A geometry constraint handling technique for stiffener layout optimization problem

Abstract

Beam stiffeners have frequently been used for raising natural frequencies of base structures. In stiffener layout optimization problems, most of the previous researches considering the position and/or the length of the stiffener as design variables dealt with structures having just simple convex shapes such as a square or rectangle. The reason was because concave shape structures have difficulties in formulating geometry constraints. In this paper, a new geometry constraint handling technique, which can define both convex and concave feasible regions and measure a degree of geometry constraint violation, was proposed. Evolution strategies (ESs) was utilized as an optimization tool. In addition, the constraint-handling technique of EVOSLINOC (EVOlution Strategies for scalar optimization with LInear and NOnlinear Constraints) was utilized to solve constrained optimization problems. From numerical examples, the proposed geometry constraint handling technique was verified and proved that the technique can easily be applied to structures in not only convex but also concave shapes, even with a protrusion or interior holes.

Multidiscipline Topology Optimization of Stiffened Plate/Shell Structures Inspired by Growth Mechanisms of Leaf Veins in Nature

“…Ansola et al [5] proposed a global optimality criteria method for the presentation of both the geometry of the shell mid-plane and the layout of surface stiffeners on the shell structure. Other 2 Mathematical Problems in Engineering approaches and contributions can be found in the literature [6][7][8][9][10].…”

Evolutionary constrained optimization using an addition of ranking method and a percentage-based tolerance value adjustment scheme

“…Evolutionary algorithms (EAs) have been widely adopted to solve such optimization problems due to their flexibility and adaptability to the task at hand, in combination with their robust performance and global search characteristics [4]. EAs have found practical applications in many different fields, including medical imaging technology, bioinformatics, traffic control, environmental control, and engineering design problems [3,10,13,26,28,30]. However, EAs are unconstrained optimization procedures and thus require an additional constraint handling algorithm if they are to be useful in real-world applications.…”