Multi-Objective Optimization of Reinforced Concrete Frames using Genetic Algorithm and Weighted Sum Method

Over the last decades, optimization of structures has been the subject of extensive investigations and optimization using Genetic Algorithms (GA’s) has been studied intensively. Many researchers have been focused on the weight optimization of buildings mainly on steel structures rather than reinforced concrete (RC) frames. One of the challenges in multi-objective optimization of steel and RC buildings is to determine the balance between objective functions in order to find the Pareto-front. In this study, weighted sum method (WSM) optimization technique is utilized to solve RC frames optimization problems to minimize simultaneously cost and displacement, as objective functions, subject to the stress, displacement, size and other related constraints, based on the ACI 318-11 code. For this purpose, first, using WSM technique the two objective functions is converted in to single objective function and then GA applied for optimization procedure. Three design models of 4, 8 and 12-story RC frames are introduced as representative for short, mid and high rise buildings. Cross section of beams and columns are employed as design variables. The Pareto front for building models is obtained. To validate the results, they are compared to those obtained by ant colony optimization (ACO). The results illustrate the accuracy and rapidity of the proposed method.