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Surrogate-Assisted Artificial Bee Colony Algorithm

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Bio-Inspired Computing: Theories and Applications (BIC-TA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1565))

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Abstract

Search strategies play an essential role in the artificial bee colony (ABC) algorithm. Different optimization problems and search stages may need different search strategies. However, it is not easy to choose an appropriate search strategy efficiently. In order to select an appropriate search strategy with few evaluations, this paper proposes a surrogate-assisted ABC (called SAABC). Based on our previous work, we construct a strategy pool that contains three search strategies. Then, the radial basis function (RBF) network is applied to evaluate the offspring generated by the search strategies. The search strategy with the best evaluation value will be used to guide the population. A set of 22 classical benchmark problems with 30 dimensions are utilized to verify the performance of SAABC. Experimental results show that SAABC achieves better performance than five other ABC algorithms.

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References

  1. 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 

  2. Tian, D., Shi, Z.: Mpso: modified particle swarm optimization and its applications. Swarm Evol. Comput. 41, 49–68 (2018)

    Article  Google Scholar 

  3. Price, K., Storn, R.M., Lampinen, J.A.: Differential evolution: a practical approach to global optimization. Springer Science & Business Media (2006)

    Google Scholar 

  4. Wu, G., Shen, X., Li, H., Chen, H., Lin, A., Suganthan, P.N.: Ensemble of differential evolution variants. Inf. Sci. 423, 172–186 (2018)

    Article  MathSciNet  Google Scholar 

  5. Whitley, D.: A genetic algorithm tutorial. Stat. Comput. 4(2), 65–85 (1994)

    Article  Google Scholar 

  6. Metawa, N., Hassan, M.K., Elhoseny, M.: Genetic algorithm based model for optimizing bank lending decisions. Expert Syst. Appl. 80, 75–82 (2017)

    Article  Google Scholar 

  7. Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization. IEEE Comput. Intell. Mag. 1(4), 28–39 (2006)

    Article  Google Scholar 

  8. Dorigo, M., Stützle, T.: Ant colony optimization: overview and recent advances. Handbook of metaheuristics, pp. 311–351 (2019)

    Google Scholar 

  9. Wang, H., Wang, W., Xiao, S., Cui, Z., Li, W., Zhu, H., Zhu, S.: Multi-strategy and dimension perturbation ensemble of artificial bee colony. In: 2019 IEEE Congress on Evolutionary Computation (CEC), pp. 697–704. IEEE (2019)

    Google Scholar 

  10. Zeng, T., Ye, T., Zhang, L., Xu, M., Wang, H., Hu, M.: Population diversity guided dimension perturbation for artificial bee colony algorithm. In: Zhang, H., Yang, Z., Zhang, Z., Wu, Z., Hao, T. (eds.) NCAA 2021. CCIS, vol. 1449, pp. 473–485. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-5188-5_34

    Chapter  Google Scholar 

  11. 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)

    Article  MathSciNet  Google Scholar 

  12. Cui, L., Li, G., Lin, Q., Du, Z., Gao, W., Chen, J., Lu, N.: A novel artificial bee colony algorithm with depth-first search framework and elite-guided search equation. Inf. Sci. 367, 1012–1044 (2016)

    Article  Google Scholar 

  13. 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 

  14. Gao, W.f., Liu, S.y., Huang, L.l.: A novel artificial bee colony algorithm based on modified search equation and orthogonal learning. IEEE Trans. Cybern. 43(3), 1011–1024 (2013)

    Google Scholar 

  15. 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)

    Google Scholar 

  16. Kiran, M.S., Hakli, H., Gunduz, M., Uguz, H.: Artificial bee colony algorithm with variable search strategy for continuous optimization. Inf. Sci. 300, 140–157 (2015)

    Article  MathSciNet  Google Scholar 

  17. Ye, T., Zeng, T., Zhang, L., Xu, M., Wang, H., Hu, M.: Artificial bee colony algorithm with an adaptive search manner. In: Zhang, H., Yang, Z., Zhang, Z., Wu, Z., Hao, T. (eds.) NCAA 2021. CCIS, vol. 1449, pp. 486–497. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-5188-5_35

    Chapter  Google Scholar 

  18. Regis, R.G.: Particle swarm with radial basis function surrogates for expensive black-box optimization. J. Comput. Sci. 5(1), 12–23 (2014)

    Article  MathSciNet  Google Scholar 

  19. Mallipeddi, R., Lee, M.: Surrogate model assisted ensemble differential evolution algorithm. In: 2012 IEEE Congress on Evolutionary Computation, pp. 1–8. IEEE (2012)

    Google Scholar 

  20. Sun, X.Y., Gong, D.W., Ma, X.P.: Directed fuzzy graph-based surrogate model-assisted interactive genetic algorithms with uncertain individual’s fitness. In: 2009 IEEE Congress on Evolutionary Computation, pp. 2395–2402. IEEE (2009)

    Google Scholar 

  21. Loshchilov, I., Schoenauer, M., Sebag, M.: A mono surrogate for multiobjective optimization. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 471–478 (2010)

    Google Scholar 

  22. Herrera, M., Guglielmetti, A., Xiao, M., Filomeno Coelho, R.: Metamodel-assisted optimization based on multiple kernel regression for mixed variables. Struct. Multidiscip. Optim. 49(6), 979–991 (2014). https://doi.org/10.1007/s00158-013-1029-z

  23. Zhang, Q., Liu, W., Tsang, E., Virginas, B.: Expensive multiobjective optimization by moea/d with gaussian process model. IEEE Trans. Evol. Comput. 14(3), 456–474 (2009)

    Article  Google Scholar 

  24. Buche, D., Schraudolph, N.N., Koumoutsakos, P.: Accelerating evolutionary algorithms with gaussian process fitness function models. IEEE Trans. Syst. Man Cybern. Part C (Applications and Reviews) 35(2), 183–194 (2005)

    Google Scholar 

  25. Zapotecas Martínez, S., Coello Coello, C.A.: Moea/d assisted by rbf networks for expensive multi-objective optimization problems. In: Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, pp. 1405–1412 (2013)

    Google Scholar 

  26. Sun, C., Jin, Y., Zeng, J., Yu, Y.: A two-layer surrogate-assisted particle swarm optimization algorithm. Soft. Comput. 19(6), 1461–1475 (2014). https://doi.org/10.1007/s00500-014-1283-z

    Article  Google Scholar 

  27. Gaspar-Cunha, A., Vieira, A., et al.: A hybrid multi-objective evolutionary algorithm using an inverse neural network. In: Hybrid Metaheuristics, Citeseer, pp. 25–30 (2004)

    Google Scholar 

  28. Gaspar-Cunha, A., Vieira, A.: A multi-objective evolutionary algorithm using neural networks to approximate fitness evaluations. Int. J. Comput. Syst. Signals 6(1), 18–36 (2005)

    Google Scholar 

  29. Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report (2005)

    Google Scholar 

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

    Google Scholar 

  31. Hardy, R.L.: Multiquadric equations of topography and other irregular surfaces. J. Geophys. Res. 76(8), 1905–1915 (1971)

    Article  Google Scholar 

  32. Powell, M.J.D.: Radial Basis Functions for Multivariable Interpolation: A Review, pp. 143–167. Clarendon Press, USA (1987)

    Google Scholar 

  33. Broomhead, D.S., Lowe, D.: Multivariable functional interpolation and adaptive networks. Complex Syst. 2(3), 321–355 (1988)

    MathSciNet  MATH  Google Scholar 

  34. Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Comput. 3(2), 246–257 (1991)

    Article  Google Scholar 

  35. Cui, L., Li, G., Luo, Y., Chen, F., Ming, Z., Lu, N., Lu, J.: An enhanced artificial bee colony algorithm with dual-population framework. Swarm Evol. Comput. 43, 184–206 (2018)

    Article  Google Scholar 

  36. Sun, C., Jin, Y., Cheng, R., Ding, J., Zeng, J.: Surrogate-assisted cooperative swarm optimization of high-dimensional expensive problems. IEEE Trans. Evol. Comput. 21(4), 644–660 (2017)

    Article  Google Scholar 

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Acknowledgment

This work was supported by the National Natural Science Foundation of China (No. 62166027), and Jiangxi Provincial Natural Science Foundation (Nos. 20212ACB212004 and 20212BAB202023).

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Correspondence to Hui Wang .

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Zeng, T., Wang, H., Wang, W., Ye, T., Zhang, L. (2022). Surrogate-Assisted Artificial Bee Colony Algorithm. In: Pan, L., Cui, Z., Cai, J., Li, L. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2021. Communications in Computer and Information Science, vol 1565. Springer, Singapore. https://doi.org/10.1007/978-981-19-1256-6_19

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  • DOI: https://doi.org/10.1007/978-981-19-1256-6_19

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-1255-9

  • Online ISBN: 978-981-19-1256-6

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