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An Introduction to Nature-Inspired Metaheuristics and Swarm Methods

  • Erik CuevasEmail author
  • Fernando Fausto
  • Adrián González
Chapter
Part of the Intelligent Systems Reference Library book series (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.

References

  1. 1.
    Galinier, P., Hamiez, J.P., Hao, J.K., Porumbel, D.: Handbook of Optimization, vol. 38 (2013)Google Scholar
  2. 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. 3.
    Cavazzuti, M.: Optimization Methods: From Theory to Design (2013)Google Scholar
  4. 4.
    Lin, M., Tsai, J., Yu, C.: A review of deterministic optimization methods in engineering and management. Math. Probl. Eng. 2012, 1–15 (2012)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Schneider, J.J., Kirkpatrick, S.: Stochastic optimization (2006)Google Scholar
  6. 6.
    Cuevas, E., Osuna, V., Oliva, D.: Evolutionary Computation Techniques: A Comparative Perspective, vol. 686 (2017)Google Scholar
  7. 7.
    Díaz-Cortés, M.-A., Cuevas, E., Rojas, R.: Engineering Applications of Soft Computing (2017)Google Scholar
  8. 8.
    Yang, X.: Nature-Inspired Metaheuristic Algorithms, 2nd edn. Luniver Press, Beckington, UK (2008)Google Scholar
  9. 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. 10.
    Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)CrossRefGoogle Scholar
  11. 11.
    Mitchell, M.: Genetic algorithms: an overview. Complexity 1(1), 31–39 (1995)CrossRefGoogle Scholar
  12. 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. 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)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Sette, S., Boullart, L.: Genetic programming: principles and applications. Eng. Appl. Artif. Intell. 14(6), 727–736 (2001)CrossRefGoogle Scholar
  15. 15.
    Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization. Swarm Intell. 1(1), 33–57 (2007)CrossRefGoogle Scholar
  16. 16.
    Dorigo, M., Stützle, T.: Ant Colony Optimization (2004)Google Scholar
  17. 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)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Yang, X.: Firefly algorithm, Lévy flights and global optimization (2010)Google Scholar
  19. 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)CrossRefGoogle Scholar
  20. 20.
    Rutenbar, R.A.: Simulated annealing algorithms: an overview. IEEE Circuits Devices Mag. 5(1), 19–26 (1989)CrossRefGoogle Scholar
  21. 21.
    Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. (Ny) 179(13), 2232–2248 (2009)CrossRefGoogle Scholar
  22. 22.
    Birbil, Ş.I., Fang, S.C.: An electromagnetism-like mechanism for global optimization. J. Glob. Optim. 25(3), 263–282 (2003)MathSciNetCrossRefGoogle Scholar
  23. 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)CrossRefGoogle Scholar
  24. 24.
    Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)CrossRefGoogle Scholar
  25. 25.
    Tan, Y., Zhu, Y.: Fireworks algorithm for optimization. In: First International Conference, ICSI 2010—Proceedings, Part I, 2010, June, pp. 355–364Google Scholar
  26. 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. 4661–4667Google Scholar
  27. 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. 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. 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)CrossRefGoogle Scholar
  30. 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)CrossRefGoogle Scholar
  31. 31.
    Das, S., Mullick, S.S., Suganthan, P.N.: Recent advances in differential evolution—an updated survey. Swarm Evol. Comput. 27, 1–30 (2016)CrossRefGoogle Scholar
  32. 32.
    Piotrowski, A.P.: Review of differential evolution population size. Swarm Evol. Comput. 32, 1–24 (2017)CrossRefGoogle Scholar
  33. 33.
    Bäck, T., Foussette, C., Krause, P.: Contemporary evolution strategies, vol. 47 (2013)Google Scholar
  34. 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. 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. 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. 37.
    Mitchell, M.: An introduction to genetic algorithms. The MIT Press, Cambridge, MA (1996)zbMATHGoogle Scholar
  38. 38.
    Sayed, G.I., Hassanien, A.E., Nassef, T.M.: Genetic and Evolutionary Computing, vol. 536, no. Mci (2017)Google Scholar
  39. 39.
    McCall, J.: Genetic algorithms for modelling and optimisation. J. Comput. Appl. Math. 184(1), 205–222 (2005)MathSciNetCrossRefGoogle Scholar
  40. 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. 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. 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)CrossRefGoogle Scholar
  43. 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. 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. 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. 46.
    Vanneschi, L., Castelli, M., Silva, S.: A survey of semantic methods in genetic programming. Genet. Program Evolvable Mach. 15(2), 195–214 (2014)CrossRefGoogle Scholar
  47. 47.
    Dorigo, M., Blum, C.: Ant colony optimization theory: a survey. Theor. Comput. Sci. 344(2–3), 243–278 (2005)MathSciNetCrossRefGoogle Scholar
  48. 48.
    Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. J. 8(1), 687–697 (2008)CrossRefGoogle Scholar
  49. 49.
    Yang, X.-S.: A new metaheuristic bat-inspired algorithm. Stud. Comput. Intell. 284, 65–74 (2010)zbMATHGoogle Scholar
  50. 50.
    Askarzadeh, A.: A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput. Struct. 169, 1–12 (2016)CrossRefGoogle Scholar
  51. 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. 52.
    Yang, X.S.: Flower Pollination Algorithm for Global Optimization. Lecture Notes in Computer Science, vol. 7445 LNCS, pp. 240–249, 2012Google Scholar
  53. 53.
    Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)CrossRefGoogle Scholar
  54. 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)MathSciNetCrossRefGoogle Scholar
  55. 55.
    Mirjalili, S.: Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl. Based Syst. 89, 228–249 (2015)CrossRefGoogle Scholar
  56. 56.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. IEEE Int. Conf. Neural Networks 4, 1942–1948 (1995)Google Scholar
  57. 57.
    Kirkpatrick, S., Gelatt, C.D., Vecch, M.P.: Optimization by simulated annealing. Science (80-.) 220(4598), 671–680 (2007)Google Scholar
  58. 58.
    Siddique, N., Adeli, H.: Simulated annealing, its variants and engineering applications. Int. J. Artif. Intell. Tools 25(06), 1630001 (2016)CrossRefGoogle Scholar
  59. 59.
    Mirjalili, S.: SCA: A Sine Cosine Algorithm for solving optimization problems. Knowl. Based Syst. 96, 120–133 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Erik Cuevas
    • 1
    Email author
  • Fernando Fausto
    • 2
  • Adrián González
    • 3
  1. 1.CUCEI, Universidad de GuadalajaraGuadalajaraMexico
  2. 2.CUCEI, Universidad de GuadalajaraGuadalajaraMexico
  3. 3.CUCEI, Universidad de GuadalajaraGuadalajaraMexico

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