Skip to main content
SpringerLink
Log in

An Introduction to Nature-Inspired Metaheuristics and Swarm Methods

  • Chapter
  • First Online:
New Advancements in Swarm Algorithms: Operators and Applications

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 160))

Abstract

Mathematical Optimization is an current problem in many different areas of science and technology; due to this, in the last few years, the interest on the development of methods for solving such kind of problems has increased an unprecedented way. As a result of the intensification in research aimed to the development of more powerful and flexible optimization tools, many different and unique approaches have been proposed and successfully applied to solve a wide array of real-world problems, but none has become as popular as the family of optimization methods known as nature-inspired metaheuristics. This compelling family of problem-solving approaches have become well-known among researchers around the world not only for to their many interesting characteristics, but also due to their ability to handle complex optimization problems, were other traditional techniques are known to fail on delivering competent solutions. Nature-inspired algorithms have become a world-wide phenomenon. Only in the last decade, literature related to this compelling family of techniques and their applications have experienced and astonishing increase in numbers, with hundreds of papers being published every single year. In this chapter, we present a broad review about nature-inspired optimization algorithms, highlighting some of the most popular methods currently reported on the literature as and their impact on the current research.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
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. Galinier, P., Hamiez, J.P., Hao, J.K., Porumbel, D.: Handbook of Optimization, vol. 38 (2013)

    Google Scholar 

  2. Cuevas, E., Díaz Cortés, M.A., Oliva Navarro, D.A.: Advances of Evolutionary Computation: Methods and Operators, 1st edn. Springer International Publishing (2016)

    Google Scholar 

  3. Cavazzuti, M.: Optimization Methods: From Theory to Design (2013)

    Google Scholar 

  4. Lin, M., Tsai, J., Yu, C.: A review of deterministic optimization methods in engineering and management. Math. Probl. Eng. 2012, 1–15 (2012)

    MathSciNet  MATH  Google Scholar 

  5. Schneider, J.J., Kirkpatrick, S.: Stochastic optimization (2006)

    Google Scholar 

  6. Cuevas, E., Osuna, V., Oliva, D.: Evolutionary Computation Techniques: A Comparative Perspective, vol. 686 (2017)

    Google Scholar 

  7. Díaz-Cortés, M.-A., Cuevas, E., Rojas, R.: Engineering Applications of Soft Computing (2017)

    Google Scholar 

  8. Yang, X.: Nature-Inspired Metaheuristic Algorithms, 2nd edn. Luniver Press, Beckington, UK (2008)

    Google Scholar 

  9. Binitha, S., Sathya, S.S.: A Survey of Bio inspired Optimization Algorithms. Int. J. Soft Comput. Eng. 2(2), 137–151 (2012)

    Google Scholar 

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

    Article  Google Scholar 

  11. Mitchell, M.: Genetic algorithms: an overview. Complexity 1(1), 31–39 (1995)

    Article  Google Scholar 

  12. Bäck, T., Hoffmeister, F., Schwefel, H.-P.: A survey of evolution strategies. In: Proceedings of the Fourth International Conference on Genetic Algorithms, 1991, vol. 9, no. 3, p. 8.

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  14. Sette, S., Boullart, L.: Genetic programming: principles and applications. Eng. Appl. Artif. Intell. 14(6), 727–736 (2001)

    Article  Google Scholar 

  15. Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization. Swarm Intell. 1(1), 33–57 (2007)

    Article  Google Scholar 

  16. Dorigo, M., Stützle, T.: Ant Colony Optimization (2004)

    Google Scholar 

  17. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. J. Glob. Optim. 39(3), 459–471 (2007)

    Article  MathSciNet  Google Scholar 

  18. Yang, X.: Firefly algorithm, Lévy flights and global optimization (2010)

    Google Scholar 

  19. Cuevas, E., Cienfuegos, M., Zaldívar, D., Pérez-cisneros, M.: A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Syst. Appl. 40(16), 6374–6384 (2013)

    Article  Google Scholar 

  20. Rutenbar, R.A.: Simulated annealing algorithms: an overview. IEEE Circuits Devices Mag. 5(1), 19–26 (1989)

    Article  Google Scholar 

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

    Article  Google Scholar 

  22. Birbil, Ş.I., Fang, S.C.: An electromagnetism-like mechanism for global optimization. J. Glob. Optim. 25(3), 263–282 (2003)

    Article  MathSciNet  Google Scholar 

  23. Cuevas, E., Echavarría, A., Ramírez-Ortegón, M.A.: An optimization algorithm inspired by the States of Matter that improves the balance between exploration and exploitation. Appl. Intell. 40(2), 256–272 (2013)

    Article  Google Scholar 

  24. Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)

    Article  Google Scholar 

  25. Tan, Y., Zhu, Y.: Fireworks algorithm for optimization. In: First International Conference, ICSI 2010—Proceedings, Part I, 2010, June, pp. 355–364

    Google Scholar 

  26. Atashpaz-Gargari, E., Lucas, C.: Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: 2007 IEEE Congress on Evolutionary Computation, CEC 2007, 2007, pp. 4661–4667

    Google Scholar 

  27. Opara, K.R., Arabas, J.: Differential evolution: a survey of theoretical analyses. Swarm Evol. Comput. June 2017, pp. 1–13 (2018)

    Google Scholar 

  28. Padhye, N., Mittal, P., Deb, K.: Differential evolution: performances and analyses. 2013 IEEE Congr. Evol. Comput. CEC 2013 (no. i), 1960–1967 (2013)

    Google Scholar 

  29. Mohamed, A.W., Sabry, H.Z., Khorshid, M.: An alternative differential evolution algorithm for global optimization. J. Adv. Res. 3(2), 149–165 (2012)

    Article  Google Scholar 

  30. Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011)

    Article  Google Scholar 

  31. Das, S., Mullick, S.S., Suganthan, P.N.: Recent advances in differential evolution—an updated survey. Swarm Evol. Comput. 27, 1–30 (2016)

    Article  Google Scholar 

  32. Piotrowski, A.P.: Review of differential evolution population size. Swarm Evol. Comput. 32, 1–24 (2017)

    Article  Google Scholar 

  33. Bäck, T., Foussette, C., Krause, P.: Contemporary evolution strategies, vol. 47 (2013)

    Google Scholar 

  34. Beyer, H.G., Sendhoff, B.: Covariance matrix adaptation revisited—the CMSA evolution strategy. Lecture Notes on Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5199 LNCS, pp. 123–132 (2008)

    Google Scholar 

  35. Auger, A., Schoenauer, M., Vanhaecke, N.: {LS-CMA-ES}: a second-order algorithm for covariance matrix adaptation. Parallel Probl Solving from Nat. PPSN VIII 3242(1), 182–191 (2004)

    Google Scholar 

  36. Salimans, T., Ho, J., Chen, X., Sidor, S., Sutskever, I.: Evolution strategies as a scalable alternative to reinforcement learning. arXiv:1703.03864v2, pp. 1–13 (2017)

  37. Mitchell, M.: An introduction to genetic algorithms. The MIT Press, Cambridge, MA (1996)

    MATH  Google Scholar 

  38. Sayed, G.I., Hassanien, A.E., Nassef, T.M.: Genetic and Evolutionary Computing, vol. 536, no. Mci (2017)

    Google Scholar 

  39. McCall, J.: Genetic algorithms for modelling and optimisation. J. Comput. Appl. Math. 184(1), 205–222 (2005)

    Article  MathSciNet  Google Scholar 

  40. Yadav, P.K., Prajapati, N.L.: An Overview of Genetic Algorithm and Modeling, vol. 2, no. 9, pp. 1–4 (2012)

    Google Scholar 

  41. Pham, D.T., Huynh, T.T.B., Bui, T.L.: A survey on hybridizing genetic algorithm with dynamic programming for solving the traveling salesman problem. In: 2013 International Conference on Soft Computing and Pattern Recognition, SoCPaR 2013, pp. 66–71 (2013)

    Google Scholar 

  42. Khu, S.T., Liong, S.Y., Babovic, V., Madsen, H., Muttil, N.: Genetic programming and its application in real-time runoff forecasting. J. Am. Water Resour. Assoc. 37(2), 439–451 (2001)

    Article  Google Scholar 

  43. Poli, R., Langdon, W.B., McPhee, N.F., Koza, J.R.: Genetic programming an introductory tutorial and a survey of techniques and applications. Tech Rep CES475, vol. 18, pp. 1–112 (Oct. 2007)

    Google Scholar 

  44. Harman, M., Langdon, W.B., Weimer, W.: Genetic Programming For Reverse Engineering. In: 20th Working Conference on Reverse Engineering WCRE 2013, pp. 1–10 (2013)

    Google Scholar 

  45. Gerules, G., Janikow, C.: A survey of modularity in genetic programming. 2016 IEEE Congr. Evol. Comput. CEC 2016, pp. 5034–5043 (2016)

    Google Scholar 

  46. Vanneschi, L., Castelli, M., Silva, S.: A survey of semantic methods in genetic programming. Genet. Program Evolvable Mach. 15(2), 195–214 (2014)

    Article  Google Scholar 

  47. Dorigo, M., Blum, C.: Ant colony optimization theory: a survey. Theor. Comput. Sci. 344(2–3), 243–278 (2005)

    Article  MathSciNet  Google Scholar 

  48. Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. J. 8(1), 687–697 (2008)

    Article  Google Scholar 

  49. Yang, X.-S.: A new metaheuristic bat-inspired algorithm. Stud. Comput. Intell. 284, 65–74 (2010)

    MATH  Google Scholar 

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

    Article  Google Scholar 

  51. Yang, X.S., Deb, S.: Cuckoo search via Lévy flights. In: 2009 World Congress on Nature and Biologically Inspired Computing, NABIC 2009—Proceedings, pp. 210–214 (2009)

    Google Scholar 

  52. Yang, X.S.: Flower Pollination Algorithm for Global Optimization. Lecture Notes in Computer Science, vol. 7445 LNCS, pp. 240–249, 2012

    Google Scholar 

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

    Article  Google Scholar 

  54. Gandomi, A.H., Alavi, A.H.: Krill herd: a new bio-inspired optimization algorithm. Commun. Nonlinear Sci. Numer. Simul. 17(12), 4831–4845 (2012)

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  56. Kennedy, J., Eberhart, R.: Particle swarm optimization. IEEE Int. Conf. Neural Networks 4, 1942–1948 (1995)

    Google Scholar 

  57. Kirkpatrick, S., Gelatt, C.D., Vecch, M.P.: Optimization by simulated annealing. Science (80-.) 220(4598), 671–680 (2007)

    Google Scholar 

  58. Siddique, N., Adeli, H.: Simulated annealing, its variants and engineering applications. Int. J. Artif. Intell. Tools 25(06), 1630001 (2016)

    Article  Google Scholar 

  59. Mirjalili, S.: SCA: A Sine Cosine Algorithm for solving optimization problems. Knowl. Based Syst. 96, 120–133 (2016)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CUCEI, Universidad de Guadalajara, Guadalajara, Mexico

    Erik Cuevas

  2. CUCEI, Universidad de Guadalajara, Guadalajara, Mexico

    Fernando Fausto

  3. CUCEI, Universidad de Guadalajara, Guadalajara, Mexico

    Adrián González

Authors
  1. Erik Cuevas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fernando Fausto
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Adrián González
    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

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Cuevas, E., Fausto, F., González, A. (2020). An Introduction to Nature-Inspired Metaheuristics and Swarm Methods. In: New Advancements in Swarm Algorithms: Operators and Applications. Intelligent Systems Reference Library, vol 160. Springer, Cham. https://doi.org/10.1007/978-3-030-16339-6_1

Download citation

Publish with us

Policies and ethics

Search

Navigation