Multi-objective optimal design of reinforced concrete frames using two meta-heuristic algorithms

Abstract

Genetic algorithm (GA) and differential evolution (DE) are meta-heuristic algorithms which have shown a favourable performance in the optimization of the complex problems. In recent years, only GA has been widely used for single-objective optimal design of reinforced concrete (RC) structures; however, it has been applied for multi-objective optimization of steel structures. In this article, the total structural cost and the roof displacement are considered as objective functions for optimal design of RC frames. Using weighted sum method (WSM) approach, the two-objective optimization problem is converted to a single-objective optimization problem. The size of the beams and columns are considered as design variables and the design requirements of the ACI-318 are employed as constraints. Five numerical models are investigated to test the efficiency of the GA and DE algorithms. Pareto front curves are obtained for the building models using both algorithms. The detailed results show the accuracy and convergence speed of the algorithms.