A comparative study of two meta-heuristic algorithms for optimum design of reinforced concrete frames

This article presents the application of two algorithms: heuristic big bang-big crunch (hbb-bc) and a heuristic particle swarm ant colony optimization (hpsaco) to discrete optimization of reinforced concrete planar frames subject to combinations of gravity and lateral loads based on aci 318-08 code. the objective function is the total cost of the frame which includes the cost of concrete, formwork and reinforcing steel for all members of the frame. the heuristic big bang-big crunch (hbb-bc) is based on bb-bc and a harmony search (hs) scheme to deal with the variable constraints. the hpsaco algorithm is a combination of particle swarm with passive congregation (psopc), ant colony optimization (aco), and harmony search scheme (hs) algorithms. in this paper, by using the capacity of bb-bc in aco stage of hpsaco, its performance is improved. some design examples are tested using these methods and the results are compared