Skip to main content
SpringerLink
Log in

A Complex-valued Encoding Bat Algorithm for Solving 0–1 Knapsack Problem

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

This paper proposes a novel complex-valued encoding bat algorithm (CPBA) for solving 0–1 knapsack problem. The complex-valued encoding method which can be considered as an efficient global optimization strategy is introduced to the bat algorithm. Based on the two-dimensional properties of the complex number, the real and imaginary parts of complex number are updated separately. The proposed algorithm can effectively diversify bat population and improving the convergence performance. The CPBA enhances exploration ability and is effective for solving both small-scale and large-scale 0–1 knapsack problem. Finally, numerical simulation is carried out, and the comparison results with some existing algorithms demonstrate the validity and stability of the proposed algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27

Similar content being viewed by others

References

  1. Kellerer H, Pferschy U, Pisinger D (2005) Knapsack problems. Springer, Berlin

    MATH  Google Scholar 

  2. Shen W, Xu B, Huang J (2011) An improved genetic algorithm for 0-1 knapsack problems. In: Second international conference on networking and distributed computing (ICNDC), pp 32–35

  3. Lin F-T (2008) Solving the knapsack problem with imprecise weight coefficients using genetic algorithms. Eur J Oper Re 185(1):133–145

    Article  MATH  Google Scholar 

  4. He Y, Liu K, Zhang C, Zhang W (2007) Greedy genetic algorithm for solving knapsack problems and its applications. Comput Eng Des 28(11):2656–2657

    MathSciNet  Google Scholar 

  5. Shi H (2006) Solution to 0/1 knapsack problem based on improved ant colony algorithm. In: IEEE international conference on information acquisition, pp 1062–1066

  6. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks. IEEE Press, Piscataway, pp 1942–1948

  7. Li ZK, Li N (2009) A novel multi-mutation binary particle swarm optimization for 0/1 knapsack problem. In: Control and decision conference, pp 3042–3047

  8. LI X, SHAO Z, QIAN J (2002) An Optimizing method based on autonomous animals: fish-swarm algorithm. Syst Eng-Theory Pract 22(11):32–38

    Google Scholar 

  9. Azada MAK, Rocha AMAC, Fernandes EMGP (2014) Improved binary artificial fish swarm algorithm for the 0–1 multidimensional knapsack problems. Swarm Evol Comput 14:66–75

    Article  Google Scholar 

  10. Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010), pp 65–74

  11. Yang XS, Gandomi AH (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29(5):464–483

    Article  Google Scholar 

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

    Article  Google Scholar 

  13. Gandomi AH, Yang X-S, Alavi AH (2013) Bat algorithm for constrained optimization tasks. Neural Comput Appl 22:1239–1255

    Article  Google Scholar 

  14. Gandomi AH, Yang X-S (2014) Chaotic bat algorithm. J Comput Sci 5:224–232

    Article  MathSciNet  Google Scholar 

  15. Gandomi AH, Alavi AH (2012) Krill Herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845

    Article  MathSciNet  MATH  Google Scholar 

  16. Truong TK, Li K, Xu Y (2013) Chemical reaction optimization with greedy strategy for the 0–1 knapsack problem. Appl Soft Comput 13:1774–1780

    Article  Google Scholar 

  17. Li Z, MA L, Zhang H (2012) Genetic mutation bat algorithm for 0–1 knapsack problem. Comput Eng Appl 48(4):50–53

    MathSciNet  Google Scholar 

  18. Chen D, Li H, Li Z (2009) Particle swarm optimization based on complex-valued encoding and application in function optimization. Comput Eng Appl 45(10):59–61

    Google Scholar 

  19. Zheng Z, Zhang Y, Qiu Y (2003) Genetic algorithm based on complex-valued encoding. Control Theory Appl 20(1):97–100

    Google Scholar 

  20. Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):4–31

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by National Science Foundation of China under Grant No. 61165015; 61463007. Key Project of Guangxi Science Foundation under Grant No. 2012GXNSFDA053028, and the Innovation Project of Guangxi Graduate EducationunderGrantNo.gxun-chx2014089.

Author information

Authors and Affiliations

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

    Yongquan Zhou, Liangliang Li & Mingzhi Ma

  2. Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Nanning, 530006, China

    Yongquan Zhou

Authors
  1. Yongquan Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liangliang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mingzhi Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yongquan Zhou.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, Y., Li, L. & Ma, M. A Complex-valued Encoding Bat Algorithm for Solving 0–1 Knapsack Problem. Neural Process Lett 44, 407–430 (2016). https://doi.org/10.1007/s11063-015-9465-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-015-9465-y

Keywords

Search

Navigation