Beetle-Swarm Evolution Competitive Algorithm for Bridge Sensor Optimal Placement in SHM

Yang, Jianhui; Peng, Zhenrui

doi:10.1109/jsen.2019.2934996

Cited by 16 publications

(9 citation statements)

References 23 publications

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“…The BAS algorithm is utilized to optimize a back-propagation neural network and predict cement strength [23]. A beetle swarm evolutionary competitive algorithm is proposed in [24], which introduces the swarm evolutionary competition mechanism into BAS to optimize bridge sensors. The BAS algorithm is mostly used in multi-objective optimization problems.…”

Section: Swarm Intelligence Algorithmmentioning

confidence: 99%

Global Optimization Algorithm for Cloud Service Composition

Yang

Xue

Liu

et al. 2021

IEICE Trans. Inf. & Syst.

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Service composition optimization is a classic NP-hard problem. How to quickly select high-quality services that meet user needs from a large number of candidate services is a hot topic in cloud service composition research. An efficient second-order beetle swarm optimization is proposed with a global search ability to solve the problem of cloud service composition optimization in this study. First, the beetle antennae search algorithm is introduced into the modified particle swarm optimization algorithm, initialize the population bying using a chaotic sequence, and the modified nonlinear dynamic trigonometric learning factors are adopted to control the expanding capacity of particles and global convergence capability. Second, modified secondary oscillation factors are incorporated, increasing the search precision of the algorithm and global searching ability. An adaptive step adjustment is utilized to improve the stability of the algorithm. Experimental results founded on a real data set indicated that the proposed global optimization algorithm can solve web service composition optimization problems in a cloud environment. It exhibits excellent global searching ability, has comparatively fast convergence speed, favorable stability, and requires less time cost.

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Section: Swarm Intelligence Algorithmmentioning

confidence: 99%

Global Optimization Algorithm for Cloud Service Composition

Yang

Xue

Liu

et al. 2021

IEICE Trans. Inf. & Syst.

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“…The estimation of an approximated gradient is a key feature of BAS, which distinguishes it from another metaheuristic algorithm. Since its introduction, BAS has found its application in several real-world systems [17], [55]- [64]. The working of the original BAS can be described like this; at each iteration, the value of the objective function is computed at each antennae fiber location, a vector is drawn from the fiber with the lowest value toward the fiber with the highest value, the vector represents the direction of the approximated gradient.…”

Section: (A)mentioning

confidence: 99%

BAS-ADAM: an ADAM based approach to improve the performance of beetle antennae search optimizer

Khan

Cao

et al. 2020

IEEE/CAA J. Autom. Sinica

168

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In this paper, we propose enhancements to Beetle Antennae Search (BAS) algorithm, called BAS-ADAM, to smoothen the convergence behavior and avoid trapping in local-minima for a highly non-convex objective function. We achieve this by adaptively adjusting the step-size in each iteration using the Adaptive Moment Estimation (ADAM) update rule. The proposed algorithm also increases the convergence rate in a narrow valley. A key feature of the ADAM update rule is the ability to adjust the step-size for each dimension separately instead of using the same step-size. Since ADAM is traditionally used with gradient-based optimization algorithms, therefore we first propose a gradient estimation model without the need to differentiate the objective function. Resultantly, it demonstrates excellent performance and fast convergence rate in searching for the optimum of non-convex functions. The efficiency of the proposed algorithm was tested on three different benchmark problems, including the training of a high-dimensional neural network. The performance is compared with Particle Swarm Optimizer (PSO) and the original BAS algorithm.

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“…At the same time, of the many deep learning SDI studies, few of them have considered sensor placement schemes; while different sensor layouts will result in different structural response data, which may contain different structural damage information [36,37], the impact of which on deep learning SDI cannot be ignored.…”

Section: Introductionmentioning

confidence: 99%

A new convolutional neural network-based framework and data construction method for structural damage identification considering sensor placement

Yang

Peng

2023

Meas. Sci. Technol.

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In the application of data driven Structural Damage Identification (SDI) based on supervised deep learning technology, valid data demarcation is the foundation; a convolutional neural network model with learning ability and capability of processing rich signal information is the core. Based on this understanding, this work makes three contributions: Firstly, the structural damage location and severity are jointly demarcated, and the SDI problem is transformed into a multi-classification task. Secondly, a 3D Signal processing Convolutional Neural Networks (3DS-CNN) is designed with an attempt to identify the complex and slight damages using the most basic network structure. Thirdly, a “Major and Subsidiary” Data Construction (MSDC) method integrating the key Intrinsic Mode Function (IMF) is proposed to construct 3D data. Then the proposed schemes are verified by two different structures. The results show that the 3DS-CNN has excellent damage identification ability for small-size data with noise pollution. MSDC method can enrich the feature information of the damage signals and help the network with deep feature excavation, even if the vibration signals are heavily polluted. Going one step further, the impact of sensor placement is discussed, and it is found that when external excitation is obvious, better SDI accuracy can be achieved even using a single sensor signal with slight noise. When the noise interference is obvious, the generalization ability and noise robustness of the network can be enhanced by optimizing sensor placement. In this case, the sensor placement criteria and the sensitive nodes of the structure should be comprehensively and carefully considered to avoid mutual “coupling” interference of data between sensors.

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Beetle-Swarm Evolution Competitive Algorithm for Bridge Sensor Optimal Placement in SHM

Cited by 16 publications

References 23 publications

Global Optimization Algorithm for Cloud Service Composition

Global Optimization Algorithm for Cloud Service Composition

BAS-ADAM: an ADAM based approach to improve the performance of beetle antennae search optimizer

A new convolutional neural network-based framework and data construction method for structural damage identification considering sensor placement

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