A multi-component PSO algorithm with leader learning mechanism for structural damage detection

Abstract

The particle swarm optimization (PSO) algorithm has been widely used to solve optimization problems. One of its main drawbacks is a slow and sometimes premature convergence, which traps the swarm in a local minimum. Several PSO variants have been proposed to alleviate this phenomenon. Still, when dealing with structural damage detection (SDD), the performances of these algorithms are not homogeneous and highly depend on the number of defects and their severities, which even lead them to require assistance from other methods. This paper proposes a multi-component PSO with cooperative learning named MuC-PSO. Instead of resorting to other methods, a strategy pool is constructed in the proposed MuC-PSO by combining four PSO variants, which guarantees that the algorithm can execute multiple search strategies collaboratively. Moreover, a leader learning mechanism (LLM) is also implemented to ensure information exchange and lead the global convergence of the method. This strategy allows the PSO variants to benefit from each other and enables the MuC-PSO to solve complex SDD problems. The performances of the MuC-PSO are evaluated in nine damage scenarios of single and multiple damages with severity levels between [10%,50%]. Our algorithm is compared with different recent optimization algorithms, including the four PSO variants alone and some non-PSO algorithms. The simulation results on three types of damages clearly demonstrate the effectiveness and superiority of the MuC-PSO.