Using Genetic Algorithms for Optimal Design of Axially Loaded Non-Prismatic Columns

Abstract

This paper presents a method for optimizing the design of axially loaded non-prismatic columns using genetic algorithms (GAs). The design problem was formulated as an optimization problem in which the objective function is to minimize the volume of a column under a given load by changing its shape, subject to both buckling and strength constraints. Both floating point representation and binary representation (with and without Gray coding) were used and compared against a mathematical programming method based on the generalized reduced gradient method. Our results show that the floating point representation scheme provides the best solutions, both in terms of precision and in terms of computing time. This problem is of great interest in engineering, since considerable savings can be achieved due to the effective use of material through the optimal shape of a column, mainly for mass production.