Skip to main content
SpringerLink
Log in

Prairie Dog Optimization Algorithm

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This study proposes a new nature-inspired metaheuristic that mimics the behaviour of the prairie dogs in their natural habitat called the prairie dog optimization (PDO). The proposed algorithm uses four prairie dog activities to achieve the two common optimization phases, exploration and exploitation. The prairie dogs' foraging and burrow build activities are used to provide exploratory behaviour for PDO. The prairie dogs build their burrows around an abundant food source. As the food source gets depleted, they search for a new food source and build new burrows around it, exploring the whole colony or problem space to discover new food sources or solutions. The specific response of the prairie dogs to two unique communication or alert sound is used to accomplish exploitation. The prairie dogs have signals or sounds for different scenarios ranging from predator threats to food availability. Their communication skills play a significant role in satisfying the prairie dogs' nutritional needs and anti-predation abilities. These two specific behaviours result in the prairie dogs converging to a specific location or a promising location in the case of PDO implementation, where further search (exploitation) is carried out to find better or near-optimal solutions. The performance of PDO in carrying out optimization is tested on a set of twenty-two classical benchmark functions and ten CEC 2020 test functions. The experimental results demonstrate that PDO benefits from a good balance of exploration and exploitation. Compared with the results of other well-known population-based metaheuristic algorithms available in the literature, the PDO shows stronger performance and higher capabilities than the other algorithms. Furthermore, twelve benchmark engineering design problems are used to test the performance of PDO, and the results indicate that the proposed PDO is effective in estimating optimal solutions for real-world optimization problems with unknown global optima. The PDO algorithm source codes is publicly available at https://www.mathworks.com/matlabcentral/fileexchange/110980-prairie-dog-optimization-algorithm.

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

Access this article

Log in via an institution

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
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30
Fig. 31
Fig. 32

Similar content being viewed by others

References

  1. Ezugwu AE (2021) Advanced discrete firefly algorithm with adaptive mutation-based neighborhood search for scheduling unrelated parallel machines with sequence-dependent setup times. Int J Intell Syst

  2. Horst R, Tuy H (2013) Global optimization: deterministic approaches. Springer, New York

    MATH  Google Scholar 

  3. Abualigah L (2020) Group search optimizer: a nature-inspired meta-heuristic optimization algorithm with its results, variants, and applications. Neural Comput Appl 25:1–24

    Google Scholar 

  4. Ezugwu AE, Shukla AK, Nath R, Akinyelu AA, Agushaka JO, Chiroma H, Muhuri PK (2021) Metaheuristics: a comprehensive overview and classification along with bibliometric analysis. Artif Intell Rev 87:1–80

    Google Scholar 

  5. Agushaka JO, Ezugwu AE (2021) Evaluation of several initialization methods on arithmetic optimization algorithm performance. J Intell Syst 31(1):70–94

    Article  Google Scholar 

  6. Agushaka J, Ezugwu A (2020) Influence of initializing krill herd algorithm with low-discrepancy sequences. IEEE Access 8:210886–210909

    Article  Google Scholar 

  7. Gardiner CW (1985) Handbook of stochastic methods, vol 3. Springer, Berlin

    Google Scholar 

  8. Agushaka JO, Ezugwu AE (2022) Influence of probability distribution initialization methods on the Performance of Advanced Arithmetic Optimization Algorithm with Application to Unrelated Parallel Machine Scheduling Problem. Concurr Comput Pract Exp

  9. Dokeroglu T, Sevinc E, Kucukyilmaz T, Cosar A (2019) A survey on new generation metaheuristic algorithms. Comput Ind Eng 137:106040

    Google Scholar 

  10. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Michigan (second edition: MIT Press, 1992)

  11. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN'95-international conference on neural networks, vol 4

  12. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680

    Article  MathSciNet  MATH  Google Scholar 

  13. Dorigo M, Di Caro G (1999) Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406), vol 2

  14. Akay B, Karaboga D (2012) Artificial bee colony algorithm for large-scale problems and engineering design optimization. J Intell Manuf 23(4):1001–1014

    Article  Google Scholar 

  15. Agushaka JO, Ezugwu AE (2022) Initialisation approaches for population-based metaheuristic algorithms: a comprehensive review. Appl Sci 12(2):896

    Article  Google Scholar 

  16. Abualigah L, Diabat A (2021) Advances in sine cosine algorithm: a comprehensive survey. Artif Intell Rev 54:1–42

    Article  Google Scholar 

  17. Ezugwu AE, Adeleke OJ, Akinyelu AA, Viriri S (2020) A conceptual comparison of several metaheuristic algorithms on continuous optimization problems. Neural Comput Appl 32(10):6207–6251

    Article  Google Scholar 

  18. Ezugwu AE, Akutsah F (2018) An improved firefly algorithm for the unrelated parallel machines scheduling problem with sequence-dependent setup times. IEEE Access 6:54459–54478

    Article  Google Scholar 

  19. Noshadi A, Shi J, Lee WS, Shi P, Kalam A (2016) Optimal PID-type fuzzy logic controller for a multi-input multi-output active magnetic bearing system. Neural Comput Appl 27(7):2031–2046

    Article  Google Scholar 

  20. Abonyi J, Feil B (2007) Cluster analysis for data mining and system identification. Springer, Birkhäuser

    MATH  Google Scholar 

  21. Nguyen P, Kim JM (2016) Adaptive ECG denoising using genetic algorithm-based thresholding and ensemble empirical mode decomposition. Inf Sci 373:499–511

    Article  Google Scholar 

  22. Oyelade ON, Ezugwu AE (2021) Characterization of abnormalities in breast cancer images using nature-inspired metaheuristic optimized convolutional neural networks model. Concurr Comput Pract Exp 84:e6629

    Google Scholar 

  23. Oyelade ON, Ezugwu AE (2021) A bioinspired neural architecture search based convolutional neural network for breast cancer detection using histopathology images. Sci Rep 11(1):1–28

    Article  Google Scholar 

  24. Idris H, Ezugwu AE, Junaidu SB, Adewumi AO (2017) An improved ant colony optimization algorithm with fault tolerance for job scheduling in grid computing systems. PLoS ONE 12(5):e0177567

    Article  Google Scholar 

  25. Ezugwu AE, Adeleke OJ, Viriri S (2018) Symbiotic organisms search algorithm for the unrelated parallel machines scheduling with sequence-dependent setup times. PLoS ONE 13(7):e0200030

    Article  Google Scholar 

  26. Ezugwu AE (2019) Enhanced symbiotic organisms search algorithm for unrelated parallel machines manufacturing scheduling with setup times. Knowl-Based Syst 172:15–32

    Article  Google Scholar 

  27. Agushaka JO, Ezugwu AE (2021) Advanced Arithmetic Optimization Algorithm for solving mechanical engineering design problems. PLoS ONE 16(8):e0255703

    Article  Google Scholar 

  28. Abualigah L, AbdElaziz M, Sumari P, Geem ZW, Gandomi AH (2021) Reptile Search Algorithm (RSA): a nature-inspired meta-heuristic optimizer. Expert Syst Appl 191:116158

    Article  Google Scholar 

  29. Kosorukoff A (2001) Human based genetic algorithm. In: 2001 IEEE international conference on systems, man and cybernetics. e-systems and e-man for cybernetics in cyberspace (Cat. No. 01CH37236)

  30. Biswas A, Mishra K, Tiwari S, Misra A (2013) Physics-inspired optimization algorithms: a survey. J Optim 984:2013

    Google Scholar 

  31. Parpinelli RS, Lopes HS (2011) New inspirations in swarm intelligence: a survey. Int J Bio-Inspired Comput 3(1):1–16

    Article  Google Scholar 

  32. Fogel DB (1998) Artificial intelligence through simulated evolution. Wiley, New York

    MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  34. Hansen N, Müller SD, Koumoutsakos P (2003) Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evol Comput 11(1):1–18

    Article  Google Scholar 

  35. Połap D, Woźniak M (2021) Red fox optimization algorithm. Expert Syst Appl 166:114107

    Article  Google Scholar 

  36. Abualigah L, Shehab M, Alshinwan M, Alabool H (2019) Salp swarm algorithm: a comprehensive survey. Neural Comput Appl 32:11195–11215

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  38. Hashim FA, Hussain K, Houssein EH, Mabrouk MS, Al-Atabany W (2021) Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. Appl Intell 51(3):1531–1551

    Article  MATH  Google Scholar 

  39. Kaveh A, Dadras A (2017) A novel meta-heuristic optimization algorithm: thermal exchange optimization. Adv Eng Softw 110:69–84

    Article  Google Scholar 

  40. Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315

    Article  Google Scholar 

  41. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: 2007 IEEE congress on evolutionary computation

  42. Ghosh A, Das S, Mullick SS, Mallipeddi R, Das AK (2017) A switched parameter differential evolution with optional blending crossover for scalable numerical optimization. Appl Soft Comput 57:329–352

    Article  Google Scholar 

  43. Ghambari S, Rahati A (2018) An improved artificial bee colony algorithm and its application to reliability optimization problems. Appl Soft Comput 62:736–767

    Article  Google Scholar 

  44. Zhong F, Li H, Zhong S (2016) A modified ABC algorithm based on improved-global-best-guided approach and adaptive-limit strategy for global optimization. Appl Soft Comput 46:469–486

    Article  Google Scholar 

  45. Sun G, Liu Y, Yang M, Wang A, Liang S, Zhang Y (2017) Coverage optimization of VLC in smart homes based on improved cuckoo search algorithm. Comput Netw 116:63–78

    Article  Google Scholar 

  46. Peraza C, Valdez F, Garcia M, Melin P, Castillo O (2016) A new fuzzy harmony search algorithm using fuzzy logic for dynamic parameter adaptation. Algorithms 9(4):69

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  48. Hygnstrom SE, Virchow DR (2002) Prairie dogs and the prairie ecosystem. Pap Nat Resour 36:3149

    Google Scholar 

  49. Long K (2002) Prairie dogs: a wildlife handbook. Johnson Books, Boulder

    Google Scholar 

  50. Hoogland JL (1995) The black-tailed prairie dog: social life of a burrowing mammal. University of Chicago Press, Chicago

    Google Scholar 

  51. Chance G (1976) Wonders of prairie dogs. Dodd, Mead, and Company, New York

    Google Scholar 

  52. Fitzgerald JP, Lechleitner RR (1974) Observations on the biology of Gunnison’s prairie dog in central Colorado. Am Midl Nat 87:146–163

    Article  Google Scholar 

  53. Mulhern DW, Knowles CJ (1997) Black-tailed prairie dog status and future conservation planning. In: Uresk DW, Schenbeck GL, O'Rourke JT (eds) Conserving Biodiversity on Native Rangelands: symposium proceedings: August 17, 1995, Fort Robinson State Park, Nebraska. General Technical Report RM-GTR-298. US Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station, Fort Collins, vol 298, pp 19–29

  54. Slobodchikoff CN, Kiriazis J, Fischer C, Creef E (1991) Semantic information distinguishing individual predators in the alarm calls of Gunnison’s prairie dogs. Anim Behav 42(5):713–719

    Article  Google Scholar 

  55. Slobodchikoff CN, Perla BS, Verdolin JL (2009) Prairie dogs: communication and community in an animal society. Harvard University Press, Harvard

    Book  Google Scholar 

  56. Slobodchikoff CN (2002) Cognition and communication in prairie dogs. In: Beckoff M, Allen C, Burghardt GM (eds) The cognitive animal: empirical and theoretical perspectives on animal cognition. A Bradford Book, Cambridge, pp 257–264

    Google Scholar 

  57. Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: 2009 World congress on nature & biologically inspired computing (NaBIC)

  58. Agushaka JO, Ezugwu AE, Abualigah L (2022) Dwarf mongoose optimization algorithm. Comput Methods Appl Mech Eng 391:114570

    Article  MathSciNet  MATH  Google Scholar 

  59. Abualigah L, Diabat A, Mirjalili S, AbdElaziz M, Gandomi AH (2021) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609

    Article  MathSciNet  MATH  Google Scholar 

  60. Rather S, Bala P (2019) Hybridization of constriction coefficient based particle swarm optimization and gravitational search algorithm for function optimization. In: International conference on advances in electronics, electrical, and computational intelligence (ICAEEC-2019)

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

    Article  Google Scholar 

  62. Mirjalili S, Gandomi A, Mirjalili S, Saremi S, Faris H, Mirjalili S (2017) Salp swarm algorithm: a bioinspired optimizer for engineering design problems. Adv Eng Softw 854:1–29

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  66. Dhiman G, Kumar V (2017) Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Adv Eng Softw 114:48–70

    Article  Google Scholar 

  67. Eskandar H, Sadollah A, Bahreininejad A, Hamdi M (2012) Water cycle algorithm-a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct 110(111):151–166

    Article  Google Scholar 

  68. Faramarzi A, Heidarinejad M, Stephens B, Mirjalili S (2019) Knowledge-based systems equilibrium optimizer: a novel optimization algorithm. Knowl Based Syst 191, Article ID 105190

  69. Bayzidi H, Talatahari S, Saraee M, Lamarche CP (2021) Social network search for solving engineering optimization problems. Comput Intell Neurosci 85:2021

    Google Scholar 

  70. Sandgren E (1990) NIDP in mechanical design optimization. J Mech Des 112(2):223–229

    Article  Google Scholar 

  71. Kaveh A, Dadras Eslamlou A (2020) Water strider algorithm: a new metaheuristic and applications. Structures 25:520–541

    Article  MATH  Google Scholar 

  72. Kazemzadeh-Parsi MJ (2014) A modified firefly algorithm for engineering design optimization problems. Iranian Journal of Science and Technology. Trans Mech Eng 38(2):403

    Google Scholar 

  73. Faramarzi A, Heidarinejad M, Mirjalili S, Gandomi AH (2020) Marine predators algorithm: a nature-inspired metaheuristic. Expert Syst Appl 152:113377

    Article  Google Scholar 

  74. Siddall JN (1972) Analytical decision-making in engineering design. Prentice Hall, Hoboken

    Google Scholar 

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

    Article  Google Scholar 

  76. Han X, Yue L, Dong Y, Xu Q, Xie G, Xu X (2020) Efficient hybrid algorithm based on moth search and fireworks algorithm for solving numerical and constrained engineering optimization problems. J Supercomput 76:9404–9429

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  78. Rao SS (2009) Engineering optimization. Wiley, Hoboken

    Book  Google Scholar 

  79. Parkinson A, Balling R, Hedengren JD (2018) Optimization methods for engineering design, 2nd edn. Brigham Young University, Brigham

    Google Scholar 

  80. Ravindran A, Ragsdell KM, Reklaitis GV (2006) Engineering optimization. Wiley, Hoboken

    Book  Google Scholar 

  81. Amir HM, Hasegawa T (1989) Nonlinear mixed-discrete structural optimization. J Struct Eng 115(3):626–646

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Road, Pietermaritzburg, 3201, KwaZulu-Natal, South Africa

    Absalom E. Ezugwu & Jeffrey O. Agushaka

  2. Department of Computer Science, Federal University of Lafia, LafiaNasarawa State, 950101, Nigeria

    Jeffrey O. Agushaka

  3. Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, 11953, Jordan

    Laith Abualigah

  4. Faculty of Information Technology, Middle East University, Amman, 11831, Jordan

    Laith Abualigah

  5. School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang, 11800, Malaysia

    Laith Abualigah

  6. Centre for Artificial Intelligence Research and Optimization, Torrens University, Adelaide, Australia

    Seyedali Mirjalili

  7. Yonsei Frontier Lab, Yonsei University, Seoul, Korea

    Seyedali Mirjalili

  8. Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia

    Amir H. Gandomi

Authors
  1. Absalom E. Ezugwu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jeffrey O. Agushaka
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Laith Abualigah
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Seyedali Mirjalili
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Amir H. Gandomi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Absalom E. Ezugwu or Laith Abualigah.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest relating to this work.

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

Ezugwu, A.E., Agushaka, J.O., Abualigah, L. et al. Prairie Dog Optimization Algorithm. Neural Comput & Applic 34, 20017–20065 (2022). https://doi.org/10.1007/s00521-022-07530-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07530-9

Keywords

Search

Navigation