Advertisement

Galactic Swarm Optimization with Adaptation of Parameters Using Fuzzy Logic for the Optimization of Mathematical Functions

  • Emer Bernal
  • Oscar CastilloEmail author
  • José Soria
  • Fevrier Valdez
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 749)

Abstract

In this paper the Galactic Swarm Optimization (GSO) algorithm with the use of fuzzy systems for the adaptation of the parameters in the GSO algorithm is proposed. This algorithm is inspired by the movement of stars, galaxies and superclusters of galaxies under the force of gravity. The GSO algorithm uses multiple cycles of exploration and exploitation phases to achieve a balance between exploring new solutions and exploiting existing solutions. In this work different fuzzy systems were designed for the dynamic adaptation of the c3 and c4 parameters to measure the operation of the algorithm with 7 mathematical functions with different number of dimensions. A statistical comparison was made between the different variants to test the performance of the method applied to optimization problems.

Keywords

Galactic swarm optimization GSO Fuzzy system Adaptation of parameters Mathematical function 

Notes

Acknowledgements

We would like to express our gratitude to the CONACYT and Tijuana Institute of Technology for the facilities and resources granted for the development of this research.

References

  1. 1.
    E. Atashpaz-Gargari, F. Hashemzadeh, R. Rajabioun, C. Lucas, Colonial competitive algorithm: a novel approach for PID controller design in MIMO distillation column process. Int. J. Intell. Comput. Cybern. 1, 337–355 (2008)Google Scholar
  2. 2.
    E. Bernal, O. Castillo, J. Soria, Imperialist competitive algorithm applied to the optimization of mathematical functions: a parameter variation study, in Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization, vol. 601 (Springer International Publishing, 2015), pp. 219–232Google Scholar
  3. 3.
    E. Bernal, O. Castillo, J. Soria, F. Valdez, Imperialist competitive algorithm with dynamic parameter adaptation using fuzzy logic applied to the optimization of mathematical functions. Algorithms 10(1), 18 (2017a)Google Scholar
  4. 4.
    E. Bernal, O. Castillo, J. Soria, A fuzzy logic approach for dynamic adaptation of parameters in galactic swarm optimization, in Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS), IEEE (2017b)Google Scholar
  5. 5.
    E. Bernal, O. Castillo, J. Soria, Fuzzy logic for dynamic adaptation in the imperialist competitive algorithm, in IEEE Symposium Series on Computational Intelligence (SSCI), IEEE (2017c)Google Scholar
  6. 6.
    J. Cepeda-Negrete, R.E. Sanchez-Yanez, Automatic selection of color constancy algorithms for dark image enhancement by fuzzy rule-based reasoning. Appl. Soft Comput. 28, 1–10 (2015)CrossRefGoogle Scholar
  7. 7.
    A.P. Engelbrecht, Computational intelligence (Wiley, Pretoria, South Africa, 2007)CrossRefGoogle Scholar
  8. 8.
    A.R. Hedar, Test functions for unconstrained global optimization [online], Egypt, Assiut University. Available: http://www-optima.amp.i.kyoto-u.ac.jp/member/student/hedar/Hedar_files/TestGO.htm
  9. 9.
    B.S. Khehra, A.P.S. Pharwaha, M. Kaushal, Fuzzy 2-partition entropy threshold selection based on Big Bang-Big Crunch Optimization algorithm. Egypt. Inf. J. 16(1), 133–150 (2015)CrossRefGoogle Scholar
  10. 10.
    M.J. Mahmoodabadi, H. Jahanshahi, Multi-objective optimized fuzzy-PID controllers for fourth order nonlinear systems. Eng. Sci. Technol. Int. J. 18, 1084–1098 (2016)CrossRefGoogle Scholar
  11. 11.
    P. Melin, F. Olivas, O. Castillo, F. Valdez, J. Soria, M. Valdez, Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic. Expert Syst. Appl. 40(8), 3196–3206 (2013)CrossRefGoogle Scholar
  12. 12.
    V. Muthiah-Nakarajan, M.M. Noel, Galactic swarm optimization: a new global optimization metaheuristic inspired by galactic motion. Appl. Soft Comput. 38, 771–787 (2016)CrossRefGoogle Scholar
  13. 13.
    A. Sombra, F. Valdez, P. Melin, O. Castillo, A new gravitational search algorithm using fuzzy logic to parameter adaptation, in IEEE Congress on Evolutionary Computation, Cancun, México (2013), pp. 1068–1074Google Scholar
  14. 14.
    F. Valdez, P. Melin, O. Castillo, Evolutionary method combining particle swarm optimization and genetic algorithms using fuzzy logic for decision making, in IEEE International Conference on Fuzzy Systems (2009), pp. 2114–2119Google Scholar
  15. 15.
    F. Valdez, P. Melin, O. Castillo, An improved evolutionary method with fuzzy logic for combining particle swarm optimization and genetic algorithms. Appl. Soft Comput. 11(2), 2625–2632 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Emer Bernal
    • 1
  • Oscar Castillo
    • 1
    Email author
  • José Soria
    • 1
  • Fevrier Valdez
    • 1
  1. 1.Tijuana Institute of TechnologyTijuanaMexico

Personalised recommendations