Comparison of ABC, CPSO, DE and GA algorithms in FRF based structural damage identification
Date
2013
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Walter De Gruyter
Abstract
In this contribution, performances of well-known population based algorithms, the artificial bee colony (ABC), contemporary particle swarm optimization (CPSO), genetic algorithm (GA), and differential evolution (DE) are compared in a basic model for damage identification (DI). DI is modeled as an inverse problem with the objective function based on the difference of the frequency response functions (FRF) computed by the finite element model of the structure and the reference data measured from damaged structure. Damage parameters are determined solving the problem with the aforementioned algorithms. It was observed that DE is the best one of a given number of function evaluations and gives the most accurate results in spite of noise interference to the reference data. According to the relevant literature, this is the first study including a comparison of these algorithms in an FRF based DI study.
Description
Keywords
Materials science, Particle swarm, Differential evolution, Crack detection, Frequency, Damage detection, Finite element method, Frequency response, Genetic algorithms, Inverse problems, Optimization, Particle swarm optimization (PSO), Structural analysis, Artificial bee colonies (ABC), Damage identification, Differential evolution, Frequency response functions, Noise interference, Objective functions, Population-based algorithm, Structural damage identification, Evolutionary algorithms
Citation
Gökdağ, H. (2013). “Comparison of ABC, CPSO, DE and GA algorithms in FRF based structural damage identification”. Materials Testing, 55(10), 796-802.