Skip to main content
main-content
Top

Hint

Swipe to navigate through the articles of this issue

26-04-2018 | Foundations | Issue 11/2019

Soft Computing 11/2019

Optimization based on nonlinear transformation in decision space

Journal:
Soft Computing > Issue 11/2019
Authors:
Yangyang Li, Cheng Peng, Yang Wang, Licheng Jiao
Important notes
Communicated by A. Di Nola.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Abstract

When dealing with black box optimization problem, the nature of the problem cannot be completely mastered and the position where the optimal solution appears cannot be determined. A sufficient search of all feasible regions is the necessary way to obtain the global optimal solution. As a kind of method based on search with population, evolutionary computing has attracted much attention in the field of optimization. Compared with the traditional search method, the population is expected to expand the search area, but in many instances, the solutions of the evolutionary algorithms cannot explore wide area continuously and effectively. Specifically, after several iterations, the general optimizer focuses the solutions near a small region and output one promising solution. In the limited range, population lose the advantage of searching several regions meanwhile. In this article, a new framework called optimization based on nonlinear transformation in decision space (ONTD) is proposed, in which a problem population is generated by converting a given problem. And each converted problem (subproblem) has its own interesting area with high calculation weight on the decision space. The optimizers combining differential evolution operator and ONTD are instantiated for comparison. And an adaptive ONTD strategy (AONTD) is proposed to adjust high calculation region of each converted problem. Through dealing with the different converted problems at the same time, on the test and trap problems, several optima can be retained with the optimizers based on ONTD. And on the benchmark problems, the optimizers based on ONTD also have competitive performance.

Please log in to get access to this content

To get access to this content you need the following product:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 69.000 Bücher
  • über 500 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 58.000 Bücher
  • über 300 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 50.000 Bücher
  • über 380 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Maschinenbau + Werkstoffe




Testen Sie jetzt 30 Tage kostenlos.

Literature
About this article

Other articles of this Issue 11/2019

Soft Computing 11/2019 Go to the issue

Premium Partner

    Image Credits