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A modified hybrid rice optimization algorithm for solving 0-1 knapsack problem

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Abstract

The 0-1 knapsack problem (KP) is a classic NP-hard problem and could be handled by swarm intelligence algorithms. However, most of these algorithms might be trapped in the local optima as the scale increases. Hybrid rice optimization (HRO) is a novel swarm intelligence algorithm inspired by the breeding process of Chinese three-line hybrid rice, its population is classified into three types such as the maintainer, restorer and sterile line and several stages including hybridization, selfing and renewal are implemented. In this paper, a modified HRO algorithm is proposed for the complicated large-scale 0-1 KP. A dynamic step is introduced in the renewal stage to balance the exploration and exploitation phases. Moreover, HRO is combined with binary ant colony optimization (BACO) algorithm to compose the parallel model and serial model for enhancing the convergence speed and search efficiency. In the parallel model, HRO and BACO are independently implemented on two subpopulations and communicate during each iteration. In the serial model, BACO is embedded in HRO as an operator to update the maintainer line. The experimental results on 0-1 KPs of different scales and correlations demonstrate the outperformance of the parallel model and serial model.

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References

  1. Martello S (1990) Knapsack problems: algorithms and computer implementations. Wiley, New York

    MATH  Google Scholar 

  2. Pisinger D, Toth P (1998) Knapsack problems. In: Handbook of combinatorial optimization. Springer, Boston, pp 299–428

  3. Pisinger D (2005) Where are the hard knapsack problems? Comput Oper Res 32(9):2271–2284

    Article  MathSciNet  Google Scholar 

  4. Merkle R, Hellman M (1978) Hiding information and signatures in trapdoor knapsacks. IEEE Trans Inf Theory 24(5):525–530

    Article  Google Scholar 

  5. Skiena SS (1999) Who is interested in algorithms and why? lessons from the Stony Brook Algorithm Repository. ACM SIGACT News 30(3):65–74

    Article  Google Scholar 

  6. Hatemi JA, El-Khatib Y (2015) Portfolio selection: an alternative approach. Econ Lett 135:141–143

    Article  MathSciNet  Google Scholar 

  7. Mansini R, Speranza MG (2002) A multidimensional knapsack model for asset-backed securitization. J Oper Res Soc 53(8):822–832

    Article  Google Scholar 

  8. Liu J, Bi J, Xu S et al (2019) An improved attack on the basic merkle–hellman knapsack cryptosystem. IEEE Access 7:59388–59393

    Article  Google Scholar 

  9. Zhang Y, Lin X, Liu X et al (2010) An improved high-density knapsack-type public key cryptosystem. In: 5th International Conference on Software and Data Technologies, pp 127–133

  10. Li X, Liu T (2010) On exponential time lower bound of knapsack under backtracking. Theor Comput Sci 411(16-18):1883–1888

    Article  MathSciNet  Google Scholar 

  11. Klamroth K, Wiecek MM (2000) Dynamic programming approaches to the multiple criteria knapsack problem. Nav Res Logist 47(1):57–76

    Article  MathSciNet  Google Scholar 

  12. Bettinelli A, Cacchiani V, Malaguti E, et al. (2017) A branch-and-bound algorithm for the knapsack problem with conflict graph. Informs J Comput 29(3):457–473

    Article  MathSciNet  Google Scholar 

  13. Billionnet A, Soutif E (2004) An exact method based on Lagrangian decomposition for the 0–1 quadratic knapsack problem. Eur J Oper Res 157(3):565–575

    Article  MathSciNet  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  16. 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 Comput 4(4):229–236

    Article  Google Scholar 

  17. Abdel-Basset M, El-Shahat D, El-Henawy I (2019) Solving 0-1 knapsack problem by binary flower pollination algorithm. Neural Comput Appl 31(9):5477–5495

    Article  Google Scholar 

  18. Cao J, Yin B, Lu X, et al. (2018) A modified artificial bee colony approach for the 0-1 knapsack problem. Appl Intell 48(6):1582–1595

    Article  Google Scholar 

  19. Luo K, Zhao Q (2019) A binary grey wolf optimizer for the multidimensional knapsack problem. Appl Soft Comput 83:105645

    Article  Google Scholar 

  20. Rizk-Allah RM, Hassanien AE, Elhoseny M, et al. (2019) A new binary salp swarm algorithm: development and application for optimization tasks. Neural Comput Appl 31(5):1641–1663

    Article  Google Scholar 

  21. Ye Z, Ma L, Chen H (2016) A hybrid rice optimization algorithm. In: 11th International Conference on Computer Science & Education, pp 169–174

  22. Liu W, Huang Y, Ye Z, et al. (2020) Renyi’s entropy based multilevel thresholding using a novel meta-heuristics algorithm. Appl Sci 10(9):3225

    Article  Google Scholar 

  23. Dorigo M, Di Caro G (1999) Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 congress on evolutionary computation-CEC99, pp 1470–1477

  24. Shunmugapriya P, Kanmani S (2017) A hybrid algorithm using ant and bee colony optimization for feature selection and classification (AC-ABC Hybrid). Swarm Evol Comput 36:27– 36

    Article  Google Scholar 

  25. Kiran MS, Ozceylan E, Gunduz M, et al. (2012) A novel hybrid approach based on particle swarm optimization and ant colony algorithm to forecast energy demand of Turkey. Energy Conver Manag 53 (1):75–83

    Article  Google Scholar 

  26. Kong M, Tian P (2005) A binary ant colony optimization for the unconstrained function optimization problem. In: International Conference on Computational and Information Science, pp 682–687

  27. Manbari Z, Tab FA, Salavati C (2019) Fast unsupervised feature selection based on the improved binary ant system and mutation strategy. Neural Comput Appl 31(9):4963–4982

    Article  Google Scholar 

  28. Zhang W, Lu T (2012) The research of genetic ant colony algorithm and its application. Procedia Eng 37:101–106

    Article  Google Scholar 

  29. Mellouk L, Aaroud A, Boulmalf M, et al. (2020) Development and performance validation of new parallel hybrid cuckoo search-genetic algorithm. Energy Syst 11(3):729–751

    Article  Google Scholar 

  30. Gong D, Han Y, Sun J (2018) A novel hybrid multi-objective artificial bee colony algorithm for blocking lot-streaming flow shop scheduling problems. Knowl-Based Syst 148:115–130

    Article  Google Scholar 

  31. Olsen AL (1994) Penalty functions and the knapsack problem. In: Proceedings of the First IEEE Conference on Evolutionary Computation, pp 554–558

  32. Chu PC, Beasley JE (1998) A genetic algorithm for the multidimensional knapsack problem. J Heurist 4(1):63–86

    Article  Google Scholar 

  33. Wan Y, Wang M, Ye Z, et al. (2016) A feature selection method based on modified binary coded ant colony optimization algorithm. Appl Soft Comput 49:248–258

    Article  Google Scholar 

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

    Article  Google Scholar 

  35. Bhattacharjee KK, Sarmah SP (2014) Shuffled frog leaping algorithm and its application to 0/1 knapsack problem. Appl Soft Comput J 19:252–263

    Article  Google Scholar 

  36. Sun J, Xu W, Fang W (2007) Quantum-behaved particle swarm optimization with binary encoding. In: International Conference on Adaptive and Natural Computing Algorithms, pp 376–385

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Authors and Affiliations

  1. School of Computer Science, Hubei University of Technology, Wuhan, China

    Zhe Shu, Zhiwei Ye, Xinlu Zong, Shiqin Liu, Chunzhi Wang & Mingwei Wang

  2. School of mechanical engineering, Hubei University of Technology, Wuhan, China

    Daode Zhang

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  1. Zhe Shu
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  2. Zhiwei Ye
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  3. Xinlu Zong
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  4. Shiqin Liu
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  5. Daode Zhang
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  6. Chunzhi Wang
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  7. Mingwei Wang
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Correspondence to Zhiwei Ye.

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Shu, Z., Ye, Z., Zong, X. et al. A modified hybrid rice optimization algorithm for solving 0-1 knapsack problem. Appl Intell 52, 5751–5769 (2022). https://doi.org/10.1007/s10489-021-02717-4

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