Hybrid TLBO and BFGS for structural health monitoring optimisation problems

摘要

This paper proposes hybrid teaching learning based optimisation (TLBO) and the Broyden-Fletcher-Goldfarb-Shanno algorithm (BFGS) for solving structural health monitoring optimisation problems. Two structural damage detection problems from two different truss structures are used to examine the search performance of the proposed algorithm while several well establish meta-heuristics (MHs) are used for benchmarking. The results indicated that the proposed algorithm is superior to the others. This study clarified that integrating BFGS into TLBO leads to increasing search performance of the new hybrid algorithm for solving structural health monitoring optimisation problems.