Skip to main content
Log in

The optimal multi-degree reduction of Ball Bézier curves using an improved squirrel search algorithm

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

Abstract

As a new nature-inspired swarm intelligence optimizer, squirrel search algorithm (SSA) has shown potential to solve several real-world problems, but for some complex problems, it still suffers from degraded performance. In this paper, a hybrid squirrel search algorithm (NOSSA) combined with optimal neighborhood update and quasi-opposition learning strategies is proposed to overcome the drawback of population update guided only by leading individuals in SSA. NOSSA adopts a stochastic optimal neighborhood update strategy to improve convergence speed and accuracy, and incorporates a Quasi-opposition learning strategy to enhance exploration. To verify its efficiency, NOSSA has been tested on 23 classic benchmark functions. Experimental results show that NOSSA has better performance on search-efficiency, convergence rate and solution accuracy compared with the representative stochastic optimizers. Furthermore, intelligent algorithms are introduced into the optimal multi-degree reduction of Ball Bézier curves and two new methods are proposed for the multi-degree reduction of center curve and radius function of Ball Bézier curve respectively. Experimental results demonstrate the effectiveness of the methods and show that NOSSA performs best among the representative stochastic optimizers in the degree reduction. The methods achieve the automatic and intelligent degree reduction of Ball Bézier curves.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  2. Robinson J, Rahmat-Samii Y (2004) Particle swarm optimization in electromagnetic. IEEE Trans Antennas Propag 52:397–407

    MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  6. Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191

    Google Scholar 

  7. Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Future Gener Comput Syst 97:849–872

    Google Scholar 

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

    Google Scholar 

  9. Jain M, Singh V, Rani A (2019) A novel nature-inspired algorithm for optimization: squirrel search algorithm. Swarm Evol Comput 44:148C175

    Google Scholar 

  10. Tanweer MR, Suresh S, Sundararajan N (2015) Self regulating particle swarm optimization algorithm. Inf Sci 294:182–202

    MathSciNet  MATH  Google Scholar 

  11. Lenin K (2020) Real power loss reduction by Duponchelia fovealis optimization and enriched squirrel search optimization algorithms. Soft Comput 24(23):17863–17873

    MATH  Google Scholar 

  12. Deb D, Roy S (2020) Brain tumor detection based on hybrid deep neural network in MRI by adaptive squirrel search optimization. Ultimed Tools Appl 80:2621–2645

    Google Scholar 

  13. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Google Scholar 

  14. Guo W, Wang Y, Dai F, Xu P (2020) Improved sine cosine algorithm combined with optimal neighborhood and quadratic interpolation strategy. Eng Appl Artif Intell 94:103779

    Google Scholar 

  15. Mehrabian AR, Lucas C (2006) A novel numerical optimization algorithm inspired from weed colonization. Ecol Inf 1:355–366

    Google Scholar 

  16. Hu H, Zhang L, Bai Y, Wang P, Tan X (2019) A hybrid algorithm based on squirrel search algorithm and invasive weed optimization for optimization. IEEE Access 7:105652–105668

    Google Scholar 

  17. Gupta S, Deep K (2020) A memory-based grey wolf optimizer for global optimization tasks. Appl Soft Comput 93:106367

    Google Scholar 

  18. Kohli M, Arora S (2018) Chaotic grey wolf optimization algorithm for constrained optimization problems. J Comput Des Eng 5(4):458–472

    Google Scholar 

  19. Saxena MA, Kumar R, Das S (2019) \(\beta \)-chaotic map enabled grey wolf optimizer. Appl Soft Comput 75:84–105

    Google Scholar 

  20. Tang Y, Wang Z, Fang J (2011) Feedback learning particle swarm optimization. Appl Soft Comput 11:4713–4725

    Google Scholar 

  21. Lin CJ, Chern MS, Chih M (2016) A binary particle swarm optimization based on the surrogate information with proportional acceleration coefficients for the 0–1 multidimensional knapsack problem. J Ind Prod Eng 33:77–102

    Google Scholar 

  22. Ardizzon G, Cavazzini G, Pavesi G (2015) Adaptive acceleration coefficients for a new search diversification strategy in particle swarm optimization algorithms. Inf Sci 299:337–378

    Google Scholar 

  23. Gülcü Ş, Kodaz H (2015) A novel parallel multi-swarm algorithm based on comprehensive learning particle swarm optimization. Eng Appl Artif Intell 45:33–45

    Google Scholar 

  24. Wang F, Zhang H, Li K et al (2018) A hybrid particle swarm optimization algorithm using adaptive learning strategy. Inf Sci 436:162–177

    MathSciNet  Google Scholar 

  25. Ouyang HB, Gao LQ, Li S et al (2017) Improved global-best-guided particle swarm optimization with learning operation for global optimization problems. Appl Soft Comput 52:987–1008

    Google Scholar 

  26. Chen K, Zhou F, Yin L et al (2017) A hybrid particle swarm optimizer with sine cosine acceleration coefficients. Inf Sci 422:218–241

    MathSciNet  Google Scholar 

  27. Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence, In: International Conference on computational intelligence for modelling, control and automation International Conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC’06). IEEE 01:695–701

  28. Guha D, Roy PK, Banerjee S (2016) Load frequency control of large scale power system using quasi-oppositional grey wolf optimization algorithm. Eng Sci Technol 19(4):1693–1713

    Google Scholar 

  29. Basu M (2016) Quasi-oppositional group search optimization for hydrothermal power system. Int J Electr Power Energy Syst 81:324–335

    Google Scholar 

  30. Nandi M, Shiva CK, Mukherjee V (2017) TCSC based automatic generation control of deregulated power system using quasi-oppositional harmony search algorithm. Eng Sci Technol 20(4):1380–1395

    Google Scholar 

  31. Ammad M, Misro M, Abbas M et al (2021) Generalized developable cubic trigonometric Bézier surfaces. Mathematics 9(3):283. https://doi.org/10.3390/math9030283

    Article  Google Scholar 

  32. Majeed A, Abbas M, Qayyum F et al (2020) Geometric modeling using new cubic trigonometric B-Spline functions with shape parameter. Mathematics 8(12):2102. https://doi.org/10.3390/math8122102

    Article  Google Scholar 

  33. Bashir U, Abbas M, Ali J (2013) The \(G^{2}\) and \(C^{2}\) rational quadratic trigonometric Bézier curve with two shape parameters with applications. Appl Math Comput 219(20):10183–10197

    MathSciNet  MATH  Google Scholar 

  34. Usman M, Abbas M, Miura K (2020) Some engineering applications of new trigonometric cubic Bézier-like curves to free-form complex curve modeling. J Adv Mech Des Syst 14(4):JAMDSM0048

    Google Scholar 

  35. Bibi S, Abbas M, Miura K et al (2020) Geometric modeling of novel generalized hybrid trigonometric Bézier-like curve with shape parameters and its applications. Mathematics 8(6):967. https://doi.org/10.3390/math8060967

    Article  Google Scholar 

  36. Majeed A, Abbas M, Miura K et al (2020) Surface modeling from 2D contours with an application to craniofacial fracture construction. Mathematics 8(8):1246. https://doi.org/10.3390/math8081246

    Article  Google Scholar 

  37. Maqsood S, Abbas M, Miura K et al (2020) Geometric modeling and applications of generalized blended trigonometric Bézier curves with shape parameters. Adv Differ Equ 550:1–8

    MATH  Google Scholar 

  38. Leng C, Wu Z, Zhou M (2011) Reconstruction of tubular object with ball b-spline curve. In: Proceedings of computer graphics international

  39. Wang X, Wu Z, Shen J et al (2016) Repairing the cerebral vascular through blending Ball B-Spline curves with \(G^{2}\) continuity. Neurocomputing 173:768–777

    Google Scholar 

  40. Xu X, Leng C, Wu Z (2011) Rapid 3d human modeling and animation based on sketch and motion database, In. Workshop on Digital Media and Digital Content Management (DMDCM) 2011, pp 121–124

  41. Wu Z, Zhou M, Wang X et al (2007) An interactive system of modeling 3D trees with ball b-spline curves, In: 2007 10th IEEE International Conference on computer-aided design and computer graphics, 1:259–265

  42. Zhu T, Tian F, Zhou Y et al (2008) Plant modeling based on 3D reconstruction and its application in digital museum. Int J Virt Real 7(1):81–88

    Google Scholar 

  43. Wu Z, Seah H, Zhou M (2007) Skeleton based parametric solid models: Ball B-Spline curves, In: 2007 10th IEEE International Conference on computer-aided design and computer graphics, pp 421–424

  44. Fu Q, Wu Z, Zhou M, Zheng J, Wang X, Wang X et al (2018) An algorithm for finding intersection between ball B-spline curves. J Comput Appl Math 327:260–273

    MathSciNet  MATH  Google Scholar 

  45. Liu X, Wang X, Wu Z, Zhang D, Liu X (2020) Extending Ball B-spline by B-spline. Comput Aided Geom Des 82:101926

    MathSciNet  MATH  Google Scholar 

  46. Chen F, Lou W (2000) Degree reduction of interval Bézier curves. Comput Aided Des 32(6):571–582

    MATH  Google Scholar 

  47. Chen F, Yang W (2004) Degree reduction of disk Bézier curves. Comput Aided Geom Des 21(3):263–280

    MATH  Google Scholar 

  48. Shi M (2015) Degree reduction of classic disk rational Bézier curves in L2 norm, In: 2016 14th International Conference on computer-aided design and computer graphics, CAD/Graphics. 7450417, pp 202–203

  49. Yang X-S (2010) Firefly algorithm, Lévy flights and global optimization. Springer, London, pp 209–218

    Google Scholar 

  50. Jensi R, Jiji GW (2016) An enhanced particle swarm optimization with Lévy flight for global optimization. Appl Soft Comput 43:248–261

    Google Scholar 

  51. Wu J, Zhang X (2015) Integro quadratic spline interpolation. Appl Math Model 39:2973–2980

    MathSciNet  MATH  Google Scholar 

  52. Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102

    Google Scholar 

  53. Digalakis J, Margaritis K (2001) On benchmarking functions for genetic algorithm. Int J Comput Math 77(4):481–506

    MathSciNet  MATH  Google Scholar 

  54. Mirjalili S, Lewis A (2013) S-shaped versus V-shaped transfer functions for binary particle swarm optimization. Swarm Evol Comput 9:1–14

    Google Scholar 

  55. Mirjalili S, Mirjalili SM, Yang XS (2014) Binary bat algorithm. Neural Comput Appl 25(3–4):663–681

    Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No. 51875454).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Hongchan Zheng or Gang Hu.

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

Cao, H., Zheng, H. & Hu, G. The optimal multi-degree reduction of Ball Bézier curves using an improved squirrel search algorithm. Engineering with Computers 39, 1143–1166 (2023). https://doi.org/10.1007/s00366-021-01499-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-021-01499-0

Keywords

Mathematics Subject Classification

Navigation