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

Ching-Yun Kao, Shih-Lin Hung*, Budy Setiawan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number8741862
Number of pages20
JournalAdvances in Civil Engineering
Volume2020
DOIs
StatePublished - 20 Jul 2020

Keywords

  • PARTICLE SWARM OPTIMIZER
  • GLOBAL OPTIMIZATION

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