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

Sand Cat swarm optimization: a nature-inspired algorithm to solve global optimization problems

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

Abstract

This study proposes a new metaheuristic algorithm called sand cat swarm optimization (SCSO) which mimics the sand cat behavior that tries to survive in nature. These cats are able to detect low frequencies below 2 kHz and also have an incredible ability to dig for prey. The proposed algorithm, inspired by these two features, consists of two main phases (search and attack). This algorithm controls the transitions in the exploration and exploitation phases in a balanced manner and performed well in finding good solutions with fewer parameters and operations. It is carried out by finding the direction and speed of the appropriate movements with the defined adaptive strategy. The SCSO algorithm is tested with 20 well-known along with modern 10 complex test functions of CEC2019 benchmark functions and the obtained results are also compared with famous metaheuristic algorithms. According to the results, the algorithm that found the best solution in 63.3% of the test functions is SCSO. Moreover, the SCSO algorithm is applied to seven challenging engineering design problems such as welded beam design, tension/compression spring design, pressure vessel design, piston lever, speed reducer design, three-bar truss design, and cantilever beam design. The obtained results show that the SCSO performs successfully on convergence rate and in locating all or most of the local/global optima and outperforms other compared methods.

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
Fig. 16

Similar content being viewed by others

References

  1. Jamil M, Xin-She Y (2013) A literature survey of benchmark functions for global optimization problems. http://arxiv.org/abs/1308.4008

  2. Talbi EG (2009) Metaheuristics: from design to implementation, vol 74. Wiley, New York, pp 5–39

  3. Tang C, Zhou Y, Tang Z et al (2021) Teaching-learning-based pathfinder algorithm for function and engineering optimization problems. Appl Intell 51:5040–5066

    Google Scholar 

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

    Google Scholar 

  5. Kiani F, Seyyedabbasi A, Nematzadeh S (2021) Improving the performance of hierarchical wireless sensor networks using the metaheuristic algorithms: efficient cluster head selection. Sens Rev 1–14

  6. Kaveh A (2017) Applications of metaheuristic optimization algorithms in civil engineering. Springer International Publishing, Basel. https://doi.org/10.1007/978-3-319-48012-1

    Book  MATH  Google Scholar 

  7. Kiani F, Seyyedabbasi A, Mahouti P (2021) Optimal characterization of a microwave transistor using grey wolf algorithms. Analog Integr Circ Sig Process 109:599–609

    Google Scholar 

  8. Can U, Alatas B (2015) Physics based metaheuristic algorithms for global optimization. Am J Inf Sci Comput Eng 1(3):94–106

    Google Scholar 

  9. Back T (1996) Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press, Oxford

    MATH  Google Scholar 

  10. Holland JH (1992) Genetic algorithms. Sci Am 267(1):66–73

    Google Scholar 

  11. Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    MathSciNet  MATH  Google Scholar 

  12. Cai X, Zhao H, Shang Sh, Zhou Y et al (2021) An improved quantum-inspired cooperative co-evolution algorithm with muli-strategy and its application. Expert Syst Appl 121:1–13

    Google Scholar 

  13. Glover F (1990) Tabu search: a tutorial. Inf J Appl Anal 20(4):75–94

    Google Scholar 

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

    Google Scholar 

  15. Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713

    Google Scholar 

  16. Hayyolalam V, Kazem AAP (2020) Black widow optimization algorithm: a novel metaheuristic approach for solving engineering optimization problems. Eng Appl Artif Intell 87(103249):1–28

    Google Scholar 

  17. Webster B, Bernhard PJ (2003) A local search optimization algorithm based on natural principles of gravitation. Florida Institute of Technology, Technical Reports, pp 1–19

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

    MATH  Google Scholar 

  19. Erol OK, Eksin I (2006) A new optimization method: big bang–big crunch. Adv Eng Softw 37(2):106–111

    Google Scholar 

  20. Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inf Sci 222:175–184

    MathSciNet  Google Scholar 

  21. Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system search. Acta Mech 213(3):267–289

    MATH  Google Scholar 

  22. Moghaddam FF, Moghaddam RF, Cheriet M (2012) Curved space optimization: a random search based on general relativity theory. http://arxiv.org/abs/1208.2214

  23. Formato RA (2007) Central force optimization: a new metaheuristic with applications in applied electromagnetics. Prog Electromag Res 77:425–491

    Google Scholar 

  24. Shah-Hosseini H (2011) Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation. Int J Comput Sci Eng 6(1–2):132–140

    Google Scholar 

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

  26. Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28–39

    Google Scholar 

  27. Okdem S, Karaboga D (2009) Routing in wireless sensor networks using an ant colony optimization (ACO) router chip. Sensors 9(2):909–921

    Google Scholar 

  28. Seyyedabbasi A, Kiani F (2020) MAP-ACO: an efficient protocol for multi-agent pathfinding in real-time WSN and decentralized IoT systems. Microprocess Microsyst 79(103325):1–9

    Google Scholar 

  29. Karaboga D, Basturk B (2007) Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. International fuzzy systems association world congress. Springer, Berlin, pp 789–798

    Google Scholar 

  30. Yang XS (2010) A new metaheuristic bat-inspired algorithm. Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin, pp 65–74

    Google Scholar 

  31. Yang XS (2009) Firefly algorithms for multimodal optimization. International symposium on stochastic algorithms. Springer, Berlin, pp 169–178

    Google Scholar 

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

    Google Scholar 

  33. Seyyedabbasi A, Kiani F (2021) I-GWO and Ex-GWO: improved algorithms of the grey wolf optimizer to solve global optimization problems. Eng Comput 37:509–532

    Google Scholar 

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

    Google Scholar 

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

    MathSciNet  Google Scholar 

  36. Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: 2009 world congress on nature & biologically inspired computing (NaBIC). IEEE, pp 210–214

  37. Arora S, Singh S (2019) Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput 23(3):715–734

    Google Scholar 

  38. Bayraktar Z, Komurcu M, Werner DH (2010) Wind driven optimization (WDO): a novel nature-inspired optimization algorithm and its application to electromagnetics. In: IEEE Antennas and propagation society Internation symposium (APSURSI), pp 1–4

  39. Chu SC, Tsai PW, Pan JS (2006) Cat swarm optimization. In: Pacific Rim international conference on artificial intelligence. Springer, Berlin, pp 854–858

  40. Pan WT (2012) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl Based Syst 26:69–74

    Google Scholar 

  41. Yang XS (2012) Flower pollination algorithm for global optimization. In: Unconventional computation and natural computation, lecture notes in computer science, vol 7445, pp 240–249

  42. 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 

  43. Tang C, Zhou Y, Luo Q et al (2021) An enhanced pathfinder algorithm for engineering optimization problems. Eng Comput. https://doi.org/10.1007/s00366-021-01286-x

    Article  Google Scholar 

  44. 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 

  45. Zhong L, Zhou Y, Luo Q, Zhong K (2021) Wind driven dragonfly algorithm for global optimization. Concurr Comput Pract Exp 33(6):e6054

    Google Scholar 

  46. Wang Z, Luo Q, Zhou Y (2021) Hybrid metaheuristic algorithm using butterfly and flower pollination base on mutualism mechanism for global optimization problems. Eng Comput 37:3665–3698

    Google Scholar 

  47. Cole FR, Wilson DE (2015) Felis margarita (Carnivora: Felidae). Mamm Species 47(924):63–77

    Google Scholar 

  48. Huang G, Rosowski J, Ravicz M, Peake W (2002) Mammalian ear specializations in arid habitats: structural and functional evidence from sand cat (Felis margarita). J Comp Physiol A 188(9):663–681

    Google Scholar 

  49. Abbadi M (1989) Radiotelemetric observations on sand cats (Felis margarita) in the Arava Valley. Isr J Zool 36:155–156

    Google Scholar 

  50. Liang JJ, Qu BY, Suganthan PN (2013) Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization, vol 635. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore, pp 1–32

  51. Liang JJ, Qu BY, Suganthan PN, Chen Q (2014) Problem definitions and evaluation criteria for the CEC 2015 competition on learning-based real-parameter single objective optimization, vol 29. Technical Report201411A. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore, pp 625–640

  52. Price KV, Awad NH, Ali MZ, Suganthan PN (2018) The 100-digit challenge: problem definitions and evaluation criteria for the 100-digit challenge special session and competition on single objective numerical optimization. Nanyang Technological University

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

    Google Scholar 

  54. Seyyedabbasi A, Aliyev R, Kiani F, Gulle M, Basyildiz H, Shah M (2021) Hybrid algorithms based on combining reinforcement learning and metaheuristic methods to solve global optimization problems. Knowl Based Syst 223:1–22

    Google Scholar 

  55. Molga M, Smutnicki C (2005) Test functions for optimization needs

  56. Jamil M, Yang X (2013) A literature survey of benchmark functions for global optimization problems. Int J Math Model Numer Optim 4(2):1–47

    MATH  Google Scholar 

  57. Van den Bergh F, Engelbrecht AP (2006) A study of particle swarm optimization particle trajectories. Inf Sci 176(8):937–971

    MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

  59. Chattopadhyay S (2004) Pressure vessels: design and practice, 1st edn. CRC Press, Boca Raton. https://doi.org/10.1201/9780203492468

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

    Google Scholar 

  61. Bayzidi H, Talatahari S, Saraee M, Lamarche CP (2021) Social network search for solving engineering optimization problems. Comput Intell Neurosci

  62. Nowcki H (1974) Optimization in pre-contract ship design. In: Fujita Y, Lind K, Williams TJ (eds) Computer applications in the automation of shipyard operation and ship design, vol 2. North Holland, Elsevier, New York, pp 327–338

    Google Scholar 

  63. Chickermane H, Gea HC (1996) Structural optimization using a new local approximation method. Int J Numer Methods Eng 39(5):829–846

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Software Engineering Department, Faculty of Engineering and Natural Science, Istinye University, Istanbul, Turkey

    Amir Seyyedabbasi & Farzad Kiani

Authors
  1. Amir Seyyedabbasi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Farzad Kiani
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

AS conceptualization, investigation, methodology, software, validation, formal analysis, original draft, writing—review and editing. FK conceptualization, supervision, project administration, methodology, writing—review and editing.

Corresponding author

Correspondence to Amir Seyyedabbasi.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

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

Supplementary Information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Seyyedabbasi, A., Kiani, F. Sand Cat swarm optimization: a nature-inspired algorithm to solve global optimization problems. Engineering with Computers 39, 2627–2651 (2023). https://doi.org/10.1007/s00366-022-01604-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-022-01604-x

Keywords

Search

Navigation