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.

In this paper a discrete Big Bang-Big Crunch algorithm is applied to optimal design of reinforced concrete planar frames under

the gravity and lateral loads. Optimization is based on ACI 318-08 code. Columns are assumed to resist axial loads and bending

moments, while beams resist only bending moments. Second-order effects are also considered for the compression members, and

columns are checked for their slenderness and their end moments are magnified when necessary. The main aim of the BB-BC

process is to minimize the cost of material and construction of the reinforced concrete frames under the applied loads such that

the strength requirements of the ACI 318 code are fulfilled. In the process of optimization, the cost per unit length of the sections

is used for the formation of the subsequent generation. Three bending frames are optimized using BB-BC and the results are

compared to those of the genetic algorithm.