Unconscious Search - A New Structured Search Algorithm for Solving Continuous Engineering Optimization Problems Based on the Theory of Psychoanalysis

  • Ehsan Ardjmand
  • Mohammad Reza Amin-Naseri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7331)


Many metaheuristic methods are based on the ability of systems in Nature to optimize on aspects of their performance. One such system is the human brain with its capacity for optimizing towards a general state of mental balance. The Theory of Psychoanalysis propounded by Sigmund Freud is generally recognized as an account of the mechanisms involved in psychological processes. It is possible to draw an analogy between the practice of psychoanalysis and the treatment of optimization problems. The proposed new Unconscious Search (US) method shares in some features with the procedure attempted in psychoanalysis to elicit the suppressed contents of the subject’s mind. One bounded and several unbounded benchmark problems have been solved using the proposed algorithm; the results were satisfactory when compared against earlier results obtained using other known methods.


Unconscious Search Psychoanalysis Metaheuristic Optimization 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Glover, F., Kochenberger, G.A.: Handbook of Metaheuristics. Kluwer Academic Publishers, USA (2003)zbMATHGoogle Scholar
  2. 2.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
  3. 3.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, MA (1989)zbMATHGoogle Scholar
  4. 4.
    Kirkpatrick, S., Gelatt, C., Vecchi, M.: Optimization by simulated annealing. Science 220, 671–680 (1983)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Glover, F.: Tabu Search - Part I. ORSA Journal on Computing I(3) (1989)Google Scholar
  6. 6.
    Glover, F.: Tabu Search - Part II. ORSA Journal on Computing II(3) (1989)Google Scholar
  7. 7.
    Dorigo, M.: Optimization, learning and natural algorithms. Dipartimento di Elettronica, Politecnico di Milano, Milan (1992) (in Italian)Google Scholar
  8. 8.
    Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B 26(1), 29–41 (1996)CrossRefGoogle Scholar
  9. 9.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE Service Center, NJ (1995)CrossRefGoogle Scholar
  10. 10.
    Glover, F.: Tabu Search-Uncharted Domains. Annals of Operations Research 149, 89–98 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Mijolla, A.: International Dictionary of Psychoanalysis. In: Mijolla, A. (ed. in chief), pp. 1362–1366. Thomson Gale, USA (2005)Google Scholar
  12. 12.
    Cahn, R.: International Dictionary of Psychoanalysis. In: Mijolla, A. (ed. in chief), pp. 333–334. Thomson Gale, USA (2005)Google Scholar
  13. 13.
    Assoun, P.L.: Le Vocabulaire de freud. Ellipses, France (2002)Google Scholar
  14. 14.
    Freud, S.: The Interpretation of Dreams. In: Strachey, J. (ed.), 3rd (Revised) English edn., New York (2010)Google Scholar
  15. 15.
    Lee, K.S., Geem, Z.W.: A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Computers Methods in Applied Mechanics and Engineering 194, 3902–3933 (2005)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ehsan Ardjmand
    • 1
  • Mohammad Reza Amin-Naseri
    • 1
  1. 1.Department of Industrial EngineeringTarbiat Modares UniversityTehranIran

Personalised recommendations