Sizing and layout optimization of truss structures with artificial bee colony algorithm

In this paper, a swarm intelligence-based optimization technique called Artificial Bee Colony algorithm (ABC) is employed in combined optimization of truss structures. The objective is to optimize the layout and members size of truss structures with displacement, stress and buckling constraints. The ABC is based on simulating the intelligent foraging behavior of honey bees. The nodal coordinates of the joints and the cross-sectional areas of the members for the truss structure system are the design variables of shape and size optimization, respectively. Allowable stress, Euler buckling stress, and displacement are considered as the problem constraints. The efficiency of the ABC is tested in four benchmark structural optimization problems. The results clearly reveal the superiority of ABC over other algorithms in terms of optimized weight, standard deviation and number of structural analyses. The ABC demonstrates a robust performance with a 100% success rate.

Automated summary

In this study, the ABC algorithm is utilized for combined optimization of truss structures, optimizing layout and member size while considering stress and buckling constraints. The results show the superiority of ABC over other algorithms in terms of optimized weight and structural analyses, with a robust performance and 100% success rate demonstrated across four benchmark optimization problems.