New Optimization Algorithm in Corrosion Analysis

Environmental aggressive processes can be distinct in three fundamentals phenomenon: stress corrosion, hydrogen brittle-damage, fatigue corrosion. Aggressive elements’ presence reduces structural elements’ strength, since it firstly changes the surface morphology and then crack evolves. Finally, corrosive means produce a fasting crack propagation. In this paper we investigate a metallic plate, with corrosion presence, by eddy current method. The inspection method exploits impedance measurements to determine the material loss profile caused by corrosion. We propose the corrosion stage as an impedance function and carry out computational development by Particle Swarm Optimization (PSO) algorithm. PSO is a population based stochastic optimization technique inspired by social behaviour of bird flocking or fish schooling. PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. The chance of tuning a few PSO’s parameters is another reason making the PSO attractive. A particular version, with slight variations, works well in a wide variety of applications, such as the proposed one.