Use of modified hybrid PSOGSA for optimum design of RC frame

Abstract

ABSTRACTA realistic and optimum design of reinforced concrete structural frame, by hybridizing enhanced versions of standard particle swarm optimization (PSO) and standard gravitational search algorithm (GSA) is presented in this paper. PSO has been democratized by considering all good and bad experiences of the particles, whereas GSA has been made self-adaptive by considering a specific range for certain parameters like ‘gravitational constant’ and ‘set of agents with best fitness value.’ Optimal size and reinforcement of the members have been found by employing the technique in a computer-aided environment. Use of self-adaptive GSA together with democratic PSO technique has been found to provide two distinct advantages over standard PSO and GSA, namely better capability to escape from local optima and faster convergence rate. The entire formulation for optimal cost design of frame includes the cost of beams and columns. In this approach, variables of each element of structural frame have been considered as continuous functions and rounded off appropriately to imbibe practical relevance to the study. An example has been considered to emphasize the validity of this optimum design procedure and results have been compared with earlier studies.