0
  • DE
  • EN
  • FR
  • Base de données et galerie internationale d'ouvrages d'art et du génie civil

Publicité

Two Strategies to Improve the Differential Evolution Algorithm for Optimizing Design of Truss Structures

Auteur(s):


Médium: article de revue
Langue(s): anglais
Publié dans: Advances in Civil Engineering, , v. 2020
Page(s): 1-20
DOI: 10.1155/2020/8741862
Abstrait:

The performance of differential evolution (DE) mostly depends on mutation operator. Inappropriate configurations of mutation strategies and control parameters can cause stagnation due to over exploration or premature convergence due to over exploitation. Balancing exploration and exploitation is crucial for an effective DE algorithm. This work presents an enhanced DE (EDE) for truss design that utilizes two new strategies, namely,integrated mutationandadaptive mutation factorstrategies, to obtain a good balance between the exploration and exploitation of DE. Three mutation strategies (DE/rand/1,DE/best/2, andDE/rand-to-best/1) are combined in theintegrated mutationstrategy to increase the diversity of random search and avoid premature convergence to a local minimum. Theadaptive mutation factorstrategy systematically adapts the mutation factor from a large value to a small value to avoid premature convergence in the early searching period and to increase convergence to the global optimum solution in the later searching period. The outstanding performance of the proposed EDE is demonstrated through optimization of five truss structures.

Copyright: © 2020 Ching-Yun Kao et al.
License:

Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original.

  • Informations
    sur cette fiche
  • Reference-ID
    10427935
  • Publié(e) le:
    30.07.2020
  • Modifié(e) le:
    02.06.2021