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Solving 0–1 Knapsack Problems by Binary Dragonfly Algorithm

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Intelligent Computing Methodologies (ICIC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10363))

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Abstract

The 0–1 knapsack problem (0–1KP) is a well-known combinatorial optimization problem. It is an NP-hard problem which plays significant roles in many real life applications. Dragonfly algorithm (DA) a novel swarm intelligence optimization algorithm, inspired by the nature of static and dynamic swarming behaviors of dragonflies. DA has demonstrated excellent performance in solving multimodal continuous problems and engineering optimization problems. This paper proposes a binary version of dragonfly algorithm (BDA) to solve 0–1 knapsack problem. Experimental results have proven the superior performance of BDA compared with other algorithms in literature.

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References

  1. Hu, T.C., Kahng, A.B.: The knapsack problem. In: Hu, T.C., Kahng, Andrew B. (eds.) Linear and Integer Programming Made Easy, pp. 87–101. Springer, Cham (2016). doi:10.1007/978-3-319-24001-5_8

    Chapter  Google Scholar 

  2. Martello, S., Pisinger, D., Toth, P.: New trends in exact algorithms for the 0–1 knapsack problem. Eur. J. Oper. Res. 123(2), 325–332 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  3. Plateau, G., Nagih, A.: 0–1 knapsack problems. In: Paradigms of Combinatorial Optimization, 2nd edn., pp. 215–242 (2010)

    Google Scholar 

  4. Kulkarni, A.J., Krishnasamy, G., Abraham, A.: Solution to 0–1 knapsack problem using cohort intelligence algorithm. In: Kulkarni, A.J., Krishnasamy, G., Abraham, A. (eds.) Cohort Intelligence: A Socio-inspired Optimization Method. ISRL, vol. 114, pp. 55–74. Springer, Cham (2017). doi:10.1007/978-3-319-44254-9_5

    Chapter  Google Scholar 

  5. Zhou, Y., Bao, Z., Luo, Q., Zhang, S.: A complex-valued encoding wind driven optimization for the 0–1 knapsack problem. Appl. Intell. 1–19 (2016)

    Google Scholar 

  6. Lv, J., Wang, X., Huang, M., Cheng, H., Li, F.: Solving 0-1 knapsack problem by greedy degree and expectation efficiency. Appl. Soft Comput. 41, 94–103 (2016)

    Article  Google Scholar 

  7. Zhou, Y., Chen, X., Zhou, G.: An improved monkey algorithm for a 0-1 knapsack problem. Appl. Soft Comput. 38, 817–830 (2016)

    Article  Google Scholar 

  8. Lim, T.Y., Al-Betar, M.A., Khader, A.T.: Taming the 0/1 knapsack problem with monogamous pairs genetic algorithm. Expert Syst. Appl. 54, 241–250 (2016)

    Article  Google Scholar 

  9. Nguyen, P.H., Wang, D., Truong, T.K.: A new hybrid particle swarm optimization and greedy for 0-1 knapsack problem. Indones. J. Electr. Eng. Comput. Sci. 1(3), 411–418 (2016)

    Article  Google Scholar 

  10. Pavithr, R.S.: Quantum inspired social evolution (QSE) algorithm for 0–1 knapsack problem. In: Swarm and Evolutionary Computation (2016, in press)

    Google Scholar 

  11. Lin, C.J., Chern, M.S., Chih, M.: A binary particle swarm optimization based on the surrogate information with proportional acceleration coefficients for the 0-1 multidimensional knapsack problem. J. Indu. Prod. Eng. 33(2), 77–102 (2016)

    Google Scholar 

  12. Truong, T.K., Li, K., Xu, Y., Ouyang, A., Nguyen, T.T.: Solving 0-1 knapsack problem by artificial chemical reaction optimization algorithm with a greedy strategy. J. Intell. Fuzzy Syst. 28(5), 2179–2186 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  13. Kong, X., Gao, L., Ouyang, H., Li, S.: A simplified binary harmony search algorithm for large scale 0–1 knapsack problems. Expert Syst. Appl. 42(12), 5337–5355 (2015)

    Article  Google Scholar 

  14. Yan, C., Gao, S., Luo, H., Hu, Z.: A hybrid algorithm based on tabu search and chemical reaction optimization for 0-1 knapsack problem. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds.) ICSI 2015. LNCS, vol. 9141, pp. 229–237. Springer, Cham (2015). doi:10.1007/978-3-319-20472-7_25

    Chapter  Google Scholar 

  15. Du, D.-P., Zu, Y.-R.: Greedy strategy based self-adaption ant colony algorithm for 0/1 knapsack problem. In: Park, J.J., Pan, Y., Chao, H.-C., Yi, G. (eds.) Ubiquitous Computing Application and Wireless Sensor. LNEE, vol. 331, pp. 663–670. Springer, Dordrecht (2015). doi:10.1007/978-94-017-9618-7_70

    Google Scholar 

  16. Zhou, Y., Li, L., Ma, M.: A complex-valued encoding bat algorithm for solving 0–1 knapsack problem. Neural Process. Lett. 44(2), 407–430 (2016)

    Google Scholar 

  17. Razavi, S.F., Sajedi, H.: Cognitive discrete gravitational search algorithm for solving 0-1 knapsack problem. J. Intell. Fuzzy Syst. 29(5), 2247–2258 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  18. Feng, Y., Jia, K., He, Y.: An improved hybrid encoding cuckoo search algorithm for 0-1 knapsack problems. Comput. Intell. Neurosci. 2014, 1 (2014)

    Google Scholar 

  19. Cheng, K., Ma, L.: Artificial glowworm swarm optimization algorithm for 0-1 knapsack problem. Appl. Res. Comput. 4, 009 (2013)

    Google Scholar 

  20. Fang, Z., Yu-Lei, M., Jun-Peng, Z.: Solving 0-1 knapsack problem based on immune clonal algorithm and ant colony algorithm. In: Yang, G. (ed.) Proceedings of the 2012 International Conference on Communication, Electronics and Automation Engineering. AISC, vol. 181, pp. 1047–1053. Springer, Heidelberg (2013). doi:10.1007/978-3-642-31698-2_148

    Chapter  Google Scholar 

  21. Gupta, M.: A fast and efficient genetic algorithm to solve 0-1 Knapsack problem. Int. J. Digit. Appl. Contemp. Res 1(6), 1–5 (2013)

    Google Scholar 

  22. Layeb, A.: A hybrid quantum inspired harmony search algorithm for 0–1 optimization problems. J. Comput. Appl. Math. 253, 14–25 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  23. Gherboudj, A., Layeb, A., Chikhi, S.: Solving 0-1 knapsack problems by a discrete binary version of cuckoo search algorithm. Int. J. Bio-Inspired Comput. 4(4), 229–236 (2012)

    Article  Google Scholar 

  24. Zou, D., Gao, L., Li, S., Wu, J.: Solving 0–1 knapsack problem by a novel global harmony search algorithm. Appl. Soft Comput. 11(2), 1556–1564 (2011)

    Article  Google Scholar 

  25. Gong, Q.Q., Zhou, Y.Q., Yang, Y.: Artificial glowworm swarm optimization algorithm for solving 0-1 knapsack problem. In: Advanced Materials Research, vol. 143, pp. 166–171. Trans Tech Publications (2011)

    Google Scholar 

  26. Layeb, A.: A novel quantum inspired cuckoo search for knapsack prob lems. Int. J. Bio-Inspired Comput. 3(5), 297–305 (2011)

    Article  Google Scholar 

  27. Kong, M., Tian, P.: Apply the particle swarm optimization to the multidimensional knapsack problem. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS, vol. 4029, pp. 1140–1149. Springer, Heidelberg (2006). doi:10.1007/11785231_119

    Chapter  Google Scholar 

  28. Xiang, W.L., An, M.Q., Li, Y.Z., He, R.C., Zhang, J.F.: A novel discrete global-best harmony search algorithm for solving 0-1 knapsack problems. Discret. Dyn. Nat. Soc. 2014, 19 p. (2014). Article no. 637412. http://dx.doi.org/10.1155/2014/637412

  29. Changdar, C., Mahapatra, G.S., Pal, R.K.: An ant colony optimization approach for binary knapsack problem under fuzziness. Appl. Math. Comput. 223, 243–253 (2013)

    MathSciNet  MATH  Google Scholar 

  30. Bansal, J.C., Deep, K.: A modified binary particle swarm optimization for knapsack problems. Appl. Math. Comput. 218(22), 11042–11061 (2012)

    MathSciNet  MATH  Google Scholar 

  31. Ma, Y., Wan, J.: Improved hybrid adaptive genetic algorithm for solving knapsack problem. In: 2011 2nd International Conference on Intelligent Control and Information Processing (2011)

    Google Scholar 

  32. Mirjalili, S.: Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput. Appl. 27(4), 1053–1073 (2016)

    Article  Google Scholar 

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Acknowledgments

This work is supported by National Science Foundation of China under Grants Nos. 61563008; 61463007. Project of Guangxi Science Foundation under Grant No. 2016GXNSFAA380264.

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Authors and Affiliations

  1. Head of Department of Operations Research, Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt

    Mohamed Abdel-Basset

  2. College of Information Science and Engineering, Guangxi University for Nationalities, Nanning, 530006, China

    Qifang Luo, Fahui Miao & Yongquan Zhou

  3. Guangxi High School Key Laboratory of Complex System and Computational Intelligence, Nanning, 530006, China

    Qifang Luo & Yongquan Zhou

Authors
  1. Mohamed Abdel-Basset
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  2. Qifang Luo
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  3. Fahui Miao
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  4. Yongquan Zhou
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Corresponding author

Correspondence to Qifang Luo .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Liverpool John Moores University, Liverpool, United Kingdom

    Abir Hussain

  3. Inha University, Incheon, Korea (Republic of)

    Kyungsook Han

  4. Indian Institute of Technology Madras, Chennai, India

    M. Michael Gromiha

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Abdel-Basset, M., Luo, Q., Miao, F., Zhou, Y. (2017). Solving 0–1 Knapsack Problems by Binary Dragonfly Algorithm. In: Huang, DS., Hussain, A., Han, K., Gromiha, M. (eds) Intelligent Computing Methodologies. ICIC 2017. Lecture Notes in Computer Science(), vol 10363. Springer, Cham. https://doi.org/10.1007/978-3-319-63315-2_43

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  • DOI: https://doi.org/10.1007/978-3-319-63315-2_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63314-5

  • Online ISBN: 978-3-319-63315-2

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