Advertisement

Neural Computing and Applications

, Volume 31, Issue 12, pp 8837–8857 | Cite as

The naked mole-rat algorithm

  • Rohit SalgotraEmail author
  • Urvinder Singh
Original Article
  • 128 Downloads

Abstract

This work proposes a new swarm intelligent nature-inspired algorithm called naked mole-rat (NMR) algorithm. This NMR algorithm mimics the mating patterns of NMRs present in nature. Two types of NMRs called workers and breeders are found to depict these patterns. Workers work continuously in the endeavor to become breeders, while breeders compete among themselves to mate with the queen. Those breeders who become sterile are pushed back to the worker’s group, and the fittest worker becomes a new breeder. This phenomenon has been adapted to develop the NMR algorithm. The algorithm has been benchmarked on 27 well-known test functions, and its performance is evaluated by a comparative study with particle swarm optimization (PSO), grey wolf optimization (GWO), whale optimization algorithm (WOA), differential evolution (DE), gravitational search algorithm (GSA), fast evolutionary programming (FEP), bat algorithm (BA), flower pollination algorithm (FPA), and firefly algorithm (FA). The experimental results and statistical analysis prove that NMR algorithm is very competitive as compared to other state-of-the-art algorithms. The matlab code for NMR algorithm is avaliable at https://github.com/rohitsalgotra/Naked-Mole-Rat-Algorithm.

Keywords

Swarm intelligence Eusociality Optimization Benchmark Naked mole-rat algorithm 

Notes

Acknowledgements

Rohit Salgotra acknowledges the support of INSPIRE Fellowship (IF-160215) by Directorate of Science and Technology, Govt. of India.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal rights

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

Informed consent

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5).

References

  1. 1.
    Holland JH (1992) Genetic algorithms. Sci Am 267(1):66–72Google Scholar
  2. 2.
    Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359MathSciNetzbMATHGoogle Scholar
  3. 3.
    Rechenberg I (1978) Evolutionsstrategien. In: Simulationsmethoden in der Medizin und Biologie. Springer Berlin Heidelberg, pp 83–114Google Scholar
  4. 4.
    Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713Google Scholar
  5. 5.
    Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection, vol 1. MIT Press, CambridgezbMATHGoogle Scholar
  6. 6.
    Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98Google Scholar
  7. 7.
    Dasgupta D, Michalewicz Z (eds) (2013) Evolutionary algorithms in engineering applications. Springer, BerlinGoogle Scholar
  8. 8.
    Beni G, Wang J (1993) Swarm intelligence in cellular robotic systems. In: Robots and biological systems: towards a new bionics? Springer Berlin Heidelberg, pp 703–712Google Scholar
  9. 9.
    Karaboga D (2005) An idea based on honey bee swarm for numerical optimization, vol 200. Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering DepartmentGoogle Scholar
  10. 10.
    Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the 1995 IEEE international conference on neural networks, pp 1942–1948Google Scholar
  11. 11.
    Yang XS (2010) Firefly algorithm, stochastic test functions, and design optimisation. International Journal of Bio-Inspired Computation 2(2):78–84Google Scholar
  12. 12.
    Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE control systems 22(3):52–67MathSciNetGoogle Scholar
  13. 13.
    Yang XS (2012) Flower pollination algorithm for global optimization. In: International conference on unconventional computing and natural computation. Springer Berlin Heidelberg, pp 240–249Google Scholar
  14. 14.
    Singh U, Salgotra R (2018) Synthesis of linear antenna array using flower pollination algorithm. Neural Comput Appl 29(2):435–445Google Scholar
  15. 15.
    Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010). Springer Berlin Heidelberg, pp 65–74Google Scholar
  16. 16.
    Faris H, Mirjalili S, Aljarah I, Mafarja M, Heidari AA (2020) Salp swarm algorithm: theory, literature review, and application in extreme learning machines. In: Nature-inspired optimizers. Springer, Cham, pp 185–199Google Scholar
  17. 17.
    Khalilpourazari S, Khalilpourazary S (2018) Optimization of time, cost and surface roughness in grinding process using a robust multi-objective dragonfly algorithm. Neural Comput Appl 1–12Google Scholar
  18. 18.
    Salgotra R, Singh U, Saha S (2018) New cuckoo search algorithms with enhanced exploration and exploitation properties. Expert Syst Appl 95:384–420Google Scholar
  19. 19.
    El Aziz MA, Hassanien AE (2018) Modified cuckoo search algorithm with rough sets for feature selection. Neural Comput Appl 29(4):925–934Google Scholar
  20. 20.
    Armaghani DJ, Hasanipanah M, Mahdiyar A, Majid MZA, Amnieh HB, Tahir MM (2018) Airblast prediction through a hybrid genetic algorithm-ANN model. Neural Comput Appl 29(9):619–629Google Scholar
  21. 21.
    Liu A, Li P, Sun W, Deng X, Li W, Zhao Y, Liu B (2019). Prediction of mechanical properties of micro-alloyed steels via neural networks learned by water wave optimization. Neural Comput Appl 1–16Google Scholar
  22. 22.
    Aljarah I, Faris H, Mirjalili S (2018) Optimizing connection weights in neural networks using the whale optimization algorithm. Soft Comput 22(1):1–15Google Scholar
  23. 23.
    Kaur K, Singh U, Salgotra R (2018) An enhanced moth flame optimization. Neural Comput Appl 1–35Google Scholar
  24. 24.
    Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm intelligence: from natural to artificial systems (No. 1). Oxford University Press, New YorkzbMATHGoogle Scholar
  25. 25.
    Jeanne RL (1986) The evolution of the organization of work in social insects. Monitore Zoologico Italiano-Italian Journal of Zoology 20(2):119–133Google Scholar
  26. 26.
    Oster GF, Wilson EO (1978) Caste and ecology in the social insects. Princeton University Press, PrincetonGoogle Scholar
  27. 27.
    Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82Google Scholar
  28. 28.
    Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697Google Scholar
  29. 29.
    Lampinen J, Zelinka I (2000) On stagnation of the differential evolution algorithm. In: Proceedings of MENDEL, pp 76–83Google Scholar
  30. 30.
    Shi Y, Eberhart RC (1998) Parameter selection in particle swarm optimization. In: International conference on evolutionary programming. Springer Berlin Heidelberg, pp 591–600Google Scholar
  31. 31.
    Blouin SF, Blouin M (1988) Inbreeding avoidance behaviors. Trends Ecol Evol 3(9):230–233Google Scholar
  32. 32.
    Niu P, Niu S, Chang L (2019) The defect of the Grey Wolf optimization algorithm and its verification method. Knowl Based SystGoogle Scholar
  33. 33.
    Williams GC (1975) Sex and evolution (No. 8). Princeton University Press, PrincetonGoogle Scholar
  34. 34.
    Hamilton WD (1993) Inbreeding in Egypt and in this book: a childish perspective. The natural history of Inbreeding and outbreeding, pp 429–450Google Scholar
  35. 35.
    Bateson P (1983) Optimal outbreeding. Mate Choice 257:277Google Scholar
  36. 36.
    Thornhill NW (1993) The natural history of inbreeding and outbreeding: theoretical and empirical perspectives. University of Chicago Press, ChicagoGoogle Scholar
  37. 37.
    Smith RH (1979) On selection for inbreeding in polygynous animals. Heredity 43(2):205–211Google Scholar
  38. 38.
    Parker GA (1979) Sexual selection and sexual conflict. In: Blum MS, Blum NA (eds) Sexual selection and reproductive competition in insects, pp 123–163Google Scholar
  39. 39.
    Ciszek D (2000) New colony formation in the “highly inbred” eusocial naked mole-rat: outbreeding is preferred. Behav Ecol 11(1):1–6Google Scholar
  40. 40.
    Buffenstein R, Jarvis JU, Opperman LA, Cavaleros M, Ross FP, Pettifor JM (1994) Subterranean mole-rats naturally have an impoverished calciol status, yet synthesize calciol metabolites and calbindins. Eur J Endocrinol 130(4):402–409Google Scholar
  41. 41.
    Sheffield SR, Sawicka-Kapusta K, Cohen JB, Rattner BA (2001) Rodentia and LagomorphaGoogle Scholar
  42. 42.
    Wilson DE, Reeder DM (eds) (2005) Mammal species of the world: a taxonomic and geographic reference. JHU Press, BaltimoreGoogle Scholar
  43. 43.
    Deuve JL, Bennett NC, Britton-Davidian J, Robinson TJ (2008) Chromosomal phylogeny and evolution of the African mole-rats (Bathyergidae). Chromosome Res 16(1):57–74Google Scholar
  44. 44.
    Brett RA (1991) The population structure of naked mole-rat colonies. The biology of the naked mole-rat, 97Google Scholar
  45. 45.
    Sherman PW, Jarvis JUM, Alexander RD (eds) (1991) The biology of inbreeding and outbreeding. University of Chicago Press, ChicagoGoogle Scholar
  46. 46.
    Edrey YH, Hanes M, Pinto M, Mele J, Buffenstein R (2011) Successful aging and sustained good health in the naked mole rat: a long-lived mammalian model for biogerontology and biomedical research. ILAR J 52(1):41–53Google Scholar
  47. 47.
    O’Riain MJ, Jarvis JUM, Alexander R, Buffenstein R, Peeters C (2000) Morphological castes in a vertebrate. Proc Natl Acad Sci 97(24):13194–13197Google Scholar
  48. 48.
    Crish SD, Dengler-Crish CM, Catania KC (2006) Central visual system of the naked mole-rat (Heterocephalus glaber). Anat Rec A Discov Mol Cell Evol Biol 288(2):205–212Google Scholar
  49. 49.
    Faulkes CG, Abbott DH, Jarvis JUM, Sherriff FE (1990) LH responses of female naked mole-rats, Heterocephalus glaber, to single and multiple doses of exogenous GnRH. J Reprod Fertil 89(1):317–323Google Scholar
  50. 50.
    Clarke FM, Faulkes CG (1998) Hormonal and behavioral correlates of male dominance and reproductive status in captive colonies of the naked mole–rat, Heterocephalus glaber. Proceedings of the Royal Society of London B: Biological Sciences 265(1404):1391–1399Google Scholar
  51. 51.
    Alexander RD, Noonan KM, Crespi BJ (1991) The evolution of eusociality. The biology of the naked mole-rat 3:44Google Scholar
  52. 52.
    Jarvis JU (1981) Eusociality in a mammal: cooperative breeding in naked mole-rat colonies. Science 212(4494):571–573Google Scholar
  53. 53.
    Van Den Bergh F, Engelbrecht AP (2006) A study of particle swarm optimization particle trajectories. Inf Sci 176(8):937–971MathSciNetzbMATHGoogle Scholar
  54. 54.
    Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation 1(1):3–18Google Scholar
  55. 55.
    Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67Google Scholar
  56. 56.
    Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61Google Scholar
  57. 57.
    Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248zbMATHGoogle Scholar
  58. 58.
    Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102Google Scholar
  59. 59.
    Črepinšek M, Liu SH, Mernik M (2013) Exploration and exploitation in evolutionary algorithms: a survey. ACM Computing Surveys (CSUR) 45(3):35zbMATHGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of ECEThapar UniversityPatialaIndia

Personalised recommendations