Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

A shuffled frog leaping algorithm with contraction factor and its application in mechanical optimum design

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

The shuffled frog leaping algorithm is easily trapped into local optimum and has the low optimization accuracy when it is used to optimize the complex functions problems. To overcome the above shortcomings, a shuffled frog leaping algorithm with contraction factor was proposed. By introducing acceleration factors c1 and c2, the ability of worst individual to learn from best individual within the submemeplexes or global best individual of the entire population was improved and the convergence rate of algorithm was accelerated. Under inserting the contraction factor χ, the convergence of algorithm was ensured. After performing local optimization of the self-learning operator on the worst individual, and taking full advantage of the useful information in the worst individuals, the self-learning ability of the individual and the optimization accuracy of the algorithm were improved. Simulation results illustrated that the enhanced algorithm performed better optimization performance than basic SFLA and other improved SFLAs. Finally, the proposed algorithm was used to optimize five problems of the mechanical design, and its validity and practicability were verified.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  1. Hassan BA, Rashid TA (2020) Operational framework for recent advances in backtracking search optimization algorithm: a systematic review and performance evaluation. Appl Math Comput 370:124919

    MathSciNet  MATH  Google Scholar 

  2. Zhao FQ, Zhao LX, Wang L et al (2020) A collaborative LSHADE algorithm with comprehensive learning mechanism. Appl Soft Comput 96:106609

    Article  Google Scholar 

  3. Zhao FQ, Qin S, Zhang Y et al (2018) A two-stage differential biogeography-based optimization algorithm and its performance analysis. Expert Syst Appl 115:329

    Article  Google Scholar 

  4. Eusuff MM, Lansey KE (2003) Optimization of water distribution network design using the shuffled frog leaping algorithm. J Water Res Plan Man 129(3):210–225

    Article  Google Scholar 

  5. Ahandani MA, Kharrati H (2018) Chaotic shuffled frog leaping algorithms for parameter identification of fractional-order chaotic systems. J Exp Theor Artif In 30(5):561–581

    Google Scholar 

  6. Dash R (2017) An improved shuffled frog leaping algorithm based evolutionary framework for currency exchange rate prediction. Physica A 486:782–796

    Article  Google Scholar 

  7. Li RB, Jiang ZQ, Li AQ et al (2018) An improved shuffled frog leaping algorithm and its application in the optimization of cascade reservoir operation. Hydrolog Sci J 63(15–16):2020–2034

    Article  Google Scholar 

  8. Dash R, Dash R, Rautray R (2019) An evolutionary framework based microarray gene selection and classification approach using binary shuffled frog leaping algorithm. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2019.04.002

    Article  Google Scholar 

  9. Karpagam M, Geetha K, Rajan C (2020) A modified shuffled frog leaping algorithm for scientific workflow scheduling using clustering techniques. Soft Comput 24(1):637–646

    Article  Google Scholar 

  10. Hu BB (2020) Shuffled frog leaping algorithm based on quantum rotation angle. Matec Web Conf 309:03012

    Article  Google Scholar 

  11. Guo YX, Tian X, Fang GH et al (2020) Many-objective optimization with improved shuffled frog leaping algorithm for inter-basin water transfers. Adv Water Res 138:103531

    Article  Google Scholar 

  12. Wang LG (2020) A Shuffled Frog Leaping Algorithm based on the Improved Simplex Method. In: 2020 13th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI), 2020, pp 1020–1025. https://doi.org/10.1109/CISP-BMEI51763.2020.9263578

  13. Clerc M (1999) The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. in Proc. 1999 ICEC, Washington, DC, pp 1951–1957

  14. Wang LG, Hong Y, Zhao FQ et al (2008) Improved artificial fish swarm algorithm. Comput Eng 34(19):192–194

    Google Scholar 

  15. Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82

    Article  Google Scholar 

  16. Abderazek H, Yildiz AR, Sait SM (2019) Mechanical engineering design optimisation using novel adaptive differential evolution algorithm. Int J Vehicle Design 80(2/3/4):285–329

  17. Guo HX, Che XY, Xiao WY (2003) Chaos-genetic optimal algorithm and application in mechanical optimal design. Mach Des 20(10):23–25

    Google Scholar 

  18. He Q, Wang L (2007) A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization. Appl Math Comput 186(2):1407–1422

    MathSciNet  MATH  Google Scholar 

  19. Yildiz AR, Abderazek H, Mirjalili S (2019) A comparative study of recent non-traditional approaches for mechanical design optimization. Arch Comput Method Eng 27(4):1031

    Article  Google Scholar 

  20. Ray T, Saini P (2001) Engineering design optimization using a swarm with an intelligent information sharing among individuals. Eng Optimiz 33(6):735–748

    Article  Google Scholar 

  21. Tsai JF (2005) Global optimization of nonlinear fractional programming problems in engineering design. Eng Optimiz 37(4):399–409

    Article  MathSciNet  Google Scholar 

  22. Gandomi AH, Yang XS, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35

    Article  Google Scholar 

  23. Mirjalilis S (2016) SCA: A sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133

    Article  Google Scholar 

  24. Shubham G, Kusum D (2019) A hybrid self-adaptive sine cosine algorithm with opposition based learning. Expert Syst Appl 119:210

    Article  Google Scholar 

  25. Shen Y, Pu YF (2009) Particle swarm optimization for box girder of crane based on chaos series. Hoisting Convey Mach 11:55–58

    Google Scholar 

  26. Zhao ZJ, Huang HZ, Chen X (2000) A genetic neural network algorithm in optimum design. J Southwest Jiaotong Univ 01:67–70

    MATH  Google Scholar 

  27. Sun GZ (1992) Optimization design and its application. Communication Press, Beijing

    Google Scholar 

  28. Belegundu AD (1985) A study of mathematical programming approaches for structural optimization. Int J Numer Meth Eng 21(9):1601–1623

    Article  MathSciNet  MATH  Google Scholar 

  29. Arora JS (1989) Introduction to optimum design. McGraw-Hill, New York

    Google Scholar 

  30. Coello CAC (2000) Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind 41(2):113–127

    Article  Google Scholar 

  31. Coello CAC, Montes EM (2002) Constraint-handling in genetic algorithms through the use of dominance based tournament selection. Adv Eng Inform 16(3):193–203

    Article  Google Scholar 

  32. He Q, Wang L (2007) An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intel 20(1):89–99

    Article  Google Scholar 

  33. Sandgren E (1990) Nonlinear integer and discrete programming in mechanical engineering systems. Mach Des 112(1):223–229

    Google Scholar 

  34. Kannan BK, Kramer SN (1994) An augmented Lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design. Mach Des 116(2):405–411

    Google Scholar 

  35. Deb K (1997) GeneAS: a robust optimal design technique for mechanical component design. In: Dasgupta D, Michalewicz Z (eds) Evolutionary algorithms in engineering applications. Springer-Verlag, Berlin, pp 497–514

    Chapter  Google Scholar 

  36. Lu QB (2008) Differential evolution algorithm and its application in mechanical optimum design. Dissertation, Taiyuan University of Science and Technology

  37. Hammoudi A, Djeddou F, Atanasovska I (2017) Adaptive mixed differential evolution algorithm for bi-objective tooth profile spur gear optimization. Int J Adv Manuf Technol 90(5–8):2063

    Google Scholar 

  38. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471

    Article  MathSciNet  MATH  Google Scholar 

  39. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks pp 1942–1948

  40. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249

    Article  Google Scholar 

  41. Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization-artificial ants as a computational intelligence technique. IEEE Comput Intell Mag 1:28–39

    Article  Google Scholar 

  42. Sadollah A, Bahreininejad A, Eskandar H et al (2013) Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems. Appl Soft Comput 13(5):2592–2612

    Article  Google Scholar 

  43. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  44. Eskandar H, Sadollah A, Bahreininejad A et al (2012) Water cycle algorithm–a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct 110:151–166

    Article  Google Scholar 

  45. Sadollah A, Bahreininejad A, Eskandar H et al (2013) Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems. Appl Soft Comput J 13(5):2592–2612

    Article  Google Scholar 

  46. Mirjalili S, Gandomi AH, Mirjalili SZ et al (2017) Salp Swarm Algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191

    Article  Google Scholar 

  47. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

Download references

Funding

This study was supported by the Key Research and Development Program of Gansu Province (Grant no. 21YF5GA088). National Natural Science Foundation of China (Grant no. 61751313) and Education Information Construction Special Task Project in Gansu (Grant no. 2011- 02).

Author information

Authors and Affiliations

  1. College of Information Science and Technology, Gansu Agricultural University, No. 1 Yingmen Village, Lanzhou, 730070, Gansu, China

    Lianguo Wang & Xiaojuan Liu

Authors
  1. Lianguo Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaojuan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lianguo Wang.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, L., Liu, X. A shuffled frog leaping algorithm with contraction factor and its application in mechanical optimum design. Engineering with Computers 38 (Suppl 4), 3655–3673 (2022). https://doi.org/10.1007/s00366-021-01510-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-021-01510-8

Keywords

Search

Navigation