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Multi-objective Firefly Algorithm Guided by Elite Particle

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Computational Intelligence and Intelligent Systems (ISICA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 873))

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Abstract

With the diversification and complexity of the social needs, multi-objective optimization problems gradually attract more and more attention. The traditional multi-objective optimization algorithms cannot meet the practical needs. Therefore, it is urgent to improve and develop new multi-objective optimization algorithms to meet the challenges. On the basis of standard firefly algorithm, this paper proposed a multi-objective firefly algorithm based on population evolution guided by elite particle. The algorithm randomly selects a non-inferior solution as the elite particle to participate in the population evolution, extends the detection range of firefly, and improves the diversity and accuracy of the non-inferior solution set. The experimental results show that the proposed algorithm is superior to the MOPSO, MOEA/D, PESA-II, NSGA-III algorithm on the GD, SP, MS and other quantitative indexes for the seven classic test functions, and the proposed algorithm is an effective method for multi-objective optimization.

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References

  1. Zhao, J., Fan, T., Lv, L., Sun, H., Wang, J.: Adaptive intelligent single particle optimizer based image de-noising in shearlet domain. Intell. Autom. Soft Comput. 23(4), 661–666 (2017)

    Article  Google Scholar 

  2. Zhao, J., Lv, L., Wang, H., Sun, H., Wu, R., Nie, J., Xie, Z.: Particle swarm optimization based on vector gaussian learning. KSII Trans. Internet Inf. Syst. 11(4), 2038–2057 (2017)

    Google Scholar 

  3. Sun, H., Wang, K., Zhao, J., Yu, X.: Artificial bee colony algorithm with improved special centre. Int. J. Comput. Sci. Math. 7(6), 548–553 (2016)

    Article  MathSciNet  Google Scholar 

  4. Lv, L., Wu, L.Y., Zhao, J., Wang, H., Wu, R.X., Fan, T.H., Hu, M., Xie, Z.F.: Improved multi-strategy artificial bee colony algorithm. Int. J. Comput. Sci. Math. 7(5), 467–475 (2016)

    Article  MathSciNet  Google Scholar 

  5. Lu, Y., Li, R.X., Li, S.M.: Artificial bee colony with bidirectional search. Int. J. Comput. Sci. Math. 7(6), 586–593 (2016)

    Article  MathSciNet  Google Scholar 

  6. Yun, G.: A new multi-population-based artificial bee colony for numerical optimization. Int. J. Comput. Sci. Math. 7(6), 509–515 (2016)

    Article  MathSciNet  Google Scholar 

  7. Cui, Z., Sun, B., Wang, G., Xue, Y.: A novel oriented cuckoo search algorithm to improve DV-Hop performance for cyber-physical systems. J. Parallel Distrib. Comput. 77(103), 42–52 (2017)

    Article  Google Scholar 

  8. Wang, G.G., Gandomi, A.H., Yang, X.S., Alavi, A.H.: A new hybrid method based on krill herd and cuckoo search for global optimization tasks. Int. J. Bio-Inspir. Comput. 8(5), 286–299 (2016)

    Article  Google Scholar 

  9. Cai, X., Wang, L., Kang, Q., Wu, Q.: Bat algorithm with Gaussian walk. Int. J. Bio-Inspir. Comput. 6(3), 166–174 (2014)

    Article  Google Scholar 

  10. Cai, X., Gao, X.Z., Xue, Y.: Improved bat algorithm with optimal forage strategy and random disturbance strategy. Int. J. Bio-Inspir. Comput. 8(4), 205–214 (2016)

    Article  Google Scholar 

  11. Xue, F., Cai, Y., Cao, Y., Cui, Z., Li, F.: Optimal parameter settings for bat algorithm. Int. J. Bio-Inspir. Comput. 7(2), 125–128 (2015)

    Article  Google Scholar 

  12. Cui, Z., Fan, S., Zeng, J., Shi, Z.Z.: APOA with parabola model for directing orbits of chaotic systems. Int. J. Bio-Inspir. Comput. 5(1), 67–72 (2013)

    Article  Google Scholar 

  13. Cui, Z., Fan, S., Zeng, J., Shi, Z.Z.: Artificial plant optimisation algorithm with three-period photosynthesis. Int. J. Bio-Inspir. Comput. 5(2), 133–139 (2013)

    Article  Google Scholar 

  14. Xiao, X.W., Xiao, D., Lin, J.G., et al.: Overview on multi-objective optimization problem research. Appl. Res. Comput. 28(3), 805–808 (2011)

    Google Scholar 

  15. Zheng, X.W.: Progress of research on multi-objective evolutionary algorithms. Comput. Sci. 34(7), 187–192 (2007)

    Article  Google Scholar 

  16. Fonseca, C.M., Fleming, P.J.: Genetic algorithms for multi-objective optimization: formulation discussion and generalization. In: Proceedings of the 5th International Conference on Genetic Algorithms, pp. 416–423. Morgan Kaufmann Publishers Inc. (1993)

    Google Scholar 

  17. Srinivas, N., Deb, K.: Multi-objective optimization using non-dominated sorting in genetic algorithms. Evol. Comput. 2(3), 221–248 (1994)

    Article  Google Scholar 

  18. Coello, C.A.C., Pulido, G.T., Lechuga, M.S.: Handling multiple objectives with particle swarm optimization. IEEE Trans. Evol. Comput. 8(3), 256–279 (2004)

    Article  Google Scholar 

  19. Wang, H., Wang, W.J., Sun, H., Rahnamayan, S.: Firefly algorithm with random attraction. Int. J. Bio-Inspir. Comput. 8(1), 33–41 (2016)

    Article  Google Scholar 

  20. Lv, L., Zhao, J.: The firefly algorithm with Gaussian disturbance and local search. J. Signal Process. Syst. 9(11), 1–9 (2017)

    Google Scholar 

  21. Wang, H., Wang, W.J., Zhou, X.Y., Sun, H., Zhao, J., Yu, X., Cui, Z.: Firefly algorithm with neighborhood attraction. Inf. Sci. 23(s), 382–383, 374–387 (2017)

    Google Scholar 

  22. Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, Somerset (2008)

    Google Scholar 

  23. Yang, X.S.: Multi-objective firefly algorithm for continuous optimization. Eng. Comput. 29(2), 175–184 (2013)

    Article  Google Scholar 

  24. Deb, K., Gupta, H.: Introducing robustness in multi-objective optimization. Evol. Comput. 14(4), 463–494 (2014)

    Article  Google Scholar 

  25. Yang, J.J., Zhou, J.Z., Wan, R.C.: Multi-objective particle swarm optimization based on adaptive grid algorithms. J. Syst. Simul. 20(21), 5843–5847 (2008)

    Google Scholar 

  26. Li, K., Kwong, S., Deb, K.: A dual-population paradigm for evolutionary multi-objective optimization. Inf. Sci. 309(C), 50–72 (2015)

    Google Scholar 

  27. Zitzler, E., Deb, K., Thiele, L.: Comparison of multi-objective evolutionary algorithms: empirical results. Evol. Comput. 8(2), 173–195 (2000)

    Article  Google Scholar 

  28. Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms - a comparative case study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 292–301. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0056872

    Chapter  Google Scholar 

  29. Tang, L.X., Wang, X.P.: A hybrid multi-objective evolutionary algorithm for multi-objective optimization problems. IEEE Trans. Evol. Comput. 17(1), 20–46 (2013)

    Article  MathSciNet  Google Scholar 

  30. Mkaouer, W., Kessentini, M., Shaout, A.: Many-objective software remodularization using NSGA-III. ACM Trans. Softw. Eng. Method. 24(3), 1–45 (2015)

    Article  Google Scholar 

  31. Zhang, Q.F., Li, H.: MOEA/D: a multi-objective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)

    Article  Google Scholar 

  32. Corne, D.W., Jerram, N.R., Knowles, J.D., Oates, M.J.: PESA-II: region-based selection in evolutionary multi-objective optimization. In: Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation, pp. 283–290, Morgan Kaufmann Publishers Inc. (2001)

    Google Scholar 

  33. Schott, J.R.: Fault tolerant design using single and multi-criteria genetic algorithm optimization. Cell. Immunol. 37(1), 1–13 (1995)

    Google Scholar 

  34. Veldhuizen, D.A.V.: Multi-objective evolutionary algorithms: classifications, analyses, and new innovations. Evol. Comput. 8(2), 125–147 (1999)

    Article  Google Scholar 

  35. Cai, Z., Wang, Y.: A Multi-objective optimization-based evolutionary algorithm for constrained optimization. IEEE Trans. Evol. Comput. 10(6), 658–675 (2006)

    Article  Google Scholar 

Download references

Acknowledgment

This research was supported by the Jiangxi Province Department of Education Science and Technology Project under Grant (No. GJJ161108), the National Natural Science Foundation of China under Grant (Nos. 61663029, 51669014, 61663028), Science Foundation of Jiangxi Province under Grant (Nos. 20161BAB212037, 20171BAB202035), National Undergraduate Training Programs for Innovation and Entrepreneurship under Grant (No. 201711319001) and the project of Nanchang Institute of Technology’s graduate student innovation program under Grant (No. YJSCX2017023).

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Authors and Affiliations

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

    Jiayuan Wang, Li Lv, Zhifeng Xie, Xi Zhang, Hui Wang & Jia Zhao

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  1. Jiayuan Wang
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  2. Li Lv
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  3. Zhifeng Xie
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  4. Xi Zhang
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  5. Hui Wang
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  6. Jia Zhao
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Corresponding author

Correspondence to Li Lv .

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Editors and Affiliations

  1. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China

    Kangshun Li

  2. Jiangxi University of Science and Technology, Ganzhou, Jiangxi, China

    Wei Li

  3. Chemical and Petroleum Engineering, University of Calgary, Calgary, Alberta, Canada

    Zhangxing Chen

  4. School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

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Wang, J., Lv, L., Xie, Z., Zhang, X., Wang, H., Zhao, J. (2018). Multi-objective Firefly Algorithm Guided by Elite Particle. In: Li, K., Li, W., Chen, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2017. Communications in Computer and Information Science, vol 873. Springer, Singapore. https://doi.org/10.1007/978-981-13-1648-7_14

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  • DOI: https://doi.org/10.1007/978-981-13-1648-7_14

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  • Print ISBN: 978-981-13-1647-0

  • Online ISBN: 978-981-13-1648-7

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