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.