Finite element model updating using fish school search and volitive particle swarm optimization

Abstract

A customized version of Fish School Search (FSS) algorithm and the innovative volitive operator of FSS (which is incorporated into the regular particle swarm optimization (PSO) algorithm) are applied to the finite element model (FEM) updating problem. These algorithms are tested on the updating of two real structures namely; an unsymmetrical H-shaped beam and a GARTEUR SM-AG19 structure. The results thereof are compared with results of two other metaheuristic algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) on the same structures. The GA and PSO algorithms being the most popular metaheuristic algorithms used in the model updating area. It is observed that on average, the FSS and PSO algorithms produce more accurate results than the GA. In this paper we confirm that the FSSb (i.e. a customised version of the FSS algorithm, with minor modifications) and the hybrid algorithm - the Volitive PSO (i.e. the volitive operator of FSS into PSO) - are also more effective in this optimization task, producing superior results when updating the underlining Finite Element Model of both structures.