Flower pollination algorithm: a comprehensive review

Abdel‐Basset, Mohamed; Shawky, Laila A.

doi:10.1007/s10462-018-9624-4

Cited by 179 publications

(50 citation statements)

References 142 publications

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“…where l best is the optimal learning rate solution in the global. γ is the scaling factor; its value is suggested to be in the range of (0,10) in previous studies [49]. It was found that the best result can be obtained when it is set to 0.1 in this application.…”

Section: Training Methods Based On Optimization Of Learning Ratementioning

confidence: 97%

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Blister Defect Detection Based on Convolutional Neural Network for Polymer Lithium-Ion Battery

Xie

Zhang

2019

Applied Sciences

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To ensure the quality and reliability of polymer lithium-ion battery (PLB), automatic blister defect detection instead of manual detection is developed in the production of PLB cell sheets. A convolutional neural network (CNN) based detection method is proposed to detect blister in cell sheets employing cell sheet images. An improved architecture for dense block and a learning method based on optimization of learning rate are discussed. The proposed method was superior to other machine learning based methods when the classification performance and confusion matrix were compared in experiments. The proposed CNN method had the best defect detection performance and real-time performance for industry field application. Keywords: defect detection; polymer lithium-ion battery; convolutional neural network; deep learning; blister defect; flower pollination algorithm IntroductionThe application of lithium-ion batteries has changed consumer electronic products, which greatly reduce the weight and volume of mobile phones, notebooks, and other portable products. Lithium-ion batteries have been widely studied, including model, optimal design, and so on [1][2][3][4][5][6]. At present, the most commonly used lithium-ion battery is polymer lithium-ion battery (PLB). The cathode materials of PLB are commonly lithium cobalt acid, lithium manganese acid, ternary materials or lithium iron phosphate. The anode materials usually use carbon materials, such as artificial graphite, natural graphite, intermediate phase carbon microspheres, petcoke, carbon fiber, pyrolysis resin carbon and so on. C-rate is the measurement of the charge and discharge current with respect to its nominal capacity. At present, most PLB use polymer gel electrolyte instead of liquid electrolyte, which makes PLB have the advantages of thinning, arbitrary area and arbitrary shape. These characteristics improve the capacity of PLB, and PLB has the characteristics of miniaturization, thinning, and light quantification. PLB has been widely used in portable electronic equipment, and it is gradually being applied to more fields. With the application of PLB in more and more electronic products, the quality of PLB has more and more influence on the quality of electronic products. The quality of PLB is critical to the quality and reliability of electronic products.Recently, an automatic line of PLB has been developed in Dongsheng Energy Corporation, Weihai, China. The PLB has a voltage of 48 V, a charging current of 2-3 A, and nominal capacity of 16 AH. The anode of PLB is a conductive high molecular polymer, the catrode is graphite, and a colloidal polymer electrolyte is used. During the production process of the PLB, several cathode pieces, anode pieces, and separator pieces are combined to produce a cell sheet. Great battery capacity can be provided with more cell sheets combined together. Thus, the quality inspection of cell sheets in automated production lines is essential to the quality of the final battery product. The cell sheet defect needs to be detected to ens...

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Section: Training Methods Based On Optimization Of Learning Ratementioning

confidence: 97%

“…where Γ(λ) is a standard gamma function, s is step, and λ was set to 1.5 in this study as recommended [49]. When P > P C , the current learning rate l i is updated as below to simulate self-pollination operation…”

Section: Training Methods Based On Optimization Of Learning Ratementioning

confidence: 99%

Blister Defect Detection Based on Convolutional Neural Network for Polymer Lithium-Ion Battery

Xie

Zhang

2019

Applied Sciences

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“…As above, FF may suffer from a weak global search ability, i.e., liable to be trapped into a local minimum. To solve this problem, some algorithms such as extreme learning machines, support vector machines, particle swarms and genetic algorithms are usually used to improve FF [24,25]. In this study, the improved flower pollination algorithm (IFPA) is developed to optimize the FF neural network.…”

Section: The Fundamentals Of the Ff Modelmentioning

confidence: 99%

Optimization of Feedforward Neural Networks Using an Improved Flower Pollination Algorithm for Short-Term Wind Speed Prediction

Ren

Lin

2019

Energies

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It is well known that the inherent instability of wind speed may jeopardize the safety and operation of wind power generation, consequently affecting the power dispatch efficiency in power systems. Therefore, accurate short-term wind speed prediction can provide valuable information to solve the wind power grid connection problem. For this reason, the optimization of feedforward (FF) neural networks using an improved flower pollination algorithm is proposed. First of all, the empirical mode decomposition method is devoted to decompose the wind speed sequence into components of different frequencies for decreasing the volatility of the wind speed sequence. Secondly, a back propagation neural network is integrated with the improved flower pollination algorithm to predict the changing trend of each decomposed component. Finally, the predicted values of each component can get into an overlay combination process and achieve the purpose of accurate prediction of wind speed. Compared with major existing neural network models, the performance tests confirm that the average absolute error using the proposed algorithm can be reduced up to 3.67%.

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“…From the conclusion, the contributor's states that, the FPA performance is high for different nonlinear problems compared to the other heuristic techniques. In article [11] the researcher considered FPA is an MPPT technique to track MPP under non-uniform irradiation and temperature conditions.…”

Section: Fig1 Evaluation Of Fpa Performance By Usingmentioning

confidence: 99%

“…Hence, in this article different metaheuristic MPPT techniques are discussed to obtain the MPP of solar PV. Those are Flower Pollination Algorithm (FPA) [10], Bat algorithm (BAT) [11], and Harmony Search (HS) [12].…”

Section: Introductionmentioning

confidence: 99%

Recent Nature Inspired Techniques for Solar Power Point Tracking Application

Kiran*,

Reddy

2019

IJITEE

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Photovoltaic (PV) array generates non-linear I-V and P-V characteristics. As a result, it is difficult to transfer the maximum power from source to load. The Maximum Power Point Tracking (MPPT) is used to track the Maximum Power Point (MPP) and extract the maximum power of solar PV. The disadvantages of conventional MPPT techniques are less MPP tracking speed, high MPP settling time and less accuracy. In order to overcome the disadvantages of conventional techniques, in this article different types of recent nature inspired optimization techniques are reviewed to track the MPP. In addition, the application of optimization technique based Maximum Power Point Tracking (MPPT) technique for standalone and grid connected application is explained in detail. The advantages of optimization techniques are highly reliable, better accuracy, less time to convergence and fast response.

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Flower pollination algorithm: a comprehensive review

Cited by 179 publications

References 142 publications

Blister Defect Detection Based on Convolutional Neural Network for Polymer Lithium-Ion Battery

Blister Defect Detection Based on Convolutional Neural Network for Polymer Lithium-Ion Battery

Optimization of Feedforward Neural Networks Using an Improved Flower Pollination Algorithm for Short-Term Wind Speed Prediction

Recent Nature Inspired Techniques for Solar Power Point Tracking Application

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