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A New Thermodynamic Equilibrium-Based Metaheuristic

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Cybernetics Approaches in Intelligent Systems (CoMeSySo 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 661))

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Abstract

In this work, a new optimization method inspired on the Thermodynamic Equilibrium is described to address nonlinear problems in continuous domains. In our proposal, each decision variable is treated as the most volatile chemical component of a saturated binary liquid mixture at a determined pressure and temperature. The optimization procedure is started with an initial solution randomly generated. The search is done by changing the equilibrium state of each mixture. The search is carried out by accepting worse solutions to avoid being left trapped in local optimums. The search includes the random change of the mixtures. The algorithm was tested by using known mathematical functions as benchmark functions showing competitive results in comparison with other metaheuristics.

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References

  1. Blum, C., Blesa Aguilera, M.J., Roli, A., Sampels, M. (eds.): Hybrid Metaheuristics, An Emerging Approach to Optimization. Studies in Computational Intelligence, vol. 114. Springer, Heidelberg (2008)

    MATH  Google Scholar 

  2. Crawford, B., Soto, R., Astorga, G., García, J., Castro, C., Paredes, F.: Putting continuous metaheuristics to work in binary search spaces. Complexity 2017 (2017)

    Google Scholar 

  3. Crawford, B., Soto, R., Legüe, I.F., Olguín, E.: A binary invasive weed optimization algorithm for the set covering problem. In: Silhavy et al. [17], pp. 459–468

    Google Scholar 

  4. Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B 26(1), 29–41 (1996)

    Article  Google Scholar 

  5. Fox, B., Xiang, W., Lee, H.P.: Industrial applications of the ant colony optimization algorithm. Int. J. Adv. Manuf. Technol. 31(7), 805–814 (2007)

    Google Scholar 

  6. Glover, F., Laguna, M.: General purpose heuristics for integer programming - part I. J. Heuristics 2(4), 343–358 (1997)

    Article  MATH  Google Scholar 

  7. Glover, F., Laguna, M.: General purpose heuristics for integer programming-part II. J. Heuristics 3(2), 161–179 (1997)

    Article  MATH  Google Scholar 

  8. Guo, Y.W., Li, W.D., Mileham, A.R., Owen, G.W.: Applications of particle swarm optimisation in integrated process planning and scheduling. Rob. Comput. Integr. Manuf. 25(2), 280–288 (2009)

    Article  MATH  Google Scholar 

  9. Hatamlou, A.: Black hole: a new heuristic optimization approach for data clustering. Inf. Sci. 222, 175–184 (2013)

    Article  MathSciNet  Google Scholar 

  10. Smith, J.M., Van Ness, H., Abbott, M.: Introduction to Chemical Engineering Thermodynamics. The McGraw-Hill Companies Inc., New York (2005)

    Google Scholar 

  11. Karaboga, D.: Artificial bee colony algorithm. Scholarpedia 5(3), 6915 (2010)

    Article  Google Scholar 

  12. Kirkpatrick, S., Gelatt Jr., D., Vecchi, M.P.: Optimization by simmulated annealing. Science 220(4598), 671–680 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  13. Lanza-Gutierrez, J.M., Crawford, B., Soto, R., Berrios, N., Gomez-Pulido, J.A., Paredes, F.: Analyzing the effects of binarization techniques when solving the set covering problem through swarm optimization. Expert Syst. Appl. 70, 67–82 (2017)

    Article  Google Scholar 

  14. Mladenovic, N., Hansen, P.: Variable neighborhood search. Comput. Oper. Res. 24(11), 1097–1100 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  15. Rana, S., Jasola, S., Kumar, R.: A review on particle swarm optimization algorithms and their applications to data clustering. Artif. Intell. Rev. 35(3), 211–222 (2011)

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  17. Silhavy, R., Senkerik, R., Komínková Oplatková, Z., Silhavy, P., Prokopova, Z. (eds.): Artificial Intelligence Perspectives in Intelligent Systems - Proceedings of the 5th Computer Science On-line Conference 2016, CSOC2016, vol. 1. Advances in Intelligent Systems and Computing, vol. 464. Springer (2016)

    Google Scholar 

  18. Soto, R., Crawford, B., Lama, J., Paredes, F.: A firefly algorithm to solve the manufacturing cell design problem. In: Silhavy et al. [17], pp. 103–114

    Google Scholar 

  19. Whitley, D.: An executable model of a simple genetic algorithm. In: Darrell Whitley, L. (eds.) Proceedings of the Second Workshop on Foundations of Genetic Algorithms, Vail, Colorado, USA, 26–29 July 1992, pp. 45–62. Morgan Kaufmann (1992)

    Google Scholar 

  20. Yaghini, M., Akhavan, R.: DIMMA: a design and implementation methodology for metaheuristic algorithms - a perspective from software development. Int. J. Appl. Metaheuristic Comput. 1(4), 57–74 (2010)

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank the grants given as follows: Ph.D. Broderick Crawford is supported by grant CONICYT/FONDECYT/REGULAR/1171243. Ph.D. Ricardo Soto is supported by grant CONICYT/FONDECYT/REGULAR/1160455. MSc. Enrique Cortés and MSc. Gino Astorga are supported by grant INF-PUCV 2015.

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Authors and Affiliations

  1. Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2950, Valparaíso, 2374631, Chile

    Broderick Crawford, Ricardo Soto, Enrique Cortés & Gino Astorga

  2. Universidad de Playa Ancha, Leopoldo Carvallo 270, Valparaíso, 2340000, Chile

    Enrique Cortés

  3. Universidad de Valparaíso, Prat 856, Valparaíso, 2361864, Chile

    Gino Astorga

Authors
  1. Broderick Crawford
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  2. Ricardo Soto
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  3. Enrique Cortés
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  4. Gino Astorga
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Corresponding author

Correspondence to Enrique Cortés .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Radek Silhavy

  2. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Petr Silhavy

  3. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Zdenka Prokopova

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Crawford, B., Soto, R., Cortés, E., Astorga, G. (2018). A New Thermodynamic Equilibrium-Based Metaheuristic. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Cybernetics Approaches in Intelligent Systems. CoMeSySo 2017. Advances in Intelligent Systems and Computing, vol 661. Springer, Cham. https://doi.org/10.1007/978-3-319-67618-0_31

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  • DOI: https://doi.org/10.1007/978-3-319-67618-0_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67617-3

  • Online ISBN: 978-3-319-67618-0

  • eBook Packages: EngineeringEngineering (R0)

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