Imperialist competitive algorithm for natural crack shape reconstruction from ECT signals

Author

Siquan Zhang ; Yu Liu ; Chang Yin ; Chuan Qi

Author_Institution

Dept. of Electr. & Autom., Shanghai Maritime Univ., Shanghai, China

fYear

2014

Firstpage

4921

Lastpage

4926

Abstract

This paper presents a method to solve the problem of natural crack shape reconstruction from eddy current testing signals by means of imperialist competitive algorithm (ICA). ICA is a new meta-heuristic optimization and stochastic search strategy which is inspired from socio-political phenomenon of imperialistic competition. In order to evaluate the efficiency on solving crack shape inversion problem, ICA is compared with some heuristic algorithms such as genetic algorithm, particles swarm optimization and ant colony optimization. The reconstructed results verified the efficiency of neural network based forward model and the promising of imperialist competitive algorithm in crack shape inversion.

Keywords

ant colony optimisation; crack detection; eddy current testing; genetic algorithms; image reconstruction; neural nets; particle swarm optimisation; shape recognition; stochastic processes; structural engineering computing; ECT signals; ant colony optimization; crack shape inversion problem; eddy current testing signals; genetic algorithm; heuristic algorithms; imperialist competitive algorithm; imperialistic competition; meta-heuristic optimization; natural crack shape reconstruction; neural network based forward model; particles swarm optimization; socio-political phenomenon; stochastic search strategy; Cost function; Eddy current testing; Genetic algorithms; Neural networks; Noise; Shape; Artificial neural network; Crack shape reconstruction; Eddy current testing; Imperialist Competitive Algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation (WCICA), 2014 11th World Congress on

Type

conf

DOI

10.1109/WCICA.2014.7053548

Filename

7053548