1. Tao Zeng
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  2. Tingyu Ye
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  3. Luqi Zhang
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  4. Minyang Xu
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  5. Hui Wang
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  6. Min Hu
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Corresponding author

Correspondence to Hui Wang .

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Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Haijun Zhang

  2. Nanfang College of Sun Yat-sen University, Guangzhou, China

    Zhi Yang

  3. Hefei University of Technology, Hefei, China

    Zhao Zhang

  4. Chongqing University, Chongqing, China

    Zhou Wu

  5. South China Normal University, Guangzhou, China

    Tianyong Hao

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Zeng, T., Ye, T., Zhang, L., Xu, M., Wang, H., Hu, M. (2021). Population Diversity Guided Dimension Perturbation for Artificial Bee Colony Algorithm. In: Zhang, H., Yang, Z., Zhang, Z., Wu, Z., Hao, T. (eds) Neural Computing for Advanced Applications. NCAA 2021. Communications in Computer and Information Science, vol 1449. Springer, Singapore. https://doi.org/10.1007/978-981-16-5188-5_34

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  • DOI: https://doi.org/10.1007/978-981-16-5188-5_34

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  • Print ISBN: 978-981-16-5187-8

  • Online ISBN: 978-981-16-5188-5

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