Smart Structures and Systems

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A hybrid identification method on butterfly optimization and differential evolution algorithm

  • Zhou, Hongyuan (Key Laboratory of Urban Security and Disaster Engineering of Ministry of Education, Beijing University of Technology) ;
  • Zhang, Guangcai (Key Laboratory of Urban Security and Disaster Engineering of Ministry of Education, Beijing University of Technology) ;
  • Wang, Xiaojuan (Key Laboratory of Urban Security and Disaster Engineering of Ministry of Education, Beijing University of Technology) ;
  • Ni, Pinghe (Key Laboratory of Urban Security and Disaster Engineering of Ministry of Education, Beijing University of Technology) ;
  • Zhang, Jian (Faculty of Civil Engineering and Mechanics, Jiangsu University)
  • Received : 2020.01.22
  • Accepted : 2020.06.14
  • Published : 2020.09.25
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Abstract

Modern swarm intelligence heuristic search methods are widely applied in the field of structural health monitoring due to their advantages of excellent global search capacity, loose requirement of initial guess and ease of computational implementation etc. To this end, a hybrid strategy is proposed based on butterfly optimization algorithm (BOA) and differential evolution (DE) with purpose of effective combination of their merits. In the proposed identification strategy, two improvements including mutation and crossover operations of DE, and dynamic adaptive operators are introduced into original BOA to reduce the risk to be trapped in local optimum and increase global search capability. The performance of the proposed algorithm, hybrid butterfly optimization and differential evolution algorithm (HBODEA) is evaluated by two numerical examples of a simply supported beam and a 37-bar truss structure, as well as an experimental test of 8-story shear-type steel frame structure in the laboratory. Compared with BOA and DE, the numerical and experimental results show that the proposed HBODEA is more robust to detect the reduction of stiffness with limited sensors and contaminated measurements. In addition, the effect of search space, two dynamic operators, population size on identification accuracy and efficiency of the proposed identification strategy are further investigated.

Keywords

Acknowledgement

The work was supported by the research project of Beijing Municipal Committee of Education Project KM201810005019, Beijing Natural Science Foundation grant number 8184063, National Natural Science Foundation of China (NSFC) grant number 11872190, 51808017 and 51778028. These financial supports are sincerely appreciated. Besides, the author would like to thank the anonymous reviewers for their detailed and fruitful remarks.

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