Fruit Fly Optimization Algorithm Research Articles

Large Data Oriented to Image Information Fusion Spark and Improved Fruit Fly Optimization Based on the Density Clustering Algorithm

The density-based applied spatial clustering algorithm is an algorithm based on high-density interconnected regions, which discovers class clusters of arbitrary shapes in noisy data sets and is widely used. However, it suffers from slow computation speed due to large-scale disk I/O and clustering bias due to uneven density class clusters and poor parameter search ability. To address these problems, a parallel density clustering algorithm based on an improved fruit fly optimization algorithm and Spark memory iteration is proposed. The proposed algorithm first divides the data grid using an irregular dynamic density region partitioning strategy. Then, a hybrid fruit fly particle swarm algorithm based on a genetic optimization mechanism is proposed to achieve dynamic optimization seeking for parameters in local clustering to improve the clustering effect of local clustering. Finally, the local merging of samples in irregularly bounded grid cells under each partition is achieved by designing a custom clustering merging strategy. The experiments show that the improved algorithm is generally applicable to the clustering of different shape class clusters and larger scale data and has obvious improvement in accuracy and parallel efficiency.

Comparison of various chemometric methods on visible and near-infrared spectral analysis for wood density prediction among different tree species and geographical origins.

Visible and near-infrared (Vis-NIR) spectroscopy has been widely applied in many fields for the qualitative and quantitative analysis. Chemometric techniques including pre-processing, variable selection, and multivariate calibration models play an important role to better extract useful information from spectral data. In this study, a new de-noising method (lifting wavelet transform, LWT), four variable selection methods, as well as two non-linear machine learning models were simultaneously analyzed to compare the impact of chemometric approaches on wood density determination among various tree species and geographical locations. In addition, fruit fly optimization algorithm (FOA) and response surface methodology (RSM) were employed to optimize the parameters of generalized regression neural network (GRNN) and particle swarm optimization-support vector machine (PSO-SVM), respectively. As for various chemometric methods, the optimal chemometric method was different for the same tree species collected from different locations. FOA-GRNN model combined with LWT and CARS deliver the best performance for Chinese white poplar of Heilongjiang province. In contrast, PLS model showed a good performance for Chinese white poplar collected from Jilin province based on raw spectra. However, for other tree species, RSM-PSO-SVM models can improve the performance of wood density prediction compared to traditional linear and FOA-GRNN models. Especially for Acer mono Maxim, when compared to linear models, the coefficient of determination of prediction set ( ) and relative prediction deviation (RPD) were increased by 47.70% and 44.48%, respectively. And the dimensionality of Vis-NIR spectral data was decreased from 2048 to 20. Therefore, the appropriate chemometric technique should be selected before building calibration models.

Robot Time-Optimal Trajectory Planning Based on Quintic Polynomial Interpolation and Improved Harris Hawks Algorithm

Time-optimal trajectory planning is one of the most important ways to improve work efficiency and reduce cost and plays an important role in practical application scenarios of robots. Therefore, it is necessary to optimize the running time of the trajectory. In this paper, a robot time-optimal trajectory planning method based on quintic polynomial interpolation and an improved Harris hawks algorithm is proposed. Interpolation with a quintic polynomial has a smooth angular velocity and no acceleration jumps. It has widespread application in the realm of robot trajectory planning. However, the interpolation time is usually obtained by testing experience, and there is no unified criterion to determine it, so it is difficult to obtain the optimal trajectory running time. Because the Harris hawks algorithm adopts a multi-population search strategy, compared with other swarm intelligent optimization algorithms such as the particle swarm optimization algorithm and the fruit fly optimization algorithm, it can avoid problems such as single population diversity, low mutation probability, and easily falling into the local optimum. Therefore, the Harris hawks algorithm is introduced to overcome this problem. However, because some key parameters in HHO are simply set to constant or linear attenuation, efficient optimization cannot be achieved. Therefore, the nonlinear energy decrement strategy is introduced in the basic Harris hawks algorithm to improve the convergence speed and accuracy. The results show that the optimal time of the proposed algorithm is reduced by 1.1062 s, 0.5705 s, and 0.3133 s, respectively, and improved by 33.39%, 19.66%, and 12.24% compared with those based on particle swarm optimization, fruit fly algorithm, and Harris hawks algorithms, respectively. In multiple groups of repeated experiments, compared with particle swarm optimization, the fruit fly algorithm, and the Harris hawks algorithm, the computational efficiency was reduced by 4.7019 s, 1.2016 s, and 0.2875 s, respectively, and increased by 52.40%, 21.96%, and 6.30%. Under the optimal time, the maximum angular displacement, angular velocity, and angular acceleration of each joint trajectory meet the constraint conditions, and their average values are only 75.51%, 38.41%, and 28.73% of the maximum constraint. Finally, the robot end-effector trajectory passes through the pose points steadily and continuously under the cartesian space optimal time.

Explainable Artificial Intelligence Enabled Intrusion Detection Technique for Secure Cyber-Physical Systems

A cyber-physical system (CPS) can be referred to as a network of cyber and physical components that communicate with each other in a feedback manner. A CPS is essential for daily activities and approves critical infrastructure as it provides the base for innovative smart devices. The recent advances in the field of explainable artificial intelligence have contributed to the development of robust intrusion detection modes for CPS environments. This study develops an Explainable Artificial Intelligence Enabled Intrusion Detection Technique for Secure Cyber-Physical Systems (XAIID-SCPS). The proposed XAIID-SCPS technique mainly concentrates on the detection and classification of intrusions in the CPS platform. In the XAIID-SCPS technique, a Hybrid Enhanced Glowworm Swarm Optimization (HEGSO) algorithm is applied for feature selection purposes. For intrusion detection, the Improved Elman Neural Network (IENN) model was utilized with an Enhanced Fruitfly Optimization (EFFO) algorithm for parameter optimization. Moreover, the XAIID-SCPS technique integrates the XAI approach LIME for better understanding and explainability of the black-box method for accurate classification of intrusions. The simulation values demonstrate the promising performance of the XAIID-SCPS technique over other approaches with maximum accuracy of 98.87%.

Research on Structure Design and Control Method of Magnetorheological Suspension Based on Improved Fruit Fly Optimization Algorithm

To reduce engine vibration, a semi-active controlled magnetorheological suspension is designed. First, the magnetic circuit of the new magnetorheological suspension is simulated. Furthermore, the non-parametric forward and inverse dynamic models of the magnetorheological suspension with the Elman neural network are established, where the modeling results and accuracy are analyzed. Second, according to the characteristics of semi-active vibration control of engine suspension, an improved fruit fly optimized proportional-integral-differential (PID) controller is proposed. Compared with the original fruit fly optimized PID controller and particle swarm optimization PID controller, the control strategies based on the proposed methods are simulated by MATLAB. Then, a vibration control experiment is carried out under different control strategies. The simulation and experiment results show that the PID controller based on the improved fruit fly optimization algorithm has the advantages of short adjustment time and fast dynamic response.

Self-Calibration Method for the Errors of Dual-Axis Turntable in Redundant Rotational Inertial Navigation System

Attitude measurement is an important capability of a redundant rotational inertial navigation system (RRINS). The attitude of RRINS is converted from the attitude of the redundant inertial measurement unit (RIMU) using a turntable. The attitude measurement accuracy of RRINS is reduced greatly by turntable errors because the turntable is rotated frequently when the RRINS is working. Therefore, it is important to calibrate the turntable errors for improving the attitude measurement accuracy of RRINS. In this study, a self-calibration method for the errors of a dual-axis turntable in RRINS is proposed. First, an error model of the dual-axis turntable is established, and the influence of turntable errors on RRINS attitude measurement is analyzed. Second, a self-calibration method, including rotation scheme, numerical algorithm (NA) errors calculation, and fruit fly optimization algorithm (FOA) errors estimation, is proposed. The influence of Earth’s rotational rate and gyroscope biases is eliminated during calibration using the designed rotation scheme, NA calculates the errors directly when they are minor angles, and FOA estimates the errors whether they are minor or large angles. The simulations and experiment results show that the proposed method can improve the attitude measurement accuracy of RRINS.

A hybrid fruit fly optimization algorithm based on three dimensional space

The original fruit fly optimization algorithm (FOA) for the optimal solution in two dimensional space has been proposed to address complex optimization problems in a myriad of domains, such as production scheduling, path planning, financial investment, and software engineering. However, FOA is confronted with two defects unsolved. One is that the convergence speed is relatively low and the other is that the convergence accuracy is relatively low. In this paper, to solve the abovementioned two problems and improve the original FOA, we firstly propose a novel hybrid fruit fly optimization algorithm after due diligence, known as HFOA-3D based on three dimensional space, which takes advantage of crossover operator and mutation operator of genetic algorithm. We also conduct an experiment including five classic test functions to evaluate the performance of HFOA-3D. Finally, the experiment result shows that HFOA-3D outperforms the original FOA in terms of convergence speed and convergence accuracy.

Short-term electrical load forecasting using hybrid model of manta ray foraging optimization and support vector regression

Demand prediction is playing a progressively important role in electricity management, and is fundamental to the corresponding decision-making. Because of the high variability of the increased electrical load, and of the new renewable energy technologies, power systems are facing technical challenges. Thus, short-term forecasting has a crucial utility for generating dispatching commands, managing the spot market, and detecting anomalies. Accordingly, in this work, a hybrid method that optimizes the parameters of a support vector machine using Manta ray foraging optimization is proposed for short-term load forecasting. To evaluate the accuracy of the proposed method, five other optimizers, including the Slime Mould algorithm, Tug of War optimization, Moth Flame optimization, the Satin Bowerbird optimizer, and the Fruit-fly optimization algorithm, are used for evaluation of the proposed method's superiority. By conducting a case study based on actual data, the performance of all methods is investigated using various statistical indexes. Results show that using the hybridized technique can address the disadvantages and weaknesses of single methods. For example, based on the train and test datasets, the coefficient of determination (R2) values of the proposed SVR-MRFO method for electrical loads are 0.999 and 0.993, respectively.

A Novel Codebook Generation by Smart Fruit Fly Algorithm based on Exponential Flight

A codebook is a combination of vectors that represents a digital image best and very useful tool for compression. Besides the well-known techniques such as Linde-Buzo-Gray, C-Means, and Fuzzy C-Means the nature-inspired metaheuristic algorithms have also become alternate techniques for solving the codebook generation problem. Fruit Fly Optimization Algorithm (FFA) is a simple and efficient algorithm, but the capturing of an agent by a local minimum point is the main problem. Therefore, the fruit flies generally do not reach the global solution at the end of the iterations. In this study, the FFA is empowered with a smart exponential flight approach to finding out a global optimum codebook. In this approach, if a fruit fly agent is captured by a local minimum point accidentally, the smart exponential flight steps provide an opportunity to escape from it easily. In the experimental studies, successful compression results have been taken in terms of lower error rates. The numerical results prove that the proposed Smart Exponential flight-based Fruit Fly Algorithm (SE-FFA) is better than the variations of convolutional FFA by providing a global optimum codebook.

Multi-level thresholding image segmentation for rubber tree secant using improved Otsu's method and snake optimizer.

The main disease that decreases the manufacturing of natural rubber is tapping panel dryness (TPD). To solve this problem faced by a large number of rubber trees, it is recommended to observe TPD images and make early diagnosis. Multi-level thresholding image segmentation can extract regions of interest from TPD images for improving the diagnosis process and increasing the efficiency. In this study, we investigate TPD image properties and enhance Otsu's approach. For a multi-level thresholding problem, we combine the snake optimizer with the improved Otsu's method and propose SO-Otsu. SO-Otsu is compared with five other methods: fruit fly optimization algorithm, sparrow search algorithm, grey wolf optimizer, whale optimization algorithm, Harris hawks optimization and the original Otsu's method. The performance of the SO-Otsu is measured using detail review and indicator reviews. According to experimental findings, SO-Otsu performs better than the competition in terms of running duration, detail effect and degree of fidelity. SO-Otsu is an efficient image segmentation method for TPD images.

Securing wireless sensor network from node capture attack using an efficient optimization defense strategy model

Wireless Sensor Networks (WSNs) are a promising evolving technology for recent real-world applications in collaborative settings. These applications ranging from household monitoring to crucial military applications transmit critical data and thus more challenging approaches are required for providing security. In WSNs, a node capture attack (NCA) is a more serious attack among various attacks which supports an impostor to perform different network (NW) operations, and thereby the security of the entire NW is compromised easily. For conquering the surpassing issues of sensitivity-related techniques and for enhancing the NCA’s AE, we establish the NCA technique amalgamating several objectives like speed and location to lessen comprehensive intricacy. For securing the NW out of the NCA, this study proffers Improved Flower Pollination Optimization Defense Strategy Algorithm (IFPODSA) intending to compute the multiple objective functions for effectual chief administration procedure employing Hankel matrix formation. This proffered IFPODSA is correlated with two prevailing methodologies like Fruit Fly Optimization Algorithm (FFOA) and Finding Robust Assailant Optimization-Particle Swarm Optimization and Genetic Algorithm (FRAOPSOGA) concerning diverse criteria. Consequently, it has been observed that the proffered IFPODSA attains 458.7 kbps of throughput, 81.56% of PDR, 18.2% of energy consumption, and 64.36% of overhead.

A novel binary multi-swarms fruit fly optimisation algorithm for the 0-1 multidimensional knapsack problem

A novel binary multi-swarms fruit fly optimisation algorithm for the 0-1 multidimensional knapsack problem

An Adaptive Fruit Fly Optimization Algorithm for Optimization Problems

An Adaptive Fruit Fly Optimization Algorithm for Optimization Problems

An Approach for Identifying Network Intrusion in an Automated Process Control Computer System

Technology and networks have improved significantly in recent decades, and Internet services are now available in almost every business. It has become increasingly important to develop information security technology to identify the most recent attack as hackers are getting better at stealing information. The most important technology for security is an Intrusion Detection System (IDS) which employs machine learning and deep learning technique to identify network irregularities. To detect an unknown attack, we propose to use a new intrusion detection system using a deep neural network methodology which provides excellent performance to detect intrusion. This research focuses on an automated process control computer system that recognizes, records, analyzes, and correlates threats to online safety. In addition, two different methods are used to detect an attack (the binary classification and the multiclass classification). One of the most promising features of the proposed technique is its accuracy (98.99 percent with the multiclass classification and the binary classification). The proposed method's first step creates a model for a multiclass intrusion detection system based on CNN. FOA (Fruit Fly Optimization Algorithm) is used in the process's pre-training phase to address the class imbalance issue. Each batch is obtained during the training process using the resampling method following the resampling weights, which are the results of the pre-training procedure.

Reconstruction Layout Optimization of Multivariety and Small Batch Workshop in Aerospace Industry.

To cope with the problems of frequent mold changes, long production cycles and serious logistics crossings in workshop of aerospace enterprise. First, a manufacturing cell layout planning method based on the feature bit code domain method and K-Means++ is proposed to realize the accurate division of manufacturing cells. Then, a multiobjective optimization method of dynamic reconstruction layout based on improved fruit fly optimization algorithm (IFOA) is proposed to solve the reconstruction layout optimization of the production workshop problem with the optimization objectives of logistics cost, reconstruction cost, loss cost, and cell integrated area. Finally, plant simulation software is applied to simulate the workshop layout before and after optimization. The simulation results show that the logistics cost of the workshop cell layout after optimization is reduced by 8.7%, the utilization rate of the workshop area is improved by 5.2%, and the value-added rate of products is increased by 6.6%, which verifies the effectiveness and feasibility of the proposed model and method.

A feature-level degradation measurement method for composite health index construction and trend prediction modeling

Accurate measurement of degradation levels and evolution trend for mechanical equipment has great significance to achieve the goal of condition-based maintenance (CBM) and increase the economic benefit. In this paper, a feature-level degradation measurement method is proposed for composite health index (CHI) construction and trend prediction, systematically blending the multi-source signals analysis theory and deep learning technology. First, a CHI is innovatively constructed based on a novel hybrid stacked auto-encoder (HSAE) and self-organizing map (SOM) network to comprehensively evaluate the degradation levels, which achieves the feature-level data fusion for degradation measurement. Second, considering the significant characteristics of strong nonlinear and random fluctuations for the generated CHI sequence, a hybrid algorithm, combining the variational mode decomposition (VMD) and a dynamic step size-based fruit fly optimization algorithm (DSSFOA), is applied for effectively extracting the multiscale frequency components from raw sequence and capturing the inherent laws of health degradation from different scales. Specifically, by designing a hierarchical search structure with dynamic step sizes in DSSFOA, the parameters of VMD can be optimized with high efficiency and accuracy. Finally, a scale-adaptive attention (SAA)-based bidirectional long short-term memory (BiLSTM) network and the corresponding dual-stage training strategy are developed to adaptively estimate the attention weights of different components and further predict the degradation trend in future. Extensive experiments on the C-MAPSS turbofan engine dataset and the PSU dataset validate the more superior performance of the proposed method on both of health index construction and degradation trend prediction than other existing methods.

A novel elitist fruit fly optimization algorithm

A novel elitist fruit fly optimization algorithm

Prediction and determination of mildew grade in grain storage based on FOA-SVM algorithm

AbstractGrain mildew is a significant hazard that causes food loss and poses a serious threat to human health when severe. Therefore, effective prediction and determination of mildew grade is essential for the prevention and control of mildew and global food security. In the present study, a model for predicting and determining the mildew grade of rice was constructed using logistic regression, back propagation neural network and GS-SVM (a grid search-based support vector machine algorithm) based on laboratory culture data and actual data from a granary, respectively. The results show that the GS-SVM model has a better prediction effect, but the model cannot automatically adjust the parameters and is more subjective, and the accuracy may decrease when the data set changes. Therefore, this paper establishes a new model for a support vector machine based on a fruit fly optimization algorithm (FOA-SVM), which can achieve automatic parameter search and automatically adjust its parameters to find the best result when the data set changes, with a strong ability of self-adjustment of parameters. In addition, the FOA-SVM converges quickly and the model is stable. The results of this study provide a technical method for early identification of mildew grade during grain storage, which is beneficial for the prevention and control of rice mildew during grain storage.

Robust Deep Learning Empowered Real Time Object Detection for Unmanned Aerial Vehicles based Surveillance Applications

Surveillance is a major stream of research in the field of Unmanned Aerial Vehicles (UAV), which focuses on the observation of a person, group of people, buildings, infrastructure, etc. With the integration of real time images and video processing approaches such as machine learning, deep learning, and computer vision, the UAV possesses several advantages such as enhanced safety, cheap, rapid response, and effective coverage facility. In this aspect, this study designs robust deep learning based real time object detection (RDL-RTOD) technique for UAV surveillance applications. The proposed RDL-RTOD technique encompasses a two-stage process namely object detection and objects classification. For detecting objects, YOLO-v2 with ResNet-152 technique is used and generates a bounding box for every object. In addition, the classification of detected objects takes place using optimal kernel extreme learning machine (OKELM). In addition, fruit fly optimization (FFO) algorithm is applied for tuning the weight parameter of the KELM model and thereby boosts the classification performance. A series of simulations were carried out on the benchmark dataset and the results are examined under various aspects. The experimental results highlighted the supremacy of the RDL-RTOD technique over the recent approaches in terms of several performance measures.

Employing Quantum Fruit Fly Optimization Algorithm for Solving Three-Dimensional Chaotic Equations

In a chaotic system, deterministic, nonlinear, irregular, and initial-condition-sensitive features are desired. Due to its chaotic nature, it is difficult to quantify a chaotic system’s parameters. Parameter estimation is a major issue because it depends on the stability analysis of a chaotic system, and communication systems that are based on chaos make it difficult to give accurate estimates or a fast rate of convergence. Several nature-inspired metaheuristic algorithms have been used to estimate chaotic system parameters; however, many are unable to balance exploration and exploitation. The fruit fly optimization algorithm (FOA) is not only efficient in solving difficult optimization problems, but also simpler and easier to construct than other currently available population-based algorithms. In this study, the quantum fruit fly optimization algorithm (QFOA) was suggested to find the optimum values for chaotic parameters that would help algorithms converge faster and avoid the local optimum. The recommended technique used quantum theory probability and uncertainty to overcome the classic FA’s premature convergence and local optimum trapping. QFOA modifies the basic Newtonian-based search technique of FA by including a quantum behavior-based searching mechanism used to pinpoint the position of the fruit fly swarm. The suggested model has been assessed using a well-known Lorenz system with a specified set of parameter values and benchmarked signals. The results showed a considerable improvement in the accuracy of parameter estimates and better estimation power than state-of-the art parameter estimation approaches.