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A particle swarm inspired multi-elitist artificial bee colony algorithm for real-parameter optimization

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Abstract

The Artificial Bee Colony (ABC) algorithm is one of the most recent swarm intelligence based algorithms which simulates the foraging behavior of honey bee colonies. In this work, a particle swarm inspired multi-elitist ABC algorithm named PS-MEABC is proposed and applied for real-parameter optimization. In this modified version, the global best solution and an elitist randomly selected from the elitist archive are used to modify parameters of each food source in either onlooker bees or employed bees phases. PS-MEABC is compared with 5 state-of-the-art swarm based algorithms on CEC05 and BBOB12 benchmark functions in terms of four metrics: the mean error, the best error, the success rate (SR) and the expected running time (ERT). Wilcoxon signed ranks test results on the mean and the best error show that the performance of PS-MEABC is significantly better than or at least similar to these algorithms, and PS-MEABC has wider application range in terms of the success rate and faster convergence speed in terms of the expected running time. Our algorithm is comparable to its competitors with a fewer control parameters to be tuned.

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References

  1. Akay, B., Karaboga, D.: A modified artificial bee colony algorithm for real-parameter optimization. Inf. Sci. 192(1), 120–142 (2012)

    Article  Google Scholar 

  2. Alzaqebah, M., Abdullah, S.: Hybrid artificial bee colony search algorithm based on disruptive selection for examination timetabling problems. In: Combinatorial Optimization and Applications. Lecture Notes in Computer Science, vol. 6831, pp. 31–45. Springer, Berlin (2011)

    Chapter  Google Scholar 

  3. Auger, A., Hansen, N.: Performance evaluation of an advanced local search evolutionary algorithm. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2005), pp. 1777–1784 (2005)

    Chapter  Google Scholar 

  4. Brajevic, I., Tuba, M.: An upgraded artificial bee colony (abc) algorithm for constrained optimization problems. J. Intell. Manuf. 1–12 (2012)

  5. Chen, G., Wang, J., Li, R.: Identification of parameters in chemical kinetics using a hybrid algorithm of artificial bee colony algorithm and simplex. In: Artificial Intelligence and Computational Intelligence. Lecture Notes in Computer Science, vol. 7004, pp. 220–227. Springer, Berlin (2011)

    Chapter  Google Scholar 

  6. Cheng, X., Jiang, M.: An improved artificial bee colony algorithm based on Gaussian mutation and chaos disturbance. In: Tan, Y., Shi, Y., Ji, Z. (eds.) Advances in Swarm Intelligence. Lecture Notes in Computer Science, vol. 7331, pp. 326–333. Springer, Berlin (2012)

    Chapter  Google Scholar 

  7. Das, S., Abraham, A., Konar, A.: Automatic kernel clustering with multi-elitist particle swarm optimization algorithm. Pattern Recognit. Lett. 29, 688–699 (2008)

    Article  Google Scholar 

  8. Derrac, J., Garcia, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. 1(7), 3–18 (2011)

    Article  Google Scholar 

  9. Dorigo, M.: Optimization, learning and natural algorithms (in Italian). Ph.D. thesis, Dipartimento di Elettronica, Politecnico di Milano, Italy (1992)

  10. Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39–43 (1995)

    Chapter  Google Scholar 

  11. Eberhart, R.C., Shi, Y.: Particle swarm optimization:developments,applications and resources. In: Proceedings of the 2001 IEEE Congress on Evolutionary Computation, vol. 1, pp. 81–86 (2001)

    Google Scholar 

  12. El-Abd, M.: Performance assessment of foraging algorithms vs. evolutionary algorithms. Inf. Sci. 182(1), 243–263 (2012)

    Article  MathSciNet  Google Scholar 

  13. Finck, S., Hansen, N., Ros, R., Auger, A.: Bbob12 benchmark functions (2012). http://coco.gforge.inria.fr/doku.php?id=bbob-2012-downloads

  14. Finck, S., Hansen, N., Ros, R., Auger, A.: Real-parameter black-box optimization benchmarking 2010: presentation of the noiseless functions. Tech. rep., INRIA (2012)

  15. Gao, W.F., Liu, S.Y., Huang, L.L.: A global best artificial bee colony algorithm for global optimization. J. Comput. Appl. Math. 236(11), 2741–2753 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  16. Ghosh, S., Das, S., Kundu, D., Suresh, K., Abraham, A.: Inter-particle communication and search-dynamics of lbest particle swarm optimizers: an analysis. Inf. Sci. 182(1), 156–168 (2012)

    Article  MathSciNet  Google Scholar 

  17. Hansen, N., Auger, A., Finck, S., Ros, R.: Real-parameter black-box optimization benchmarking: experimental setup. Tech. rep., INRIA (2012)

  18. Kang, F., Li, J., Ma, Z.: Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions. Inf. Sci. 181(16), 3508–3531 (2011)

    Article  MATH  MathSciNet  Google Scholar 

  19. Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Tech. rep., Engineering Faculty, Computer Engineering Department, Erciyes University (2005)

  20. Karaboga, D.: Artificial bee colony code (2008). http://mf.erciyes.edu.tr/abc/software.htm

  21. Karaboga, D., Akay, B.: Artificial bee colony (ABC), harmony search and bees algorithms on numerical optimization. In: Proceedings of Innovative Production Machines and Systems Virtual Conference (2009)

    Google Scholar 

  22. Karaboga, D., Akay, B.: A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 214(1), 687–697 (2009)

    Article  MathSciNet  Google Scholar 

  23. Karaboga, D., Akay, B.: A modified artificial bee colony (ABC) algorithm for constrained optimization problems. Appl. Soft Comput. 11(3), 2741–2753 (2011)

    Article  Google Scholar 

  24. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39(3), 459–471 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  25. Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. J. Glob. Optim. 8(1), 687–697 (2008)

    Google Scholar 

  26. Karaboga, N., Kockanat, S., Dogan, H.: The parameter extraction of the thermally annealed Schottky barrier diode using the modified artificial bee colony. Appl. Intell. 1–10 (2012)

  27. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, Washington, DC, USA, pp. 1942–1948 (1995)

    Chapter  Google Scholar 

  28. Kennedy, J., Mendes, R.: Population structure and particle swarm performance. In: Proceedings of IEEE Congress on Evolutionary Computation, Honolulu, USA, vol. 2, pp. 1671–1676 (2002)

    Google Scholar 

  29. Li, L., Cheng, Y., Tan, L., Niu, B.: A discrete artificial bee colony algorithm for tsp problem. In: Bio-Inspired Computing and Applications. Lecture Notes in Computer Science, vol. 6840, pp. 566–573. Springer, Berlin (2012)

    Chapter  Google Scholar 

  30. Li, L., Yao, F., Tan, L., Niu, B., Xu, J.: A novel de-abc-based hybrid algorithm for global optimization. In: Bio-Inspired Computing and Applications. Lecture Notes in Computer Science, vol. 6840, pp. 558–565. Springer, Berlin (2012)

    Chapter  Google Scholar 

  31. Liang, J.J., Suganthan, P.N.: Dynamic multi-swarm particle swarm optimizer. In: Proceedings of 2005 IEEE Swarm Intelligence Symposium, pp. 124–129 (2005)

    Google Scholar 

  32. 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(3), 281–295 (2006)

    Article  Google Scholar 

  33. Lin, X., Feng, B., Sun, J.: Quantum-behaved particle swarm optimization algorithm based on bounded mutation. Comput. Eng. 34(12), 187–188 (2008)

    Google Scholar 

  34. Liu, J., Jia, Z., Qin, X., Chang, C., Xu, G., Xia, X.: The applications in channel assignment based on cooperative hybrid artificial bee colony algorithm. In: Advances in Electrical Engineering and Automation, Advances in Intelligent and Soft Computing, vol. 139, pp. 401–406. Springer, Berlin (2012)

    Chapter  Google Scholar 

  35. Manuel, M., Elias, E.: Design of frequency response masking FIR filter in the canonic signed digit space using modified artificial bee colony algorithm. Eng. Appl. Artif. Intell. 26(1), 660–668 (2013)

    Article  Google Scholar 

  36. Particle Swarm Central: standard pso 2011 code (2011). http://www.particleswarm.info

  37. Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. 22(3), 52–67 (2002)

    Article  MathSciNet  Google Scholar 

  38. Peng, Y., Peng, X.Y., Liu, Z.G.: Statistic analysis on parameter efficiency of particle swarm optimization. Acta Electron. Sin. 32(2), 209–213 (2004)

    MathSciNet  Google Scholar 

  39. Pham, D.T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., Zaidi, M.: The bees algorithm-a novel tool for complex optimization problems. In: Proceedings of Innovative Production Machines and Systems Virtual Conference, pp. 451–461 (2006)

    Google Scholar 

  40. Rajasekhar, A., Abraham, A., Jatoth, R.: Controller tuning using a Cauchy mutated artificial bee colony algorithm. In: Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011. Advances in Intelligent and Soft Computing, vol. 87, pp. 11–18. Springer, Berlin (2011)

    Chapter  Google Scholar 

  41. Rajasekhar, A., Chaitanya, V., Das, S.: Fractional-order PIλDμ controller design using a modified artificial bee colony algorithm. In: Swarm, Evolutionary, and Memetic Computing. Lecture Notes in Computer Science, vol. 7076, pp. 670–678. Springer, Berlin (2011)

    Chapter  Google Scholar 

  42. Samal, N.R., Konar, A., Das, S., Nagar, A.: Parameter selection for a particle swarm optimization dynamics by closed loop stability analysis. Int. J. Comput. Sci. Math. 3(3), 245–274 (2010)

    Article  MATH  Google Scholar 

  43. Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 69–73. IEEE Press, Piscataway (1998)

    Google Scholar 

  44. Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.P., Auger, A., Tiwari, S.: CEC05 benchmark functions (2005). http://www.ntu.edu.sg/home/EPNSugan/

  45. Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.P., Auger, A., Tiwari, S.: Problem definitions and evaluation criteria for the cec 2005 special session on real-parameter optimization. Tech. rep., Nanyang Technological University, Singapore (2005)

  46. Wang, L., Zhou, G., Xu, Y., Wang, S., Liu, M.: An effective artificial bee colony algorithm for the flexible job-shop scheduling problem. Int. J. Adv. Manuf. Technol. 60, 303–315 (2012)

    Article  Google Scholar 

  47. Wu, B., hai Fan, S.: Improved artificial bee colony algorithm with chaos. In: Yu, Y., Yu, Z., Zhao, J. (eds.) Computer Science for Environmental Engineering and EcoInformatics. Communications in Computer and Information Science, vol. 158, pp. 51–56. Springer, Berlin (2011)

    Chapter  Google Scholar 

  48. Wu, B., Qian, C., Ni, W., Fan, S.: Hybrid harmony search and artificial bee colony algorithm for global optimization problems. Comput. Math. Appl. 64(8), 2621–2634 (2012)

    Article  MATH  MathSciNet  Google Scholar 

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Acknowledgements

This paper is supported by the major research project of Guangdong Baiyun University (No. BYKY201217). The authors thank the anonymous reviewers for providing valuable comments to improve this paper, and add special thanks to Professor P.N. Suganthan for his excellent MATLAB codes in implementing DMS-PSO and CLPSO algorithms.

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Authors and Affiliations

  1. Department of Basic Courses, Guangdong Baiyun University, Guangzhou, 510450, P.R. China

    Yi Xiang, Yuming Peng, Zhenyu Chen, Xuwen Lu & Xuejun Zhong

  2. School of Mathematics and Information Sciences, Guangzhou University, Guangzhou, 510006, P.R. China

    Yubin Zhong

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  1. Yi Xiang
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Correspondence to Yi Xiang.

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Xiang, Y., Peng, Y., Zhong, Y. et al. A particle swarm inspired multi-elitist artificial bee colony algorithm for real-parameter optimization. Comput Optim Appl 57, 493–516 (2014). https://doi.org/10.1007/s10589-013-9591-2

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