Optimization of S-N curve fitting based on neighborhood rough set reduction with improved firefly algorithm

Abstract

In order to reduce the S-N curve dispersion of titanium alloy welded joints and improve the prediction accuracy of fatigue life, a novel optimization method of S-N curve fitting based on neighborhood rough set reduction with improved firefly algorithm (IFANRSR) is proposed. Firstly, we propose an improved firefly algorithm (IFA) by updating the position and step size, combining IFA algorithm and neighborhood rough set into an IFANRSR algorithm for attribute reduction. Then, according to the fatigue data of titanium alloy welded joints, the fatigue decision system of welded joints is established, and the key factors affecting the fatigue life of welded joints are determined. Next, according to the set of key influencing factors obtained based on IFANRSR algorithm, the fatigue characteristics domains are divided, and the S-N curves are fitted on each fatigue characteristics domain, to obtain a group of S-N curves. To demonstrate the effectiveness of IFA algorithm, six benchmark functions are used, then the availability of IFANRSR algorithm is evaluated in comparison with other algorithms on four UCI datasets. Finally, the results of the goodness-of-fit show that the dispersion of fatigue data is reduced, which can effectively improve the prediction accuracy of fatigue life.

Keywords

• s-n curve
• fatigue characteristics domain
• neighborhood rough set
• firefly algorithm