Skip to main content
SpringerLink
Log in

A Metaheuristic Scheme Based on the Hunting Model of Yellow Saddle Goatfish

  • Chapter
  • First Online:
Recent Metaheuristic Computation Schemes in Engineering

Part of the book series: Studies in Computational Intelligence ((SCI,volume 948))

Abstract

Animals have demonstrated clever hunting strategies when they collaborate with each other. These techniques involve swarm intelligence and have been applied to develop bio-inspired algorithms, a research field for solving optimization problems. In the last years, several methods arise from inspiration in nature to model collective animal behavior.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

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

    Google Scholar 

  2. Ab Aziz NA, Mubin M, Mohamad MS, Ab Aziz K (2014) A synchronous-asynchronous particle swarm optimization algorithm. Sci World J 17

    Google Scholar 

  3. Karaboga D (2005) An idea based on Honey Bee Swarm for Numerical Optimization. In: Tech. Rep. TR06, Erciyes Univ., no. TR06, p 10

    Google Scholar 

  4. Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12

    Article  Google Scholar 

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

    Article  Google Scholar 

  6. Luo F, Zhao J, Dong ZY (2016) A new metaheuristic algorithm for real-parameter optimization: natural aggregation algorithm. In: 2016 The IEEE congress on evolutionary computation, pp 94–103

    Google Scholar 

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

    Article  Google Scholar 

  8. Fausto F, Cuevas E, Valdivia A, González A (2017) A global optimization algorithm inspired in the behavior of selfish herds. Biosystems 160:39–55

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. Webster B, Bernhard PJ A local search optimization algorithm based on natural principles of gravitation

    Google Scholar 

  12. Erol OK, Eksin I (2006) A new optimization method: Big Bang-Big Crunch. Adv Eng Softw 37(2):106–111

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  14. Schmitt LM (2001) Theory of genetic algorithms. Theor Comput Sci 259(1):1–61

    Article  MathSciNet  Google Scholar 

  15. Price JA, Kenneth S, Lampinen RM (2005) Differential evolution. Springer, Berlin/Heidelberg

    Google Scholar 

  16. Rechenberg I (1978) Evolutions strategien. Springer, Berlin, pp 83–114

    Google Scholar 

  17. Olorunda O, Engelbrecht AP (2008) Measuring exploration/exploitation in particle swarms using swarm diversity. In: 2008 IEEE congress on evolutionary computation (IEEE world congress on computational intelligence), pp 1128–1134

    Google Scholar 

  18. Lin L, Gen M (2009) Auto-tuning strategy for evolutionary algorithms: balancing between exploration and exploitation. Soft Comput 13(2):157–168

    Article  Google Scholar 

  19. Randall JE (2014) The goatfishes Parupeneus cyclostomus, P. macronemus and freeloaders, vol 20, no 2. Aquaprint

    Google Scholar 

  20. McCormick MI (1995) Fish feeding on mobile benthic invertebrates: influence of spatial variability in habitat associations. Mar Biol 121(4):627–637

    Article  Google Scholar 

  21. Randall JE (1983) Red Sea reef fishes. IMMEL

    Google Scholar 

  22. Randall JE (2007) Reef and shore fishes of the Hawaiian Islands. Sea Grant College Program, University of Hawai

    Google Scholar 

  23. Strübin C, Steinegger M, Bshary R (2011) On group living and collaborative hunting in the yellow saddle goatfish (Parupeneus cyclostomus)1. Ethology 117(11):961–969

    Article  Google Scholar 

  24. Arnegard ME, Carlson BA (2005) Electric organ discharge patterns during group hunting by a mormyrid fish. Proc Biol Sci 272(1570):1305–1314

    Google Scholar 

  25. Bshary R, Hohner A, Ait-el-Djoudi K, Fricke H (2006) Interspecific communicative and coordinated hunting between groupers and giant moray eels in the red sea. PLoS Biol 4(12):e431

    Article  Google Scholar 

  26. Boesch C, Boesch H (1989) Hunting behavior of wild Chimpanzees in the tai’ National Park. Am J Phys Anthropol 78547–78573

    Google Scholar 

  27. Stander PE (1992) Cooperative hunting in lions: the role of the individual. Source Behav Ecol Sociobiol Behav Ecol Sociobiol 29(6):445–454

    Google Scholar 

  28. Biro D, Sasaki T, Portugal SJ (2016) Bringing a time-depth perspective to collective animal behaviour. Trends Ecol Evol 31(7):550–562

    Article  Google Scholar 

  29. Kalyani S, Swarup KS (2011) Particle swarm optimization-based K-means clustering approach for security assessment in power systems. Expert Syst Appl 38(9):10839–10846

    Article  Google Scholar 

  30. Al-Harbi SH, Rayward-Smith VJ (2006) Adapting k-means for supervised clustering. Appl Intell 24(3):219–226

    Article  Google Scholar 

  31. Kaufman L, Rousseeuw PJ (eds) (1990) Finding groups in data. Wiley Inc., Hoboken, NJ, USA

    MATH  Google Scholar 

  32. MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth berkeley symposium on mathematical statistics and probability, vol 1, Statistics, pp 281–297

    Google Scholar 

  33. Jain AK (2010) Data clustering: 50 years beyond K-means. Pattern Recognit Lett 31(8):651–666

    Article  Google Scholar 

  34. Likas A, Vlassis N, Verbeek JJ (2003) The global k-means clustering algorithm. Pattern Recognit 36(2):451–461

    Article  Google Scholar 

  35. Hartigan JA, Wong MA (1979) A K-means clustering algorithm. Source J R Stat Soc Ser C (Appl Stat) 28(1):100–108

    Google Scholar 

  36. Forgy E (1965) Cluster analysis of multivariate data: efficiency versus interpretability of classification. Biometrics 21(3):768–769

    Google Scholar 

  37. Lloyd S (1982) Least squares quantization in PCM. IEEE Trans Inf Theory 28(2):129–137

    Article  MathSciNet  Google Scholar 

  38. Redmond SJ, Heneghan C (2007) A method for initializing the K-means clustering algorithm using kd-trees. Pattern Recognit Lett 28(8):965–973

    Article  Google Scholar 

  39. Chechkin A, Metzler R, Klafter J, Gonchar V (2008) Introduction to the theory of lévy flights. In: Anomalous transport: foundations and applications, pp 129–162

    Google Scholar 

  40. Haklı H, Uğuz H (2014) A novel particle swarm optimization algorithm with Levy flight. Appl Soft Comput 23:333–345

    Article  Google Scholar 

  41. Yang X-S, Deb S (2013) Multiobjective cuckoo search for design optimization. Comput Oper Res 40(6):1616–1624

    Article  MathSciNet  Google Scholar 

  42. Yang X-S (2010) Engineering optimization: an introduction with metaheuristic applications, 1st edn. Wiley

    Google Scholar 

  43. Marini F, Walczak B (2015) Particle swarm optimization (PSO). A tutorial. Chemom Intell Lab Syst 149:153–165

    Article  Google Scholar 

  44. García S, Molina D, Lozano M, Herrera F (2009) A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 special session on real parameter optimization. J Heuristics 15(6):617–644

    Article  Google Scholar 

  45. Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics Bull 1(6):80–83

    Article  Google Scholar 

  46. Hochberg Y (1988) A sharper Bonferroni procedure for multiple tests of significance. Biometrika

    Google Scholar 

  47. Armstrong RA (2014) When to use the Bonferroni correction. Ophthalmic Physiol Opt 34(5):502–508

    Article  Google Scholar 

  48. Sandgren E (1990) Nonlinear integer and discrete programming in mechanical design optimization. J Mech Des 112(2):223

    Article  Google Scholar 

  49. Arora JS (2012) Chapter 12—numerical methods for constrained optimum design. In: Introduction to optimum design, pp 491–531

    Google Scholar 

  50. Das S, Suganthan P (2018) Problem definitions and evaluation criteria for CEC 2011 competition on testing evolutionary algorithms on real world optimization problems

    Google Scholar 

  51. Koski J (1985) Defectiveness of weighting method in multi-criterion optimization of structures. Commun Appl Numer Methods 1(6):333–337

    Article  Google Scholar 

  52. Cuevas E (2013) Block-matching algorithm based on harmony search optimization for motion estimation. Appl Intell 39(1):165–183

    Google Scholar 

  53. Díaz P, Pérez-Cisneros M, Cuevas E, Hinojosa S, Zaldivar D (2018) An improved crow search algorithm applied to energy problems. Energies 11(3):571

    Article  Google Scholar 

  54. Cuevas E, Gálvez J, Hinojosa S, Zaldívar D, Pérez-Cisneros M (2014) A comparison of evolutionary computation techniques for IIR model identification. J Appl Math 827206

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CUCEI, Universidad de Guadalajara, Guadalajara, Mexico

    Erik Cuevas & Alma Rodríguez

  2. Facultad de Ingeniería, Universidad Panamericana, Zapopan, Jalisco, Mexico

    Alma Rodríguez, Avelina Alejo-Reyes & Carolina Del-Valle-Soto

  3. Centro de Eneñanza Técnica Industrial, Universidad de Guadalajara, Guadalajara, Mexico

    Alma Rodríguez

Authors
  1. Erik Cuevas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alma Rodríguez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Avelina Alejo-Reyes
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Carolina Del-Valle-Soto
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Erik Cuevas .

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Cuevas, E., Rodríguez, A., Alejo-Reyes, A., Del-Valle-Soto, C. (2021). A Metaheuristic Scheme Based on the Hunting Model of Yellow Saddle Goatfish. In: Recent Metaheuristic Computation Schemes in Engineering. Studies in Computational Intelligence, vol 948. Springer, Cham. https://doi.org/10.1007/978-3-030-66007-9_2

Download citation

Publish with us

Policies and ethics

Search

Navigation