Skip to main content

Advertisement

SpringerLink
Log in

A complex-valued encoding wind driven optimization for the 0-1 knapsack problem

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

This paper presents a complex-valued encoding wind driven optimization (CWDO) with a greedy strategy for the 0-1 knapsack problem. We introduce a complex-value encoding method, which can be viewed as an effective global optimization strategy, and a greedy strategy, which can be viewed as an enhanced local search strategy into wind driven optimization. These strategies increase the diversity of the population and avoid premature convergence. This paper presents three types of test cases for evaluation: standard, small-scale, and large-scale test cases. The experimental results show that the proposed algorithm is suitable for these three cases. Compared to the complex valued cuckoo search algorithm, greedy genetic algorithm, wind driven optimization, binary cuckoo search algorithm, bat algorithm and particle swarm optimization, the performance, stability, and robustness of the CWDO algorithm is better. The simulation results show that the CWDO algorithm is an effective and feasible method for solving the 0-1 knapsack problem.

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
Fig. 28
Fig. 29
Fig. 30
Fig. 31
Fig. 32
Fig. 33
Fig. 34
Fig. 35
Fig. 36
Fig. 37
Fig. 38
Fig. 39
Fig. 40
Fig. 41
Fig. 42
Fig. 43
Fig. 44
Fig. 45
Fig. 46
Fig. 47
Fig. 48
Fig. 49
Fig. 50

Similar content being viewed by others

References

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

    Article  MathSciNet  MATH  Google Scholar 

  2. Zhou Y, Li L, Ma M (2016) A Complex-valued Encoding Bat Algorithm for Solving 0–1 Knapsack Problem. Neural Process Lett 44:407430

    Google Scholar 

  3. Kulkarni AJ, Shabir H (2016) Solving 0–1 knapsack problem using cohort intelligence algorithm. Int J Mach Learn Cybern 7(1):427–441

    Article  Google Scholar 

  4. Gherboudj A, Layeb A, Chikhi S (2012) Solving 0-1 knapsack problems by a discrete binary version of cuckoo search algorithm. Int J Bio-Inspired Computqq 4(2):229–236

    Article  Google Scholar 

  5. Du D, Zu Y (2015) Greedy Strategy Based Self-adaption Ant Colony Algorithm for 0/1 Knapsack ProblemUbiquitous Computing Application and Wireless Sensor, Netherlands, pp 663–670

  6. Bayraktar Z, Komurcu M, Werner DH (2010) Wind Driven Optimization (WDO). A novel nature-inspired optimization algorithm and its application to electromagnetics. Antennas and Propagation Society International Symposium (APSURSI), 2010 IEEE. IEEE

  7. Bayraktar Z, Komurcu M, Bossard J, et al. (2013) The wind driven optimization technique and its application in electromagnetics. IEEE Trans Antennas Propag 61(3):2745–2757

    Article  MathSciNet  Google Scholar 

  8. Bhandari AK, Singh VK, Kumar A, et al. (2014) Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy. Expert Syst Appl 41 (5):3538–3560

    Article  Google Scholar 

  9. Sun J, Wang X, Huang M, et al. (2013) A Cloud Resource Allocation Scheme Based on Microeconomics and Wind Driven Optimization. In: 2013 8th China Grid Annual Conference (China Grid). IEEE, pp 34–39

  10. Boulesnane A, Meshoul S (2014) A new multi-region modified wind driven optimization algorithm with collision avoidance for dynamic environments. Advances in Swarm Intelligence. Springer International Publishing, pp 412–421

  11. Kuldeep B, Singh VK, Kumar A, et al. (2015) Design of two-channel filter bank using nature inspired optimization based fractional derivative constraints. ISA Trans 54:101–116

    Article  Google Scholar 

  12. Mahto SK, Choubey A, Suman S Linear array synthesis with minimum side lobe level and null control using wind driven optimization. In: 2015 International Conference on Signal Processing And Communication Engineering System (SPACES). IEEE, pp. 191–195

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

    Google Scholar 

  14. Riehl H (1978) Introduction to the Atmosphere. McGraw Hill

  15. Ahrens CD (2003) Meteorology Today: An Introduction to Weather, Climate, and the Environment, 7th ed. Thomson–Brook/Cole, Belmont

    Google Scholar 

  16. Zhao-hui Z, Zhang Y, Qiu Y-H (2003) Genetic algorithm based on complex-valued encoding. IET Control Theory Appl 1:021

    Google Scholar 

  17. Zhao JF, Huang TL, Pang F, et al. (2009) Genetic algorithm based on greedy strategy in the 0-1 knapsack problem. In: 3rd International Conference on Genetic and Evolutionary Computing, 2009. WGEC’09. IEEE, pp 105–107

  18. Zhou Y, Zheng H (2013) A novel complex valued cuckoo search algorithm. Sci World J 2013(2013). Article ID 597803, 6 pages

  19. He YC, Liu KQ, Zhang CJ, et al. (2007) Greedy genetic algorithm for solving knapsack problems and its application. Commun Eng Des Mag 28:2655–2657

    Google Scholar 

  20. Bhattacharjee KK, Sarmah SP (2016) Modified swarm intelligence based techniques for the knapsack problem. Appl Intell:1–22

  21. Mirjalili S, Mirjalili SM, Yang XS (2014) Binary bat algorithm. Neural Comput & Applic 25(3-4):663–681

    Article  Google Scholar 

  22. Hembecker F, Lopes HS, Godoy JW (2007) Particle swarm optimization for the multidimensional knapsack problem. Adaptive and Natural Computing Algorithms:358–365

Download references

Acknowledgments

The authors would like to thank the anonymous reviewers for their careful review and constructive comments. This work was supported by National Science Foundation of China (Grant No. 61463007, 61563008).

Author information

Authors and Affiliations

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

    Yongquan Zhou, Zongfan Bao, Qifang Luo & Sen Zhang

  2. Schools Key Laboratory of Guangxi Highs Complex System and Computational Intelligence, Nanning, 530006, China

    Yongquan Zhou & Qifang Luo

Authors
  1. Yongquan Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zongfan Bao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qifang Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sen Zhang
    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., Bao, Z., Luo, Q. et al. A complex-valued encoding wind driven optimization for the 0-1 knapsack problem. Appl Intell 46, 684–702 (2017). https://doi.org/10.1007/s10489-016-0855-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-016-0855-2

Keywords

Search

Navigation