Sensor Placement Optimization in Structural Health Monitoring Using Niching Monkey Algorithm
Abstract
Optimal sensor placement (OSP) method plays a key role in setting up a health monitoring system for large-scale structures. This paper describes the implementation of monkey algorithm (MA) as a strategy for the optimal placement of a predefined number of sensors. To effectively maintain the population diversity while enhancing the exploitation capacities during the optimization process, a novel niching monkey algorithm (NMA) by combining the MA with the niching techniques is developed in this paper. In the NMA, the dual-structure coding method is adopted to code the design variables and a chaos-based approach instead of a pure random initialization is employed to initialize the monkey population. Meanwhile, the niche generation operation and fitness sharing mechanism are modified and incorporated to alleviate the premature convergence problem while enhancing the exploration of new search domain. In addition, to promote interactions and share the available resources, the replacement scheme is proposed and adopted among the niches. Finally, numerical experiments are conducted on a high-rise structure to evaluate the performance of the proposed NMA. It is found that the innovations in the proposed NMA can effectively improve the convergence of algorithm and generate superior sensor configurations when compared to the original MA.
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