Structural Damage Identification Based on Improved Fruit Fly Optimization Algorithm

Xiong, Chunbao; Lian, Sida

doi:10.1007/s12205-021-1115-5

Cited by 9 publications

(6 citation statements)

References 56 publications

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“…As for the problem that the judgment value of drosophila taste concentration is always positive, the taste concentration is used to solve it [23].…”

Section: I �mentioning

confidence: 99%

Wind Power Forecasting by the BP Neural Network with the Support of Machine Learning

Tian

Bao

Liu

2022

Mathematical Problems in Engineering

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The goal of the research is to increase the accuracy of wind power forecasts while maintaining the power system’s stability and safety. First, the wireless sensor network (WSN) is used to collect the meteorological data of wind power plants in real time. Second, the real-time data collected by WSN are combined with the meteorological forecast data of some meteorological organizations. Then, the fruit fly optimization algorithm (FOA) is improved, and the improved fruit fly optimization algorithm (IFOA) and back propagation neural network (BPNN) are combined to construct the wind power forecast model. Finally, the signal reception of the WSN and the error of wind power forecast under different receiving distances and different antenna heights are tested. The results show that with the increase of receiving and transmitting distance, the signal strength decreases, the packet loss rate increases, and the electromagnetic wave of wind plants will cause some interference to the signal strength. The fly optimization algorithm-back propagation (IFOA-BP) wind power forecast model has a better effect than other models in wind power prediction and can better fit the actual tested wind power. Its root mean square error (RMSE) and mean absolute error (MAE) are 0.16 and 0.11, respectively. The research results provide a reference for improving the forecast accuracy of wind power.

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“…As for the problem that the judgment value of drosophila taste concentration is always positive, the taste concentration is used to solve it [23].…”

Section: I �mentioning

confidence: 99%

Wind Power Forecasting by the BP Neural Network with the Support of Machine Learning

Tian

Bao

Liu

2022

Mathematical Problems in Engineering

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“…Based on the search strategy of population collaboration and information sharing, the basic FOA of strong robustness and reasonable convergence rate can quickly discover the optimal solution of optimization problems through the osphresis and vision foraging phases. It holds the characteristics of easy implementation and understanding of the algorithm code and has now been widely used in various fields [29][30][31].…”

Section: Foamentioning

confidence: 99%

A Heuristic Projection Pursuit Method Based on a Connection Cloud Model and Set Pair Analysis for Evaluation of Slope Stability

Wang

Shen

et al. 2021

Symmetry

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Determining the projection direction vector (PDV) is essential to the projection pursuit evaluation method for high-dimensional problems under multiple uncertainties. Although the PP method using a cloud model can facilitate interpretation of the fuzziness and randomness of the PDV, it ignores the asymmetry of the PDV and the fact that indicators are actually distributed over finite intervals; it quickly falls into premature defects. Therefore, a novel PP evaluation method based on the connection cloud model (CCM) is discussed to remedy these drawbacks. In this approach, adaptive numerical characteristics of the CCM are adopted to represent the randomness and fuzziness of the candidate PDV and evaluation indicators. Meanwhile, to avoid complex computing and to accelerate the convergence speed of the optimization procedure, an improved fruit fly optimization algorithm (FOA) is set up to find the rational PDV. Alternatively, candidate PDVs are mutated based on the mechanism “pick the best of the best” using set pair analysis (SPA) and chaos theory. Furthermore, the applicability and reliability are discussed based on an illustrative example of slope stability evaluation and comparisons with the neural network method and the PP evaluation method based on the other FOAs and the genetic algorithm. Results indicate that the proposed method with simpler code and quicker convergence speed has good global ergodicity and local searching capabilities, and can better explore the structure of high-dimensional data with multiple uncertainties and asymmetry of the PDV relative to other methods.

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“…The standard fruit fly algorithm has outstanding global optimization capabilities in the early stage and can quickly find the optimal value, but later search capabilities are poor and the optimization accuracy is not high when solving complex problems [16]. This feature was pointed out by [17]. With the increase in the number of microsources, it is necessary to improve the development and exploration capabilities of fruit fly algorithms to ensure optimization accuracy and speed.…”

Section: Introductionmentioning

confidence: 99%

Droop Control Optimization Strategy for Parallel Inverters in a Microgrid Based on an Improved Population Division Fruit Fly Algorithm

2022

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When the topological structure of the microgrid system changes, the traditional droop control strategy sometimes causes the output power, frequency, and voltage of the inverter to exceed their limits and oscillate, and traditional strategies cannot guarantee the dynamic performance and steady-state accuracy of inverter control. To this end, this paper is based on an improved three-partition fruit fly algorithm. An optimization strategy for grid-connected inverter droop control is proposed in this paper, and then, the PI parameters of microgrid droop control are optimized in real time. This strategy divides the fruit fly population into three zones according to the inverter output and then automatically updates the multistrategy mode according to the difference in fruit fly performance in each zone. Among them, in zone I, a local fine search is conducted to ensure that the population does not degenerate; in zone II, adaptive adjustment is performed, ensuring the diversity and convergence of the algorithm; and in zone III, fruit flies are guided to accelerate convergence. The effectiveness and feasibility of this strategy is verified by this article according to simulation experiments and actual application cases. The results show that the proposed control strategy can make the inverter output follow the changes in the system for adaptive adjustment. The inverter response speed is increased 40-fold, and the steady-state error is reduced by 4.3%.

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Structural Damage Identification Based on Improved Fruit Fly Optimization Algorithm

Cited by 9 publications

References 56 publications

Wind Power Forecasting by the BP Neural Network with the Support of Machine Learning

Wind Power Forecasting by the BP Neural Network with the Support of Machine Learning

A Heuristic Projection Pursuit Method Based on a Connection Cloud Model and Set Pair Analysis for Evaluation of Slope Stability

Droop Control Optimization Strategy for Parallel Inverters in a Microgrid Based on an Improved Population Division Fruit Fly Algorithm

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