Fruit Fly Optimization Algorithm Research Articles

Determinants of Tourism Stocks During the COVID-19: Evidence From the Deep Learning Models.

This paper examines the determinants of tourism stock returns in China from October 25, 2018, to October 21, 2020, including the COVID-19 era. We propose four deep learning prediction models based on the Back Propagation Neural Network (BPNN): Quantum Swarm Intelligence Algorithms (QSIA), Quantum Step Fruit-Fly Optimization Algorithm (QSFOA), Quantum Particle Swarm Optimization Algorithm (QPSO) and Quantum Genetic Algorithm (QGA). Firstly, the rough dataset is used to reduce the dimension of the indices. Secondly, the number of neurons in the multilayer of BPNN is optimized by QSIA, QSFOA, QPSO, and QGA, respectively. Finally, the deep learning models are then used to establish prediction models with the best number of neurons under these three algorithms for the non-linear real stock returns. The results indicate that the QSFOA-BPNN model has the highest prediction accuracy among all models, and it is defined as the most effective feasible method. This evidence is robust to different sub-periods.

The optimized GRNN based on the FDS-FOA under the hesitant fuzzy environment and its application in air quality index prediction

The generalized regression neural network (GRNN) is one of the most effective neural models in the field of information prediction. In this paper, we take advantage of the hesitant fuzzy set (HFS) and develop the GRNN under the hesitant fuzzy environment. However, the performance of the GRNN only depends on the smooth factor after determining the input and output variables. To determine the most appropriate smooth factor of network, we propose an improved fruit fly optimization algorithm with fast decreasing step (FDS-FOA) based on a dynamic step size function. A specific implementation process of the optimized GRNN based on FDS-FOA is also presented. Moreover, we apply the proposed model to the prediction of air quality index (AQI) in Beijing. Comparative analysis and sensitivity analysis are further conducted to illustrate the advantages of the optimized GRNN based on FDS-FOA.

Application of the fruit fly optimization algorithm to an optimized neural network model in radar target recognition

With the development of computer technology, there are more and more algorithms and models for data processing and analysis, which brings a new direction to radar target recognition. This study mainly analyzed the recognition of high resolution range profile (HRRP) in radar target recognition and applied the generalized regression neural network (GRNN) model for HRRP recognition. In order to improve the performance of HRRP, the fruit fly optimization algorithm (FOA) algorithm was improved to optimize the parameters of the GRNN model. Simulation experiments were carried out on three types of aircraft. The improved FOA-GRNN (IFOA-GRNN) model was compared with the radial basis function (RBF) and GRNN models. The results showed that the IFOA-GRNN model had a better convergence accuracy, the highest average recognition rate (96.4 %), the shortest average calculation time (275 s), and a good recognition rate under noise interference. The experimental results show that the IFOA-GRNN model has a good performance in radar target recognition and can be further promoted and applied in practice.

Coupling physics in machine learning to predict interlamellar spacing and mechanical properties of high carbon pearlitic steel

The present study proposed an improved composition-structure–property model which incorporates physical features in the machine learning(ML) process. Herein, the physical parameters, the volume fraction of ferrite, cementite and carbides and the transformation temperature of pearlite, were included to the dataset as extra input variables to guide the ML process. As a result, the dataset mixing physical features greatly improved the generalization ability and prediction accuracy of the generalized regression neural network(GRNN) model, which clearly demonstrates the practicability of the present physics coupled ML approach. Furthermore, several optimization algorithms are applied to improve the GRNN model and the fruit fly optimization algorithm(FOA) is demonstrated more effective than particle swarm optimization(PSO) algorithm. Thus then, a novel high-strength pearlitic steel was synthetized and experimentally validated with outperformed microstructural and mechanical characteristics.

A multi-objective optimization algorithm for feature selection problems

Feature selection (FS) is a critical step in data mining, and machine learning algorithms play a crucial role in algorithms performance. It reduces the processing time and accuracy of the categories. In this paper, three different solutions are proposed to FS. In the first solution, the Harris Hawks Optimization (HHO) algorithm has been multiplied, and in the second solution, the Fruitfly Optimization Algorithm (FOA) has been multiplied, and in the third solution, these two solutions are hydride and are named MOHHOFOA. The results were tested with MOPSO, NSGA-II, BGWOPSOFS and B-MOABC algorithms for FS on 15 standard data sets with mean, best, worst, standard deviation (STD) criteria. The Wilcoxon statistical test was also used with a significance level of 5% and the Bonferroni–Holm method to control the family-wise error rate. The results are shown in the Pareto front charts, indicating that the proposed solutions' performance on the data set is promising.

Scheduling Model of Power System Based on Forecasting Error of Wind Power Plant Output

Aiming at the problem of unnecessary waste of power resources in the power system caused by the uncertainty of wind farm output, and then affecting the economics of the system. This paper proposes a power system scheduling model based on wind power output forecasting errors. In the forecasting stage, in view of the defect that the Fruit Fly Optimization Algorithm (FOA) is easy to fall into the local optimal value, an Adaptive Mutation Fruit Fly Optimization Algorithm (AMFOA) based on the flavor concentration variance is proposed. And then, the parameters of Extreme Learning Machine (ELM) are optimized by AMFOA to predict the wind power output. In the scheduling phase, a limit scenario is established based on the obtained prediction error to reduce the fluctuation of the wind farm output. When modeling, the influence of the prediction error is considered in the power balance equation, and it is constructed to maximize the probability of its establishment. And then, incorporate it into the objective function. Based on this, a scheduling model is established and solved by AMFOA. Finally, an example is used to verify the accuracy of the proposed forecasting model and the economics of the scheduling model. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Cluster decomposing and multi-objective optimization based-ensemble learning framework for motor imagery-based brain–computer interfaces

Objective. Motor imagery (MI) is a mental representation of motor behavior and a widely used pattern in electroencephalogram (EEG) based brain–computer interface (BCI) systems. EEG is known for its non-stationary, non-linear features and sensitivity to artifacts from various sources. This study aimed to design a powerful classifier with a strong generalization capability for MI based BCIs. Approach. In this study, we proposed a cluster decomposing based ensemble learning framework (CDECL) for EEG classification of MI based BCIs. The EEG data was decomposed into sub-data sets with different distributions by clustering decomposition. Then a set of heterogeneous classifiers was trained on each sub-data set for generating a diversified classifier search space. To obtain the optimal classifier combination, the ensemble learning was formulated as a multi-objective optimization problem and a stochastic fractal based binary multi-objective fruit fly optimization algorithm was proposed for solving the ensemble learning problem. Main results. The proposed method was validated on two public EEG datasets (BCI Competition IV datasets IIb and BCI Competition IV dataset IIa) and compared with several other competing classification methods. Experimental results showed that the proposed CDECL based methods can effectively construct a diversity ensemble classifier and exhibits superior classification performance in comparison with several competing methods. Significance. The proposed method is promising for improving the performance of MI-based BCIs.

Optimal configuration of distributed power sources based on Fruit Fly optimization algorithm

Based on the fuzzy membership technology to optimize the configuration of distributed generation access to the distribution network, a multi-objective optimization model is established that comprehensively considers the investment benefits of DG, polluting gas emissions and voltage quality. The chaotic sequence of Tent mapping is quoted in the fruit fly optimization algorithm (FOA) to improve it, increase the diversity of the population, and reduce the possibility of the algorithm falling into a local optimum. The simulation results of the PG&E33 node system verify the effectiveness and rationality of the model and the improved algorithm.

Robust Secure Color Image Watermarking Using 4D Hyperchaotic System, DWT, HbD, and SVD Based on Improved FOA Algorithm

In the recent past, a different set of algorithms for watermarking and securing the color images have been developed by using transformation, decomposition, and optimization techniques for watermark embedding and extraction. In this paper, we propose an optimized and robust watermarking algorithm coupled with a 4D hyperchaotic system, and its performance is analyzed by extending and differentiating the existing work. Our contribution in the presented work is watermarking and securing the color images by an optimized algorithm that uses transformation technique such as Discrete Wavelet Transformation (DWT) and decomposition techniques such as Hessenberg decomposition (HbD) and singular value decomposition (SVD) coupled with the 4D hyperchaotic system, while the optimization is carried out by improved evolution fruit fly optimization algorithm (IEFOA). The experimental results based on different types of attacks (filter attacks, noise attacks, cropping attack, JPEG compression, motion blur, sharpening, and rotation), key sensitivity, normalized correlation, peak signal-to-noise ratio, and structural similarity index measure are done for measuring the algorithm’s performance regarding invisibility and robustness. The experimental results show that the proposed scheme has excellent invisibility and keeps a good trade-off between invisibility and robustness. The experiment results show that the proposed approach outperforms the previous approaches.

Blockchain-assisted secure image transmission and diagnosis model on Internet of Medical Things Environment

In recent days, the Internet of Medical Things (IoMT) is commonly employed in different aspects of healthcare applications. Owing to the increasing necessitates of IoT, a huge amount of sensing data is collected from distinct IoT gadgets. To investigate the generated data, artificial intelligence (AI) models plays an important role to achieve scalability and accurate examination in real-time environment. However, the characteristics of IoMT result in certain design challenges, namely, security and privacy, resource limitation, and inadequate training data. At the same time, blockchain, an upcoming technology, has offered a decentralized architecture, which gives secured data transmission and resources to distinct nodes of the IoT environment and is stimulated for eliminating centralized management and eliminates the challenges involved in it. This paper designs deep learning (DL) with blockchain-assisted secure image transmission and diagnosis model for the IoMT environment. The presented model comprises a few processes namely data collection, secure transaction, hash value encryption, and data classification. Primarily, elliptic curve cryptography (ECC) is applied, and the optimal key generation of ECC takes place using hybridization of grasshopper with fruit fly optimization (GO-FFO) algorithm. Then, the neighborhood indexing sequence (NIS) with burrow wheeler transform (BWT), called NIS-BWT, is employed to encrypt the hash values. At last, a deep belief network (DBN) is utilized for the classification process to diagnose the existence of disease. An extensive experimental validation takes place to determine the analysis of the optimal results of the presented model, and the results are investigated under diverse aspects.

Forecasting energy consumption of long-distance oil products pipeline based on improved fruit fly optimization algorithm and support vector regression

Predicting the energy consumption of oil pipelines is an important part of pipeline companies’ energy-saving and consumption-reduction plans and the realization of refined management. In order to predict the energy consumption of the long-distance product oil pipeline faster and better, this manuscript innovatively uses the normal distribution function to improve the search mode of the fruit fly optimization algorithm (FOA). It establishes the normal distribution fruit fly optimization algorithm (NFOA). It enhances search accuracy in the central area and effectively expands the search scope. Experimental results show that the accuracy and stability of the algorithm are improved by 100% and 900%. Then, NFOA combined with support vector regression (NFOA-SVR) is used to predict the three long-distance product pipeline data sets in China. The results show that the optimization speed and prediction accuracy of NFOA-SVR in LCY-Others set and LW-total set are significantly better than the other two algorithms. In the LCY-Pump set, NFOA-SVR has the same accuracy as the other two algorithms. Finally, experiments on random data sets show that the accuracy and stability of NFOA-SVR gradually decrease with the increase of the standard deviation of the data set.

Modified Stacked Autoencoder Using Adaptive Morlet Wavelet for Intelligent Fault Diagnosis of Rotating Machinery

Intelligent fault diagnosis techniques play an important role in improving the abilities of automated monitoring, inference, and decision making for the repair and maintenance of machinery and processes. In this article, a modified stacked autoencoder (MSAE) that uses adaptive Morlet wavelet is proposed to automatically diagnose various fault types and severities of rotating machinery. First, the Morlet wavelet activation function is utilized to construct an MSAE to establish an accurate nonlinear mapping between the raw nonstationary vibration data and different fault states. Then, the nonnegative constraint is applied to enhance the cost function to improve sparsity performance and reconstruction quality. Finally, the fruit fly optimization algorithm is used to determine the adjustable parameters of the Morlet wavelet to flexibly match the characteristics of the analyzed data. The proposed method is used to analyze the raw vibration data collected from a sun gear unit and a roller bearing unit. Experimental results show that the proposed method is superior to other state-of-the-art methods.

A hybrid model based on general regression neural network and fruit fly optimization algorithm for forecasting and optimizing paclitaxel biosynthesis in Corylus avellana cell culture

BackgroundPaclitaxel is a well-known chemotherapeutic agent widely applied as a therapy for various types of cancers. In vitro culture of Corylus avellana has been named as a promising and low-cost strategy for paclitaxel production. Fungal elicitors have been reported as an impressive strategy for improving paclitaxel biosynthesis in cell suspension culture (CSC) of C. avellana. The objectives of this research were to forecast and optimize growth and paclitaxel biosynthesis based on four input variables including cell extract (CE) and culture filtrate (CF) concentration levels, elicitor adding day and CSC harvesting time in C. avellana cell culture, as a case study, using general regression neural network-fruit fly optimization algorithm (GRNN-FOA) via data mining approach for the first time.ResultsGRNN-FOA models (0.88–0.97) showed the superior prediction performances as compared to regression models (0.57–0.86). Comparative analysis of multilayer perceptron-genetic algorithm (MLP-GA) and GRNN-FOA showed very slight difference between two models for dry weight (DW), intracellular and extracellular paclitaxel in testing subset, the unseen data. However, MLP-GA was slightly more accurate as compared to GRNN-FOA for total paclitaxel and extracellular paclitaxel portion in testing subset. The slight difference was observed in maximum growth and paclitaxel biosynthesis optimized by FOA and GA. The optimization analysis using FOA on developed GRNN-FOA models showed that optimal CE [4.29% (v/v)] and CF [5.38% (v/v)] concentration levels, elicitor adding day (17) and harvesting time (88 h and 19 min) can lead to highest paclitaxel biosynthesis (372.89 µg l−1).ConclusionsGreat accordance between the predicted and observed values of DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion support excellent performance of developed GRNN-FOA models. Overall, GRNN-FOA as new mathematical tool may pave the way for forecasting and optimizing secondary metabolite production in plant in vitro culture.

Water chaotic fruit fly optimization-based deep convolutional neural network for image watermarking using wavelet transform

Due to the rapid growth of multimedia in network technology, accessing the digital media becomes very easy. Hence, protecting the intellectual property requires more interest in image watermarking. For this sake, different image watermarking approaches are developed, but it poses robustness and transparency issues. Therefore, an effective image watermarking method named Water Chaotic fruit fly Optimization algorithm-based Deep Convolutional neural network (WCFOA-based Deep CNN) is developed for embedding the secret message to the cover media. The proposed WCFOA is developed by integrating the Water Wave Optimization (WWO) with the Chaotic Fruit Fly Optimization algorithm (CFOA). The inspiration of propagation operator and the refraction operator increases the diversity of population and minimizes the premature convergence. However, the breaking, propagation and the refraction operator of the proposed optimization shows the effectiveness of balance between the exploitation of exploration phase in search space using the fitness measure. Accordingly, the embedding process is achieved using the wavelet transform with the selected optimal region using the evaluated fitness value. Several images of brain tumors from BRATS dataset, with tumors having different contrast and form, are used to assess the proposed method. The experimental analysis shows that, the proposed WCFOA-based Deep CNN obtained better performance using the metrics, like correlation coefficient and Peak signal-to-noise ratio (PSNR) with the values of 1 and 45.2157 using without noise scenario and the correlation coefficient and PSNR of 0.9918 and 45.0627 for Impulse noise. By considering the salt and pepper noise, the correlation coefficient and PSNR is 0.9918 and 47.001 and in the Gaussian noise scenario the values of correlation coefficient and PSNR is 0.990 and 46.985, respectively.

Knowledge and Behavior-Driven Fruit Fly Optimization Algorithm for Field Service Scheduling Problem with Customer Satisfaction

The field service scheduling problem (FSSP) is the key problem in field services. Field service pays particular attention to customer experience, that is, customer satisfaction. Customer satisfaction described by customer behavior characteristics based on the prospect theory is considered as the primary optimization goal in this paper. The knowledge of the insertion feasibility on the solution is analysed based on the skill constraint and time window. According to the knowledge, an initialization method based on the nearest heuristic algorithm is constructed. Based on the prior knowledge of the FSSP and the endowment of the Fruit Fly Optimization Algorithm, two operators are defined according to the matrix encoding method. Based on these two operators, three search strategies are then proposed, and the smell-based search strategy and vision-based search strategy for the FOA are redesigned. To verify the performance of the algorithms, the proposed operators and strategies are tested and analysed in the well-known benchmark. Through comparison with the state-of-the-art algorithms, the results show that the proposed HFOA is an effective and efficient method to solve the FSSP with customer satisfaction.

Ant Lion Optimization Based Medical Data Classification Using Modified Neuro Fuzzy Classifier

The progression of converting depiction of medical analysis and measures into widespread medical code numbers is known as medical classification. According to the conclusion of medical investigation or description of medical handling, the classification of difficulty is resolved by means of the medical specialist in medical region. Primarily preprocessing is employed to alter appropriate model from unprocessed medical datasets in obtainable medical data classification process. After that, orthogonal local preserving projection (OLPP) is exploited to diminish the great dimensions of attribute. At last, the combination of artificial bee colony algorithm with neural network is used for classification of disease. This progression is extra time overriding and encompass poor exactness because classification is carry out devoid of removing the significant attribute from the medical dataset. Therefore, the proposed method is employed to eliminate of these difficulty. At this point the preprocessing is carrying out the medical dataset. It aids to eliminate the unnecessary noises and deficiency take place in the unprocessed medical datasets. Afterward feature choice is carry out. In medical data classification, feature choice is as well recognized as variable choice, attributes choice or variable subset choice which is the progression of choosing a subset of related attribute (variables, predictors). The ant lion optimization (ALO) is utilized for this intention. After that, classification is carrying out for distinguishing the occurrence and nonappearance of diseases. The intention is bring about by means of Modified Neuro Fuzzy classifier. The regulations engender through this classifier which are optimized by means of fruit fly optimization algorithm (FFOA). Therefore the medical data can be categorized into normal or abnormal one. This process task is quicker and generates extra precise consequences than the obtainable process. The presentation of the proposed process is calculated in expressions of accuracy, specificity and sensitivity value. The proposed process is executed in MATLAB.

Structural Damage Identification Based on Improved Fruit Fly Optimization Algorithm

To locate the damage of the structure efficiently and judge the damage degree, this paper proposes an improved fruit fly optimization algorithm (IFOA). Aiming at the problem of poor convergence of the standard fruit fly optimization algorithm in the face of complex structure damage identification, the IFOA introduces the concept of collaborative search of two subpopulations. The IFOA divides the entire population into positive subgroups and negative subgroups based on the individual taste concentration results. Among them, the positive subgroup uses the improved dynamic adaptive search step size to perform a fine search locally to improve its local search ability. Negative subgroups continue to use the standard fruit fly optimization algorithm for optimization, taking advantage of the powerful global search capabilities of the standard fruit fly optimization algorithm. It enables the algorithm to balance global and local search capabilities, prevents the algorithm from falling into local optimum, and speeds up the convergence speed and accuracy of the algorithm. Simulation results show that IFOA can effectively identify the damage location and damage degree of the structure, and it still performs well when facing the complex steel truss damage identification.

A novel fruit fly optimization algorithm with Levi flight and challenge probability

Abstract Aiming at the problem of low convergence precision and premature convergence of fruit fly optimization algorithm (FOA), this paper proposes a novel fruit fly optimization algorithm with Levi flight and challenge probability (LCFOA). We introduce the Levi flight mechanism and challenge adjustment process to coordinate the global exploration and local exploitation capabilities of the algorithm. Then, we designed an adaptive challenge factor in the challenge adjustment process to improve the convergence precision of the algorithm. The convergence performance of the algorithm is verified by 6 different benchmark functions. The numerical experimental results comfirmed that the proposed LCFOA has better search precision and convergence speed.

Search of Non-circular Slip Surface Based on Improved FOA

The determination of the most critical non-circular slip surface can be attributed to the optimization of complex non-linear multi-peak function due to its numerous control variables and large amount of calculation. It is a trend in recent years to apply intelligent optimization algorithm into slope stability analysis. Considering that the standard Fruit fly Optimization Algorithm (FOA) is prone to fall into local extremum, the improved Fruit fly Optimization Algorithm is obtained by incorporating the standard FOA with the simulated annealing idea. In order to improve the search efficiency, a fixed step size is adjusted to an adaptive step size, and a double tier search strategy is proposed to be applied: the potential non-circular slip surface is obtained from the outer layer, and the factor of safety along the potential slip surface is calculated step by step from the inner layer. The improved FOA is applied to a slope with weak interlayer. The feasibility, superiority and efficiency of the improved algorithm are proved by comparing its answers to the judges'. Different inter-slice force functions, various initial values of Fs and λ are assumed, and the results show that these parameters could hardly affect final solution for safety factor.

Deep Belief Network with FOA based Cooperative Spectrum Sensing in Cognitive Radio Network

The artificial neural network (ANN) is proposed to the cooperative spectrum sensing (CSS) at CR network. ANN has the drawback that the training of ANN with many hidden layers on large amount of data can affect the performance of the network and optimisation of ANN parameters is a challenging task. To overcome the above drawbacks, the deep belief network (DBN) and fruit fly optimisation algorithm (FOA) are employed. The DBN has four parameters on learning step: learning rate, weight decay, penalty parameter, number of hidden units. These parameters should be properly selected for the proper functioning of DBN. Tuning of these parameters is taken into an optimisation issue and it is addressed by FOA. The proposed method has three steps: training, validation and testing. The metrics used for the performance evaluation are accuracy, false alarm rate and loss detection.