Efficient truss optimization using the contrast-based fruit fly optimization algorithm

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25 Citations (Scopus)

Abstract

A recent biological study shows that the extremely good efficiency of fruit flies in finding food, despite their small brain, emerges by two distinct stimuli: smell and visual contrast. “contrast-based fruit fly optimization”, presented in this paper, is for the first time mimicking this fruit fly behaviour and developing it as a means to efficiently address multi-parameter optimization problems. To assess its performance a study was carried out on ten mathematical and three truss optimization problems. The results are compared to those obtained using twelve state-of-the-art optimization algorithms and confirm its good and robust performance. A sensitivity analysis and an evaluation of its performance under parallel computing were conducted. The proposed algorithm has only a few tuning parameters, is intuitive, and multi-faceted, allowing application to complex n-dimensional design optimization problems.
LanguageEnglish
Pages137-148
Number of pages12
JournalComputers & Structures
Volume182
Issue numberApril
Early online date22 Dec 2016
DOIs
Publication statusPublished - Apr 2017

Fingerprint

Fruit
Fruits
Optimization Algorithm
Optimization Problem
Optimization
Robust Performance
Parameter Tuning
Parameter Optimization
Parallel Computing
Sensitivity Analysis
n-dimensional
Intuitive
Parallel processing systems
Sensitivity analysis
Distinct
Brain
Tuning
Evaluation

Bibliographical note

Due to publisher policy, the full text is not available on the repository until the 22nd of December 2017.

Keywords

  • Fruit fly optimization
  • Multi-parameter
  • Truss optimization

Cite this

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abstract = "A recent biological study shows that the extremely good efficiency of fruit flies in finding food, despite their small brain, emerges by two distinct stimuli: smell and visual contrast. “contrast-based fruit fly optimization”, presented in this paper, is for the first time mimicking this fruit fly behaviour and developing it as a means to efficiently address multi-parameter optimization problems. To assess its performance a study was carried out on ten mathematical and three truss optimization problems. The results are compared to those obtained using twelve state-of-the-art optimization algorithms and confirm its good and robust performance. A sensitivity analysis and an evaluation of its performance under parallel computing were conducted. The proposed algorithm has only a few tuning parameters, is intuitive, and multi-faceted, allowing application to complex n-dimensional design optimization problems.",
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