Fruit Fly Optimization Algorithm Research Articles

A New Estimation Method of the PID Controller In Automatic Train Operation Systems

PID controller plays an important role in Automatic Train Operation systems. Many researches have been done on the PID parameters estimation. However, the traditional models simplified the characteristics of the trains in different speeds and operation modes. It was difficult to overcome the problems with different kinds of algorithms based on the traditional models, especially the accuracy requirement of the stopping distance. In this paper, a new model of the PID controller was proposed based on the practical train operation stages. And the different response time delay was considered besides of the transmission time delay. With this new model, an Improved Fruit Fly Optimization Algorithm was applied. The tracking performance were greatly improved both on running distance and real time. The simulation results showed a better performance than the traditional methods.

Optimizing Neural Network Model Performance for Wind Energy Forecasting

High variability and intermittency of wind create difficulties in managing and optimizing wind farms. Short-term forecasts are essential for a power plant’s safe operation. The aim of this work was to develop an efficient model for forecasting wind energy in the short term using machine learning and metaheuristics methods. The study improved a fruit Fly Optimization Algorithm (FOA) with decreasing step size to enhance the forecasting accuracy of the backpropagation neural network and radial basis function neural network. The efficiencies of the proposed methods were evaluated by comparing the values of the mean absolute percentage error, the root-mean-square error, and the standard deviation error. It was found that the optimized models demonstrate the high efficiency of forecasting in comparison with actual meteorological data. The error estimation analysis showed that the error values for the optimized models are 4-5 times lower than those for the same models without optimization. It has been shown that FOA with decreasing step size for neural network improves accuracy and computational speed for short-term wind energy forecasts. This approach can be applied in programs for real wind farms and studied for other network parameters, such as weights and offsets.

A Cooperation of the Multileader Fruit Fly and Probabilistic Random Walk Strategies with Adaptive Normalization for Solving the Unconstrained Optimization Problems

A swarm-based nature-inspired optimization algorithm, namely, the fruit fly optimization algorithm (FOA), hasa simple structure and is easy to implement. However, FOA has a low success rate and a slow convergence, because FOA generates new positions around the best location, using a fixed search radius. Several improved FOAs have been proposed. However, their exploration ability is questionable. To make the search process smooth, transitioning from the exploration phase to the exploitation phase, this paper proposes a new FOA, constructed from a cooperation of the multileader and the probabilistic random walk strategies (CPFOA). This involves two population types working together. CPFOAs performance is evaluated by 18 well-known standard benchmarks. The results showed that CPFOA outperforms both the original FOA and its variants, in terms of convergence speed and performance accuracy. The results show that CPFOA can achieve a very promising accuracy, when compared with the well-known competitive algorithms. CPFOA is applied to optimize twoapplications: classifying the real datasets with multilayer perceptron and extracting the parameters of a very compact T-S fuzzy system to model the Box and Jenkins gas furnace data set. CPFOA successfully find parameters with a very high quality, compared with the best known competitive algorithms.

A hierarchical guidance strategy assisted fruit fly optimization algorithm with cooperative learning mechanism

The fruit fly optimization algorithm (FOA) has drawn enormous attention from researchers and practitioners in the computation intelligence domain for the benefits of simple implementation mechanism and few parameters tuning requirement of FOA. However, FOA is hard to adapt directly to address complex continuous problems. A hierarchical guidance strategy assisted fruit fly optimization algorithm with cooperative learning mechanism (HGCLFOA) is proposed in this study. The population is divided into elitist and inferior subpopulations with the fitness of objective function. The population center is re-designed as an elitist subpopulation to maintain the diversity of the population. In the olfaction search stage, the hierarchical guidance strategy is introduced for local search according to the difference of solution qualities to assign inferior individuals to elitist individuals on different levels. Meanwhile, the inferior information is applied by the inferior solutions repairing strategy to deflect the prediction of the elitist subpopulation for preventing HGCLFOA from falling into the local optimum. In the vision search stage, a hybrid Gaussian distribution estimation strategy is adopted to extract the elitist information of previous generations to predict the distribution of potential elitist individuals in the next generation. The exploration and exploitation of the HGCLFOA are balanced by the cooperation between elitist subpopulation and inferior subpopulation. A random walk strategy is activated to assist the elitist solutions to jump out the local optimal. The parameters of the HGCLFOA are calibrated by DOE and ANOVA methods. The experimental results demonstrated that the HGCLFOA outperformed the classical FOA and state-of-arts variants of FOA.

An Effective Heuristic for Multidepot Low-Carbon Vehicle Routing Problem

Low-carbon economy has been a hot research topic in recent years. This paper firstly considers the vehicle load weight, the key factors affecting the fuel consumption, to establish the fuel consumption model, and then constructs the vehicle routing planning model in the last mile delivery with multiple depots within time windows. In order to solve this problem, we improve the classical fruit fly algorithm which is easy to fall into the local optimum, and the improved fruit fly optimization algorithm is designed and integrated with genetic algorithm. Computational results show that our solution approach is capable of solving instances with up to 48 customers and 4 different depots. The effectiveness and efficiency of the model and multigroup fruit fly algorithm are verified through case study.

Tourist Demand Prediction Model Based on Improved Fruit Fly Algorithm

To accurately predict the development and change trend of the future, tourism market can effectively improve the planning and purpose of tourism development. In order to improve the accuracy of tourist demand prediction, this paper studies the tourist demand prediction model based on improved fruit fly algorithm. Aiming at the optimization defects of the traditional fruit fly optimization algorithm (FOA), the model introduces two concepts of sensitivity and pheromone, improves the optimization strategy and position replacement of fruit fly, improves the diversity of fruit fly population, modifies the global optimization characteristics of the algorithm, and improves the local search ability and search efficiency of the algorithm. By combining the improved AFOA with echo state network (ESN), a two-stage combined prediction model (AAFOA-ESN) is constructed. The experimental results show that the minimum prediction error accuracy of the model is only 0.55%, which has more robust prediction effect, faster convergence speed, and higher prediction accuracy.

IFFO: An Improved Fruit Fly Optimization Algorithm for Multiple Workflow Scheduling Minimizing Cost and Makespan in Cloud Computing Environments

Cloud computing platforms have been extensively using scientific workflows to execute large-scale applications. However, multiobjective workflow scheduling with scientific standards to optimize QoS parameters is a challenging task. Various metaheuristic scheduling techniques have been proposed to satisfy the QoS parameters like makespan, cost, and resource utilization. Still, traditional metaheuristic approaches are incompetent to maintain agreeable equilibrium between exploration and exploitation of the search space because of their limitations like getting trapped in local optimum value at later evolution stages and higher-dimensional nonlinear optimization problem. This paper proposes an improved Fruit Fly Optimization (IFFO) algorithm to minimize makespan and cost for scheduling multiple workflows in the cloud computing environment. The proposed algorithm is evaluated using CloudSim for scheduling multiple workflows. The comparative results depict that the proposed algorithm IFFO outperforms FFO, PSO, and GA.

A V2G scheduling strategy based on the fruit fly optimization algorithm

As electric vehicles enter people’s lives in large numbers, their behavior of disorderly charging and discharging will have a seriously negative impact on the safety and economic operation of the power system. This study proposes an electric vehicle charging and discharging load model under the constraint of joint benefits on the both power supply and demand sides. In this model, we set the minimum variance of grid load peak-to-valley values and the maximum benefit for users as the objective function, and take into account the randomness of electric vehicle starting charging time and duration as well as the grid capacity limitation. The main innovation point is that based on the fruit fly optimization algorithm, it proposes an intelligent orderly charging and discharging strategy with vehicle-to-grid (V2G). Following this, we compare several common charging models for electric vehicles with simulations, and conclude that the proposed optimization model can best reduce the target of peak-to-valley difference in grid load and also charging and discharging costs, thus demonstrating the feasibility and effectiveness of the model and strategy.

Research on Power Consumption Model of Permanent Magnet Direct Drive Belt Conveyor System Based on Fruit Fly Optimization Algorithm-Generalized Regression Neural Network-Particle Swarm Optimization

The permanent magnet direct drive electric drum is used to replace the driving device of the traditional belt conveyor, which simplifies the structure. However, the permanent magnet direct drive electric drum still operates in a high speed and stable state after starting, and cannot transport materials reasonably, belt speed cannot match the coal quantity reasonably; the problem of energy waste is still severe. Thus, based on fruit fly optimization algorithm-generalized regression neural network-particle swarm optimization algorithm, the power consumption network model of the permanent magnet direct drive belt conveyor system is established. The relationship among belt velocity, coal transport quantity and power is obtained, and the optimal belt velocity is found with the power consumption network model. Thus, the minimum power is obtained. The algorithm selects the optimal smoothing factor by fruit fly optimization algorithm, inputs the optimal smoothing factor into generalized regression neural network and establishes the optimal power consumption model. Then establishes the matching relationship of coal quantity, optimal power, and optimal belt speed in the power consumption model by the adaptive weight particle swarm optimization. The model is compared with the network model with smoothing factors of 0.7 and 0.4. The comparisons show that the optimized model performs better, which can be better applied to establish the energy consumption model of the system.

A Framework for Extractive Text Summarization Based on Deep Learning Modified Neural Network Classifier

There is an exponential growth of text data over the internet, and it is expected to gain significant growth and attention in the coming years. Extracting meaningful insights from text data is crucially important as it offers value-added solutions to business organizations and end-users. Automatic text summarization (ATS) automates text summarization by reducing the initial size of the text without the loss of key information elements. In this article, we propose a novel text summarization algorithm for documents using Deep Learning Modifier Neural Network (DLMNN) classifier. It generates an informative summary of the documents based on the entropy values. The proposed DLMNN framework comprises six phases. In the initial phase, the input document is pre-processed. Subsequently, the features are extracted using pre-processed data. Next, the most appropriate features are selected using the improved fruit fly optimization algorithm (IFFOA). The entropy value for every chosen feature is computed. These values are then classified into two classes, (a) highest entropy values and (b) lowest entropy values. Finally, the class that holds the highest entropy values is chosen, representing the informative sentences that form the last summary. The results observed from the experiment indicate that the DLMNN classifier gives 81.56, 91.21, and 83.53 of sensitivity, accuracy, specificity, precision, and f-measure. Whereas the existing schemes such as ANN relatively provide lesser value in contrast to DLMNN.

Edge user allocation by FOA in edge computing environment

In recent years, edge computing (EC) has been widely studied as a new computing paradigm which extends cloud computing. It paves the way to further reduce the network latency between IoT/mobile devices (referred to as edge users hereafter) and service providers by pushing services and corresponding data from clouds to nearby edge servers located nearby edge users. The edge user allocation (EUA) problem is a new issue in EC environment. It aims at optimizing strategies to allocate edge users to those edge servers while fulfilling specific constraints, e.g., budget constraint, coverage constraint, etc. As the EUA problem is NP-hard, effectively and efficiently solving it is still intractable. In this paper, we take allocating maximum edge users and employing minimum edge servers as objectives, then take both the proximity constraint and capacity constraint into account, and propose EUA-FOA, an Fruit fly Optimization Algorithm (FOA)-based approach, to solve the EUA problem. To extensively evaluate EUA-FOA's performance, we employ a widely used real-world dataset to conduct two sets of experiments, including small-scale EUA scenarios and large-scale EUA scenarios. We compare EUA-FOA against four representative approaches and the experimental results demonstrate that EUA-FOA is highly effective as it outperforms the state-of-the-art approaches significantly.

A new combined braking approach of magnetorheological fluids brake based on fuzzy controller and improved PID controller

With the aim to improve working performance of magnetorheological fluids (MRF) brake, a combined braking approach is proposed in this paper. A data acquisition system of MRF brake is designed and transfer function is identified for the device. Furthermore, based on the excitation current-extrusion pressure combined braking system, a fuzzy controller is designed to distribute the target torque. Moreover, through integration of fruit fly optimization algorithm (FOA) and particle swarm optimization algorithm (PSOA), an improved proportional-integral-differential (PID) controller is presented to conduct braking mechanism. Finally, the simulations and experiments are carried out. The results show that the combined braking approach with fuzzy controller and improved PID controller has characteristics of quick response and no overshoot, and superior to conventional PID and FOA PID controller.

Comparative Solutions of Exact and Approximate Methods for Traveling Salesman Problem

There are two major optimization methods: Exact and Approximate methods. A well known exact method, Branch and Bound algorithm (B&B) and approximate methods, Elimination-based Fruit Fly Optimization Algorithm (EFOA) and Artificial Atom Algorithm (A3) are used for solving the Traveling Salesman Problem (TSP). For 56 destinations, the results of total distance, processing time, and the deviation between exact and approximate method will be compared where the distance between two destinations is a Euclidean distance and this study shows that the distance of B&B is 270 , EFOA is 270 and A3 is 288.38 which deviates 6.81%. For time processing aspect, B&B needs 12.5 days to process, EFOA needs 36.59 seconds, A3 needs 35.34 seconds. But for 29 destinations, exact method is more powerful than approximate method.

Maintenance Personnel Optimization Model of Vehicle Equipment Based on Support Task

Vehicle equipment maintenance support tasks have problems such as low maintenance efficiency and unreasonable allocation of maintenance personnel. In order to further strengthen the theoretical research of vehicle equipment maintenance support, an optimization model of vehicle equipment maintenance personnel based on support task is proposed in this paper. Firstly, the maintenance workload model of vehicle equipment is constructed by analyzing the three task sources of vehicle equipment: scheduled maintenance, natural random failure, and combat damage. Then, considering the technical professional level, maintenance efficiency, and other factors of maintenance personnel, two optimization models of maintenance personnel are constructed. In view of the situation where there are enough human resources, the prediction model of the number of personnel with the minimum total number as the goal is constructed to achieve the purpose of saving human resources. Using MATLAB mixed integer nonlinear programming problem (MINP) toolbox to solve the prediction model of the number of personnel, in view of the shortage of maintenance personnel, a maintenance personnel allocation model aiming at minimizing maintenance time is constructed to maximize maintenance efficiency. In order to solve the model, the fruit fly optimization algorithm (FOA) is improved, and the group cooperation is used to update the fruit fly position. The new algorithm not only retains the essential advantages of the FOA but also solves the problem that the algorithm is easy to fall into local extreme value and improves the global optimization ability of the algorithm. Finally, two example simulations verify the effectiveness of the optimization method in this paper and provide a certain theoretical basis for maintenance personnel to optimize decision-making.

A Multi-Objective Enhanced Fruit Fly Optimization (MO-EFOA) Framework for Despeckling SAR Images using DTCWT based Local Adaptive Thresholding

ABSTRACT The importance of Satellite Aperture Radar (SAR) imagery systems is increasing day-by-day in various field such as earth observation, hi-technology war mechanisms, etc. The images captured by SAR imagery systems are mainly used to detect and classify objects captured in the images. Due to the complexity of the image capturing process, SAR images can be highly noisy – often consisting of multiplicative noise, also known as speckle. To detect or classify objects in SAR images, speckle noise must be removed from images. During despeckling process, the preservation of important information in the SAR images while removing noise such as edge or patterns is a crucial task. Despeckling methods are liable to compromise on edge preservation ability while aspiring for good quality denoised images. Many researchers have proposed wavelet transform based despeckling approaches such as Discrete Wavelet Transform (DWT), Undecimated Wavelet Transform (UDWT), Dual Tree Complex Wavelet Transform (DTCWT). In these approaches, finding the best values of the coefficients plays an important role in yielding excellent denoised images with preserved edges. In this paper, we have proposed a novel optimization framework that optimizes thresholding coefficients of DTCWT despeckling method for SAR images. The proposed optimization framework is based on Fruit Fly Optimization (FOA) algorithm. The approach is a multi-objective optimization algorithm that is used to find maximum values for Peak Signal-to-Noise Ratio (PSNR), Mean Structural Similarity Index (MSSIM) and Equivalent number of look (ENL). The maximum value of MSSIM indicates high edge preservation capacity, whereas maximum PSNR value indicates good quality denoising of images. The maximum ENL value represents a good speckle-noise smoothing capability. We have applied our framework over some classical images as well as over SAR images of MSTAR dataset. In our experiments, we found that our proposed framework results in the excellent PSNR 36.87 dB, 35.4 dB and 37.8 dB, MSSIM values 0.92, 0.93 and 0.92, respectively, in the case of lena image, MSTAR dataset image and TerraSAR-X dataset image.

An Improved Method for Distributed Localization in WSNs Based on Fruit Fly Optimization Algorithm

An Improved Method for Distributed Localization in WSNs Based on Fruit Fly Optimization Algorithm

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.