Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

The structural weight design method based on the modified grasshopper optimization algorithm

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Structural weight design is essential and difficult in engineering structure optimization. The design is affected by many factors and belongs to the NP problem. Swarm intelligent algorithm provides a valid way to solve the NP problem. Grasshopper optimization algorithm (GOA) is a nature-inspired algorithm that mimics the swarming behaviors of grasshopper insects, but the original GOA has two main problems: the convergence rate is slow and the convergence accuracy is poor. We propose a novel grasshopper optimization algorithm (CV-GOA) consisting of chaos strategy and velocity perturbation mechanism to improve the performance of standard GOA. In CV-GOA, the initial artificial swarm is constructed by Logistic map to increase the diversity of the population and improve the feasibility of finding the global optimal solution; then a set of the velocity vector is introduced and the velocity perturbation mechanism is used to update the velocity of grasshoppers and disturbs the position of grasshoppers, it can improve the searching speed of the algorithm and help the algorithm jump out of the local optimal trap, and improve the optimization accuracy of the algorithm. Experiments are conducted on fifteen benchmark functions to test the accuracy and convergence rate of CV-GOA. Experiments show the proposed CV-GOA achieves higher precision and better convergence rate than other variants. In addition, three structural weight design problems are optimized by CV-GOA, they are cantilever beam design problem, pressure vessel design problem and speed reducer design problem. The results indicate structural weight is designed with superiority. It also proves the effectiveness and value 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

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
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Notes

  1. https://www.mindspore.cn/

References

  1. Aljarah I, Al-Zoubi AM, Faris H, Hassonah MA, Mirjalili S, Saadeh H (2018) Simultaneous feature selection and support vector machine optimization using the grasshopper optimization algorithm. Cognit Comput 10(3):478–495

    Article  Google Scholar 

  2. Alkasassbeh M, Omar Z, Mebarek-Oudina F, Raza J, Chamkha A (2019) Heat transfer study of convective fin with temperature-dependent internal heat generation by hybrid block method. Heat Transf Res 48(4):1225–1244

    Article  Google Scholar 

  3. Arora S, Anand P (2018) Chaotic grasshopper optimization algorithm for global optimization. Neural Comput Appl 31(8):4385–4405

  4. Brest J, Greiner S, Boskovic B, Mernik M, Zumer V (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evol Comput 10(6):646–657

    Article  Google Scholar 

  5. Chen W, Zhang J, Lin Y, Chen N, Zhan Z, Chung HS, Li Y, Shi Y (2013) Particle swarm optimization with an aging leader and challengers. IEEE Trans Evol Comput 17(2):241–258

    Article  Google Scholar 

  6. Chen J, Liu Y, Zhu Z, Zhu W (2017) An adaptive hybrid memetic algorithm for thermal-aware non-slicing vlsi floorplanning. Integr VLSI J 58:245–252

    Article  Google Scholar 

  7. Chen Y, He F, Li H, Zhang D, Wu Y (2020) A full migration bbo algorithm with enhanced population quality bounds for multimodal biomedical image registration. Appl Soft Comput 93:106335

    Article  Google Scholar 

  8. Dong C, Ye Y, Liu XM, Yang Y, Guo WZ (2019) The sensitivity design of piezoresistive acceleration sensor in industrial iot. IEEE Access 7:16952–16963

    Article  Google Scholar 

  9. Dorigo M, Caro GD (1999) Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 congress on evolutionary computation-CEC99, vol 2, pp 1470–1477

  10. Ewees A, Elaziz M, EH H (2018) Improved grasshopper optimization algorithm using opposition-based learning. Expert Syst Appl 112:156–172

    Article  Google Scholar 

  11. Farhan M, Omar Z, Mebarek-Oudina F, Raza J, Shah Z, Choudhari R, Makinde O (2020) Implementation of the one-step one-hybrid block method on the nonlinear equation of a circular sector oscillator. Comput Math Model 31(1):116–132

    Article  MathSciNet  MATH  Google Scholar 

  12. 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 

  13. Gholizadeh S, Ebadijalal M (2017) Seismic design optimization of steel structures by a sequential ecbo algorithm. Int J Optim Civil Eng 7(2):157–171

    Google Scholar 

  14. Gholizadeh S, Milany A (2018) An improved fireworks algorithm for discrete sizing optimization of steel skeletal structures. Eng Optim 50(11):1829–1849

    Article  MathSciNet  Google Scholar 

  15. Guo L, Shen H (2017) Efficient approximation algorithms for the bounded flexible scheduling problem in clouds. IEEE Trans Parallel Distrib Syst 28:3511–3520

    Article  Google Scholar 

  16. Guo W, Liu G, Chen G, Peng S (2014) A hybrid multi-objective pso algorithm with local search strategy for vlsi partitioning. Front Comput Sci 8(2):203–216

    Article  MathSciNet  Google Scholar 

  17. Guo W, Li J, Chen G, Niu Y, Chen C (2015) A pso-optimized real-time fault-tolerant task allocation algorithm in wireless sensor networks. IEEE Trans Parallel Distrib Syst 26(12):3236–3249

    Article  Google Scholar 

  18. Hamad A, Houssein EH, Hassanien AE, Fahmy AA (2018) Hybrid grasshopper optimization algorithm and support vector machines for automatic seizure detection in eeg signals. In: Advances in intelligent systems and computing, vol 723. Springer, pp 82–91

  19. Heidari AA, Faris H, Aljarah I, Mirjalili S (2018) An efficient hybrid multilayer perceptron neural network with grasshopper optimization. Soft Comput 23(17):7941–7958

  20. Huang X, Liu G, Guo W, Niu Y, Chen G (2015) Obstacle-avoiding algorithm in x-architecture based on discrete particle swarm optimization for vlsi design. ACM Trans Des Autom Electron Syst 20(2):24:1–24:28

    Article  Google Scholar 

  21. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Tech. rep. Citeseer

  22. Kaur G, Arora S (2018) Chaotic whale optimization algorithm. J Comput Des Eng 5(3):275–284

    Google Scholar 

  23. Kaveh A, Bakhshpoori T (2016) A new metaheuristic for continuous structural optimization: water evaporation optimization. Struct Multidiscip Optim 54(1):23–43

    Article  Google Scholar 

  24. Kaveh A, Dadras A (2018) Structural damage identification using an enhanced thermal exchange optimization algorithm. Eng Optim 50(3):430–451

    Article  Google Scholar 

  25. Kaveh A, Farhoudi N (2016) Dolphin monitoring for enhancing metaheuristic algorithms: layout optimization of braced frames. Comput Struct 165:1–9

    Article  Google Scholar 

  26. Kaveh A, Ghazaan MI (2017) Enhanced whale optimization algorithm for sizing optimization of skeletal structures. Mech Based Des Struct Mach 45(3):345–362

    Article  Google Scholar 

  27. Kaveh A, Ghazaan MI (2018) A new hybrid meta-heuristic algorithm for optimal design of large-scale dome structures. Eng Optim 50(2):235–252

    Article  MathSciNet  Google Scholar 

  28. Kaveh A, Khayatazad M (2012) A new meta-heuristic method: ray optimization. Comput Struc 112–113(4):283–294

    Article  Google Scholar 

  29. Ke C, Zhou F, Liu A (2018) Chaotic dynamic weight particle swarm optimization for numerical function optimization. Knowl-Based Syst 139:23–40

    Article  Google Scholar 

  30. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95—international conference on neural networks, vol 4, pp 1942–1948

  31. Li H, He F, Chen Y, Pan Y (2021) Mlfs-ccde: multi-objective large-scale feature selection by cooperative coevolutionary differential evolution. Memet Comput 13(1):1–18

    Article  Google Scholar 

  32. Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295

    Article  Google Scholar 

  33. Liang Y, He F, Zeng X (2020) 3d mesh simplification with feature preservation based on whale optimization algorithm and differential evolution. Integr Comput-Aided Eng (Preprint) 1–19

  34. Lin SJ, Dong C, Wang ZQ, Guo WZ, Chen ZY, Ye Y (2018) A chaotic artificial bee colony algorithm based on levy search. IEICE Trans Fundam Electron Commun Comput Sci E101A(12):2472–2476

    Article  Google Scholar 

  35. Luo J, He F, Yong J (2020) An efficient and robust bat algorithm with fusion of opposition-based learning and whale optimization algorithm. Intell Data Anal 24(3):581–606

    Article  Google Scholar 

  36. Mafarja M, Aljarah I, Heidari AA, Hammouri AI, Mirjalili S (2017) Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems. Knowl-Based Syst 145:25–45

    Article  Google Scholar 

  37. Mirjalili S (2015a) The ant lion optimizer. Adv Eng Softw 83(C):80–98

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  40. Mirjalili S (2016b) Sca: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133

    Article  Google Scholar 

  41. Mirjalili S (2016c) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  42. Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513

    Article  Google Scholar 

  43. Mirjalili S Z, Mirjalili S, Saremi S, Faris H, Aljarah I (2018) Grasshopper optimization algorithm for multi-objective optimization problems. Appl Intell 48(4):805–820

    Article  Google Scholar 

  44. Navarro-Urrios D, Capuj NE, Colombano MF, García PD, Sledzinska M, Alzina F, Griol A, Martínez A, Sotomayor-Torres CM (2017) Nonlinear dynamics and chaos in an optomechanical beam. Nat Commun 8:14965

    Article  Google Scholar 

  45. Niu Y, Lin W, Ke X (2018) Cf-based optimisation for saliency detection. IET Comput Vis 12(4):365–376

    Article  Google Scholar 

  46. Panagant N, Bureerat S (2018) Truss topology, shape and sizing optimization by fully stressed design based on hybrid grey wolf optimization and adaptive differential evolution. Eng Optim 50(10):1645–1661

    Article  MathSciNet  Google Scholar 

  47. Potnuru D, Tummala AS (2019) Implementation of grasshopper optimization algorithm for controlling a bldc motor drive. In: Advances in intelligent systems and computing, vol 758. Springer, pp 369–376

  48. Qin A K, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417

    Article  Google Scholar 

  49. Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) Gsa: a gravitational search algorithm. Inf Sci 179(13):2232–2248

    Article  MATH  Google Scholar 

  50. Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47

    Article  Google Scholar 

  51. Tejani GG, Savsani VJ, Patel VK (2016) Modified sub-population teaching-learning-based optimization for design of truss structures with natural frequency constraints. J Struct Mech 44(4):495–513

    Article  Google Scholar 

  52. Tumuluru P, Ravi B (2018) Application and development of enhanced chaotic grasshopper optimization algorithm. Model Simul Eng 2018(2):1–14

  53. Xia Y, Wang J (2016) A bi-projection neural network for solving constrained quadratic optimization problems. IEEE Trans Neural Netw Learn Syst 27(2):214–224

    Article  MathSciNet  Google Scholar 

  54. Xin Z, Qiang M, Zhang H, Lei W (2018) A parameter-adaptive vmd method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery. Mech Syst Signal Process 108:58–72

    Article  Google Scholar 

  55. Yu C, Chen M, Cheng K, Zhao X, Ma C, Kuang F, Chen H (2021) Sgoa: annealing-behaved grasshopper optimizer for global tasks. Eng Comput

  56. Yue X, Zhang H (2020) Grasshopper optimization algorithm with principal component analysis for global optimization. J Supercomput 76 (7):5609–5635

    Article  Google Scholar 

  57. Yue S, Zhang H (2021) A hybrid grasshopper optimization algorithm with bat algorithm for global optimization. Multimed Tools Appl 80(3):3863–3884

    Article  Google Scholar 

  58. Zhou H, Ding Z, Peng H, Tang Z, Liang G, Chen H, Ma C, Wang M (2020) An improved grasshopper optimizer for global tasks. Complexity 2020:1–23

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant 62176194 and Grant 62101393, in part by the Major Project of IoV under Grant 2020AAA001, in part by the Sanya Science and Education Innovation Park of the Wuhan University of Technology under Grant 2021KF0031 and Grant HSPHDSRF-2022-03-017, in part by the National Natural Science Foundation of Chongqing under Grant cstc2021jcyj-msxmX1148, and in part by the Open Project of the Wuhan University of Technology Chongqing Research Institute under Grant ZL2021-6, and in part by the Natural Science Foundation of Fujian Province under Grant 2020J01500. We thank MindSpore for the partial support of this work, which is a new computing framework https://www.mindspore.cn/.

Author information

Authors and Affiliations

  1. School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, 430070, China

    Yin Ye & Shengwu Xiong

  2. Sanya Science and Education Innovation Park of Wuhan University of Technology, Sanya, 572000, China

    Yin Ye & Shengwu Xiong

  3. College of Computer Science and Big Data, Fuzhou University, Fuzhou, 350116, China

    Chen Dong

  4. Department of Electrical Engineering, University of South Florida, Tampa, FL, 33620, USA

    Zhenyi Chen

Authors
  1. Yin Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shengwu Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chen Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhenyi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Shengwu Xiong or Chen Dong.

Ethics declarations

Ethics approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

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

Appendix: The nomenclature section

Appendix: The nomenclature section

Table 8 Description of the variables and abbreviations
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ye, Y., Xiong, S., Dong, C. et al. The structural weight design method based on the modified grasshopper optimization algorithm. Multimed Tools Appl 81, 29977–30005 (2022). https://doi.org/10.1007/s11042-022-12562-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-12562-3

Keywords

Search

Navigation