Sensor Placement Optimization in Structural Health Monitoring Using Cluster-In-Cluster Firefly Algorithm

Abstract

The determination of the optimal sensor placement (OSP) is a significant task that must be completed before a structural health monitoring (SHM) system is implemented on a real structure. The firefly algorithm (FA) is a recently developed nature-inspired metaheuristic algorithm for continuous optimization problems. This paper proposes a cluster-in-cluster firefly algorithm (CiCFA) for the optimum SHM sensor deployment. First, the code defining position coordinates in basic FA is replaced with a one-dimensional binary coding system, and the Euclidean distance is replaced by the Hamming distance. Then, a movement scheme for a darker firefly approaching a lighter firefly is developed. Last, a cluster-in-cluster strategy is employed to improve the convergence speed. In addition, a self-adaptive dynamic penalty function is introduced to convert the constraint imposed by the limited wireless data transmission range in the optimal wireless sensor placement (OWSP) problem to an unconstrained optimization problem. To demonstrate the effectiveness and applicability of the proposed optimization method, numerical examples of wired sensor placement and wireless sensor placement are carried out. The results reveal that the CiCFA provides a better sensor configuration than the genetic algorithm with high efficiency and high stability and can be applied in the OWSP problem. The proposed CiCFA provides a promising alternative for solving the OSP problem in practical SHM systems.