Precision in High Dimensional Optimisation of Global Tasks with Unknown Solutions

  • Kalin PenevEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11958)


High dimensional optimisation is a challenge for most of the available search methods. Resolving global and constrained task seems to be even harder and exploration of tasks with unknown solutions can be seen very rare in the literature and requires more research efforts. This article analyses optimisation of high dimensional global, including constrained, tasks with unknown solutions. Reviewed and analysed are experimental results precision, possibilities for trapping in local sub-optima and adaptation to unknown search spaces.


Free Search Multidimensional global optimisation 


  1. 1.
    Abiyev, R.H., Tunay, M.: Optimization of high-dimensional functions through hypercube evaluation. Comput. Intell. Neurosci. 2015, 967320 (2015)Google Scholar
  2. 2.
    Ackley, D.H.: A Connectionist Machine for Genetic Hillclimbing. Kluwer, Boston (1987)CrossRefGoogle Scholar
  3. 3.
    Brekke E.F.: Complex Behaviour in Dynamical Systems. The Norwegian University of Science and Technology, pp. 37–38 (2004). Accessed 29 May 2014
  4. 4.
    Cano, A., Garcia-Martinez, C., Ventura, S.: Extremely high-dimensional optimization with MapReduce: scaling functions and algorithm. Inf. Sci. 415–416, 110–127 (2017)CrossRefGoogle Scholar
  5. 5.
    Caraffini, F., Neri, F., Iacca, G.: Large scale problems in practice: the effect of dimensionality on the interaction among variables. In: Squillero, G., Sim, K. (eds.) EvoApplications 2017, Part I. LNCS, vol. 10199, pp. 636–652. Springer, Cham (2017). Scholar
  6. 6.
    Cao, B., et al.: Distributed parallel particle swarm optimization for multi-objective and many-objective large-scale optimization. IEEE Access 5, 8214–8221 (2017)CrossRefGoogle Scholar
  7. 7.
    Chu, X., Hu, M., Wu, T., Weir, J., Lu, Q.: AHPS2: an optimizer using adaptive heterogeneous particle swarms. Inf. Sci. 280, 26–52 (2014). Scholar
  8. 8.
    Grosan, C., Abraham, A., Hassinen, A.: A line search approach for high dimensional function optimization. Telecommun. Syst. 46(3), 217–243 (2011)CrossRefGoogle Scholar
  9. 9.
    De Jung K.A.: An Analysis of the Behaviour of a Class of Genetic Adaptive Systems, Ph.D. thesis, University of Michigan (1975)Google Scholar
  10. 10.
    Griewank, A.O.: Generalized decent for global optimization. J. Optim. Theory Appl. 34, 11–39 (1981)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Kotsialos, A.: Nonlinear optimisation using directional step lengths based on RPROP. Optim. Lett. 8(3), 1401–1415 (2014)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Liang, J., Baskar, S., Suganthan, P., Qin, A.: Performance evaluation of multiagent genetic algorithm. Nat. Comput. 5(1), 83–96 (2006)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Macnish, C., Yao, X.: Direction Matters in High-Dimensional Optimisation, pp. 2372–2379. IEEE ( 2008)Google Scholar
  14. 14.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (1992). Scholar
  15. 15.
    Montes De Oca, M., Aydin, D., Stutzle, T.: An incremental particle swarm for large-scale continuous optimization problems: an example of tuning-in-the-loop (re)design of optimization algorithms. Soft Comput. 15(11), 2233–2255 (2011)CrossRefGoogle Scholar
  16. 16.
    Mühlenbein, H., Schomisch, D., Born, J.: The parallel genetic algorithm as function optimizer. Parallel Comput. 17, 619–632 (1991)CrossRefGoogle Scholar
  17. 17.
    Nesmachnow, S.: An overview of metaheuristics: accurate and efficient methods for optimisation. Int. J. Metaheuristics 3(4), 320–347 (2014)CrossRefGoogle Scholar
  18. 18.
    Penev, K.: Free search - comparative analysis 100. Int. J. Metaheuristics (IJMHEUR) 3(2), 118–132 (2014)CrossRefGoogle Scholar
  19. 19.
    Rosenbrock, H.H.: An automate method for finding the greatest or least value of a function. Comput. J. 3(1960), 175–184 (1960)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Yin, J., Wang, Y., Hu, J.: Free search with adaptive differential evolution exploitation and quantum-inspired exploration. J. Netw. Comput. Appl. 35(3), 1035–1051 (2012)CrossRefGoogle Scholar
  21. 21.
    Yang, Z., Tang, K., Yao, X.: Differential Evolution for High-Dimensional Function Optimization, pp. 3523–3530. IEEE (2007)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School Media Arts and TechnologySolent UniversitySouthamptonUK

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