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An Augmented Artificial Bee Colony with Hybrid Learning

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Advances in Swarm Intelligence (ICSI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9713))

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Abstract

Artificial bee colony as a recently proposed algorithm, suffers from low convergence speed when solving global optimization problems. This may due to the learning mechanism where each bee learns from the randomly selected exemplars. To address the issue, an augmented artificial bee colony algorithm, hybrid learning ABC (HLABC), is presented in this study. In HLABC, different learning strategies are adopted for the employed bee phase and the onlooker bee phase. The updating mechanism for food source position is enhanced by employing the guiding information from the global best food source. Eight benchmark functions with various properties are used to test the proposed algorithm, and the result is compared with that of original ABC, particle swarm optimization (PSO) and bacterial foraging optimization (BFO). Experimental results indicate that the designed strategy significantly improve the performance of ABC for global optimization in terms of solution accuracy and convergence speed.

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References

  1. Banks, A., Vincent, J., Anyakoha, C.: A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications. Nat. Comput. 7, 109–124 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  2. Banks, A., Vincent, J., Anyakoha, C.: A review of particle swarm optimization. Part I: background and development. Nat. Comput. 6, 467–484 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  3. Dorigo, M., Blum, C.: Ant colony optimization theory: a survey. Theor. Comput. Sci. 344, 243–278 (2005)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  5. Mezura-Montes, E., DamiáN-Araoz, M., Cetina-DomiNgez, O.: Smart flight and dynamic tolerances in the artificial bee colony for constrained optimization. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2010)

    Google Scholar 

  6. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 1944, pp. 1942–1948 (1995)

    Google Scholar 

  7. Niu, B., Wang, J., Wang, H., Tan, L.: Bacterial-inspired algorithms for engineering optimization. In: Huang, D.-S., Jiang, C., Bevilacqua, V., Figueroa, J.C. (eds.) ICIC 2012. LNCS, vol. 7389, pp. 649–656. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  8. Chen, S.H., Hong, X.U.: Parameter selection in particle swarm optimization. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds.) EP 1998. LNCS, vol. 1447, pp. 591–600. Springer, Heidelberg (1998)

    Google Scholar 

  9. Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. 22, 52–67 (2002)

    Article  Google Scholar 

  10. Yang, C., Ji, J., Liu, J., Liu, J., Yin, B.: Structural learning of Bayesian networks by bacterial foraging optimization. Int. J. Approximate Reasoning 69, 147–167 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  11. Mallipeddi, R., Suganthan, P.N.: Problem Definitions and Evaluation Criteria for the CEC 2010 Competition on Constrained Real-Parameter Optimization. Nanyang Technological University, Singapore (2010)

    Google Scholar 

  12. Zamuda, A., Brest, J.: Population reduction differential evolution with multiple mutation strategies in real world industry challenges. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) EC 2012 and SIDE 2012. LNCS, vol. 7269, pp. 154–161. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  13. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39, 459–471 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  14. Szeto, W.Y., Wu, Y., Ho, S.C.: An artificial bee colony algorithm for the capacitated vehicle routing problem. Eur. J. Oper. Res. 215, 126–135 (2011)

    Article  Google Scholar 

  15. Balasubramani, K., Marcus, K.: A comprehensive review of artificial bee colony algorithm. Int. J. Comput. Technol. 5, 15–28 (2013)

    Google Scholar 

  16. Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. 8, 687–697 (2008)

    Article  Google Scholar 

  17. Karaboga, D., Akay, B.: A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 214, 108–132 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  18. Hai, S., Yasuda, T., Ohkura, K.: A self adaptive hybrid enhanced artificial bee colony algorithm for continuous optimization problems. Bio Syst. 132–133, 43–53 (2015)

    Google Scholar 

  19. Banitalebi, A., Aziz, M.I.A., Bahar, A., Aziz, Z.A.: Enhanced compact artificial bee colony. Inf. Sci. 298, 491–511 (2015)

    Article  Google Scholar 

  20. Akay, B., Karaboga, D.: Solving integer programming problems by using artificial bee colony algorithm. In: Cucchiara, R., Serra, R. (eds.) AI*IA 2009. LNCS, vol. 5883, pp. 355–364. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  21. Wang, J., Li, T., Ren, R.: A real time IDSs based on artificial bee colony-support vector machine algorithm. In: 2010 Third International Workshop on Advanced Computational Intelligence (IWACI), pp. 91–96 (2010)

    Google Scholar 

  22. Kashan, M.H., Nahavandi, N., Kashan, A.H.: DisABC: a new artificial bee colony algorithm for binary optimization. Appl. Soft Comput. 12, 342–352 (2012)

    Article  Google Scholar 

  23. Karaboga, D., Ozturk, C.: A novel clustering approach: artificial bee colony (ABC) algorithm. Appl. Soft Comput. 11, 652–657 (2011)

    Article  Google Scholar 

  24. Taherdangkoo, M., Bagheri, M.H., Yazdi, M., Andriole, K.P.: An effective method for segmentation of MR brain images using the ant colony optimization algorithm. J. Digit. Imag. 26, 1116–1123 (2013)

    Article  Google Scholar 

  25. Karaboga, D., Ozturk, C.: Neural networks training by artificial bee colony algorithm on pattern classification. Neural Netw. World 19, 279–292 (2009)

    Google Scholar 

  26. Marzband, M., Azarinejadian, F., Savaghebi, M., Guerrero, J.M.: An optimal energy management system for islanded microgrids based on multiperiod artificial bee colony combined with Markov Chain. IEEE Syst. J. 100, 1–11 (2015)

    Article  Google Scholar 

  27. Bhandari, A.K., Kumar, A., Singh, G.K.: Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur’s, Otsu and Tsallis functions. Expert Syst. Appl. 42, 1573–1601 (2015)

    Article  Google Scholar 

  28. Dervis Karaboga, B.G., Ozturk, C., Karaboga, N.: A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif. Intell. Rev. 42, 37 (2014)

    Google Scholar 

  29. Chu, X., Hu, M., Wu, T., Weir, J.D., Lu, Q.: AHPS 2: an optimizer using adaptive heterogeneous particle swarms. Inf. Sci. 280, 26–52 (2014)

    Article  MathSciNet  Google Scholar 

  30. Liang, J.J., Qin, A.K., Suganthan, P.N., Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10, 281–295 (2006)

    Article  Google Scholar 

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Acknowledgments

This work was supported by the national natural science foundation of china (71501132, 71571120 and 71371127).

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Authors and Affiliations

  1. College of Management, Shenzhen University, Shenzhen, China

    Guozheng Hu, Xianghua Chu, Ben Niu, Li Li & Yao Liu

  2. Medical Business School, Guangdong Pharmaceutical University, Guangzhou, China

    Dechang Lin

Authors
  1. Guozheng Hu
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  2. Xianghua Chu
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  3. Ben Niu
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  4. Li Li
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  5. Yao Liu
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  6. Dechang Lin
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Corresponding author

Correspondence to Xianghua Chu .

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Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Xi'an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  3. Shenzen University, Shenzhen, China

    Li Li

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Hu, G., Chu, X., Niu, B., Li, L., Liu, Y., Lin, D. (2016). An Augmented Artificial Bee Colony with Hybrid Learning. In: Tan, Y., Shi, Y., Li, L. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9713. Springer, Cham. https://doi.org/10.1007/978-3-319-41009-8_42

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  • DOI: https://doi.org/10.1007/978-3-319-41009-8_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41008-1

  • Online ISBN: 978-3-319-41009-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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