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A New Meta-heuristic Optimization Algorithm Based on the String Theory Paradigm from Physics

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSINTELL))

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Abstract

In this section, we describe the literature review that was used in this book in order to have basic concepts and information about computational intelligence, bioinspired algorithms and different techniques that the researchers use in optimization problems.

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References

  1. K. Srensen, M. Sevaux, F. Glover, A history of metaheuristics, in ORBEL29–29th Belgian conference on operations research (2017)

    Google Scholar 

  2. D.H. Wolpert, W.G. Macready, No free lunch theorems for optimization. IEEE Trans. Evolut. Comput. 67–82 (1997)

    Google Scholar 

  3. H. Maier, Z. Kapelan, Evolutionary algorithms and other metaheuritics in water resources: current status, research challenges and future directions. Environ. Model. Softw. 62, 271–299 (2014)

    Article  Google Scholar 

  4. F. Aladwan, M. Alshraideh, M. Rasol, A genetic algorithm approach for breaking of simplified data encryption standard. Int. J. Secur. Its Appl. 9(9), 295–304 (2015)

    Google Scholar 

  5. Lingaraj and Haldurai, A study on genetic algorithms and its applications. Int. J. Comput. Sci. Eng. 4, 139–143 (2016)

    Google Scholar 

  6. X. Yao, Y. Liu, G. Lin, Evolutionary programming made faster. IEEE Trans. Evolut. Comput. 3, 82–102 (1999)

    Google Scholar 

  7. U. Can, B. Alatas, Physics based metaheuristic algorithms for global optimization. Am. J. Inform. Sci. Comput. Eng. 1, 94–106 (2015)

    Google Scholar 

  8. K. Osman, E. Ibrahim, A new optimization method: big bang-big crunch. Adv. Eng. Softw. 37, 106–111 (2006)

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. B. Alatas, ACROA: artificial chemical reaction optimization algorithm for global optimization. Exp. Syst. Appl. 38, 13170–13180 (2011)

    Article  Google Scholar 

  11. G. Beni, J. Wang, Swarm Intelligence in Cellular Robotic Systems, Robots and Biological Systems: Towards a New Bionics? (Springer, Berlin, 1993), pp. 703–712

    Google Scholar 

  12. X.-S. Yang, M. Karamanoglu, Swarm intelligence and bio-inspired computation: an overview, in Swarm Intelligence and Bio-inspired Computation (2013), pp. 3–23

    Google Scholar 

  13. J. Kennedy, R.C. Eberhart, Particle swarm optimization, in Proceedings of the IEEE International Joint Conference on Neural Networks (1995), pp. 1942–1948

    Google Scholar 

  14. M. Dorigo, M. Birattari, T. Stutzle, Ant colony optimization. IEEE. Comput. Intell. Magaz. 28–39 (2006)

    Google Scholar 

  15. X.S. Yang, A New Metaheuristic Bat-Inspired Algorithm (2010)

    Google Scholar 

  16. A. Frases, Simulation of genetic systems by automatic digital computers I: introduction. Austr. J. Biol. Sci. 10, 484–491 (1957)

    Article  Google Scholar 

  17. A. Frases, Simulation of genetic systems by automatic digital computers II: effects of linkage on rates of advance under selection. Austr. J. Biol. Sci. 10, 492–499 (1957)

    Article  Google Scholar 

  18. H.J. Bremermann, Optimization through evolution and recombination, in Self-organization Systems, ed. by M.C. Yovits, G.T. Jacobi, G.D. Goldstine (1962), pp 93–106

    Google Scholar 

  19. J. Reed, R. Toombs, N.A. Barricelli, Simulation of biological evolution and machine learning. J. Theor. Biol. 17, 319–342 (1967)

    Article  Google Scholar 

  20. J.H. Holland, Adaptation in Natural and Artificial Systems (University of Michigan Press, Ann Arbor, 1975)

    Google Scholar 

  21. A. Thengade, R. Donal, Genetic algorithm—survey paper, in IJCA Proceedings, National Conference on Recent Trends in Computing, NCRTC, vol. 5 (2012)

    Google Scholar 

  22. S. Mirjalili, M. Mirjalili, A. Lewis, Grey Wolf Optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

    Article  Google Scholar 

  23. C. Muro, R. Escobedo, L. Spector, R. Coppinger, Wolf-pack (Canis lupus) hunting strategies emerge from simple rules. Comput. Simul. Behav. Process. 88, 192–197 (2011)

    Article  Google Scholar 

  24. L. Rodriguez, O. Castillo, J. Soria, P. Melin, F. Valdez, C. Gonzalez, G. Martinez, J. Soto, A fuzzy hierarchical operator in the grey wolf optimizer algorithm. Appl. Soft Comput. 57, 315–328 (2017)

    Article  Google Scholar 

  25. X.-S. Yang, Firefly Algorithm: Recent Advances and Applications (2013), arXiv:1308.3898v1

  26. X-S. Yang, Flower Pollination Algorithm for Global Optimization (2012), arXiv:1312.5673v1

  27. L. Zadeh, Fuzzy sets. Inf. Control 8, 338–353 (1965)

    Article  Google Scholar 

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Authors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Luis Rodriguez

Authors
  1. Oscar Castillo
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  2. Luis Rodriguez
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Castillo, O., Rodriguez, L. (2022). Literature Review. In: A New Meta-heuristic Optimization Algorithm Based on the String Theory Paradigm from Physics. SpringerBriefs in Applied Sciences and Technology(). Springer, Cham. https://doi.org/10.1007/978-3-030-82288-0_2

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