GENETIC ALGORITHMS IN TRUSS TOPOLOGICAL OPTIMIZATION

The present paper describes the use of a stochastic search procedure t hat is the basis of genetic algorithms, in developing near-optimal top ologies of load-bearing truss structures. The problem addressed is one wherein the structural geometry is created from a specification of lo ad conditions and available support points in the design space. The de velopment of this geometry must satisfy kinematic stability requiremen ts in addition to the usual requirements of structural strength and st iffness. The approach is an adaptation of the ground structure method of topology optimization, and is implemented in a two-level genetic al gorithm-based search. In this process, the kinematic stability constra ints are imposed at one level, followed by the treatment of response c onstraints at a second level of optimization. Singular value decomposi tion is used to assess the kinematic stability constraints at the firs t level of design, and results in the creation of a finite number of i ncreasing weight, stable topologies. Member sizing is then introduced at a second level of design, where minimal weight and response constra ints on stresses, displacements and buckling are simultaneously consid ered. At this level, the only admissible topologies are those identifi ed during the first stage and any stable combination thereof. The desi gn variable representation scheme allows for both the removal and addi tion of structural members during optimization.