Fruit Fly Optimization Algorithm Research Articles

Fruit-Fly optimization based feature integration in image retrieval.

The content-based image retrieval (CBIR) system searches and retrieves the similar images from the huge database using the significant features extracted from the image. Feature integration techniques used in the CBIR system assign static weights to each feature involved in the retrieval process that gives a smaller number of similar images as a result. Moreover, the retrieval time of the CBIR system increases due to the entire database search. To overcome this disadvantage the proposed work introduced a two-level searching process in the CBIR system. The initial level of the proposed framework uses the image selection rule to select more relevant images for the second-level process. The second level of the framework takes the proposed dominant color and radial difference pattern details from the query and selected images. By using color and texture features of the selected images, similarity measure is calculated. The proposed work assigns optimal dynamic weight to the similarity measure of color and texture features using the fruit fly optimization algorithm. This improves the retrieval performance of the CBIR system.

Scheduling parallel-batching processing machines problem with learning and deterioration effect in fuzzy environment

In this paper, a problem of scheduling jobs with different sizes and fuzzy processing times (FPT) on non-identical parallel batch machines to minimize makespan is investigated. Moreover, the processing time (PT) of each batch is subject to the location-based learning and total-PT-based deterioration effect. Since this is an NP-hard combinatorial optimization problem, an improved intelligent algorithm based on fruit fly optimization algorithm (IFOA) is proposed. To verify the performance of the algorithm, the IFOA is compared with three state-of-the-art algorithms. The comparative results demonstrate that the proposed IFOA outperforms the other compared algorithms.

A Novel Hybrid Filter/Wrapper Feature Selection Approach Based on Improved Fruit Fly Optimization Algorithm and Chi-square Test for High Dimensional Microarray Data

Background: Microarray data is widely utilized for disease analysis and diagnosis. However, it is hard to process them directly and achieve high classification accuracy due to the intrinsic characteristics of high dimensionality and small size samples. As an important data preprocessing technique, feature selection is usually used to reduce the dimensionality of some datasets. Methods: Given the limitations of employing filter or wrapper approaches individually for feature selection, in the study, a novel hybrid filter-wrapper approach, CS_IFOA, is proposed for high dimensional datasets. First, the Chi-square Test is utilized to filter out some irrelevant or redundant features. Next, an improved binary Fruit Fly Optimization algorithm is conducted to further search the optimal feature subset without degrading the classification accuracy. Here, the KNN classifier with the 10-fold-CV is utilized to evaluate the classification accuracy. Results: Extensive experimental results on six benchmark biomedical datasets show that the proposed CS-IFOA can achieve superior performance compared with other state-of-the-art methods. The CS-IFOA can get a smaller number of features while achieving higher classification accuracy. Furthermore, the standard deviation of the experimental results is relatively small, which indicates that the proposed algorithm is relatively robust. Conclusion: The results confirmed the efficiency of our approach in identifying some important genes for high-dimensional biomedical datasets, which can be used as an ideal pre-processing tool to help optimize the feature selection process, and improve the efficiency of disease diagnosis.

Two-way distributed sequential pattern mining using fruit fly algorithm along with Hadoop and map reduce framework

Data mining is an effective tool used to take out information from big data as it provides several benefits to conquer the restrictions in it. In this paper, we present an innovative procedure developed using an updown directed acyclic graph (UDDAG) with fruit fly optimisation algorithm (FFOA), which is based on sequential pattern (SP) mining algorithm. In this work, the distributed sequential model mining algorithm is used to diminish the scanning time and scalability and the transferred database is employed to optimise the memory storage. The proposed method is used to expand the sequences in both the ends (prefixes and suffixes) of the identified model thereby supplying the consequences in quicker model expansion resulting in fewer database projections when compared to conventional methods. Our proposed method is implemented in Hadoop distributed surroundings to resolve the scalability issues and executed on JAVA platform using big datasets with Hadoop and map reduce framework.

Two Way Distributed Sequential Pattern Mining using Fruitfly Algorithm along with Hadoop and Map Reduce Frame Work

Data mining is an effective tool used to take out information from big data as it provides several benefits to conquer the restrictions in it. In this paper, we present an innovative procedure developed using an updown directed acyclic graph (UDDAG) with fruit fly optimisation algorithm (FFOA), which is based on sequential pattern (SP) mining algorithm. In this work, the distributed sequential model mining algorithm is used to diminish the scanning time and scalability and the transferred database is employed to optimise the memory storage. The proposed method is used to expand the sequences in both the ends (prefixes and suffixes) of the identified model thereby supplying the consequences in quicker model expansion resulting in fewer database projections when compared to conventional methods. Our proposed method is implemented in Hadoop distributed surroundings to resolve the scalability issues and executed on JAVA platform using big datasets with Hadoop and map reduce framework.

Design of Online Learning Early Warning System based on Learning Behavior Analysis

The objective of this study is to design an academic warning system to identify students with abnormal learning behaviours, to give early warning of students' learning status, and to help students successfully complete their studies. In this study, the comprehensive academic performance of university students is taken as the research object, and the basic information data, academic performance data, and online data are collected. After analysing the students' learning behaviour, an online learning warning system is constructed based on grid based on clustering by fast search and find of density peaks (GBCFSFDP) algorithm, and the system is divided into dynamic warning and static warning. It found that the clustering of students' learning behaviour completed by GBCFSFDP algorithm can be divided into six specific categories, and the attributes that influence the learning performance can be differentiated by weighted naive Bayes classification algorithm based on fruit fly optimisation algorithm (WNBC-FOA). The results show that the time spent online have the greatest impact on students' performance. Therefore, this online learning early warning system can make it easier for students and teachers to understand the reasons for learning abnormalities and the correlation between different behaviours and academic performance, so as to make corresponding improvements.

Design of an online learning early warning system based on learning behaviour analysis

The objective of this study is to design an academic warning system to identify students with abnormal learning behaviours, to give early warning of students' learning status, and to help students successfully complete their studies. In this study, the comprehensive academic performance of university students is taken as the research object, and the basic information data, academic performance data, and online data are collected. After analysing the students' learning behaviour, an online learning warning system is constructed based on grid based on clustering by fast search and find of density peaks (GBCFSFDP) algorithm, and the system is divided into dynamic warning and static warning. It found that the clustering of students' learning behaviour completed by GBCFSFDP algorithm can be divided into six specific categories, and the attributes that influence the learning performance can be differentiated by weighted naive Bayes classification algorithm based on fruit fly optimisation algorithm (WNBC-FOA). The results show that the time spent online have the greatest impact on students' performance. Therefore, this online learning early warning system can make it easier for students and teachers to understand the reasons for learning abnormalities and the correlation between different behaviours and academic performance, so as to make corresponding improvements.

A Mobile Robotic 3-D Measurement Method Based on Point Clouds Alignment for Large-Scale Complex Surfaces

Large-scale complex surfaces are the core components of large aircraft, ships, and wind turbine generators. High-accuracy manufacturing of the components is very crucial for its performance. Automated and high-accuracy large-scale 3-D measurement is an effective means for the manufacturing accuracy control of these components. This article proposes an automated mobile robotic profile measurement method for large-scale complex surfaces, and it also proposes a high-accuracy point clouds alignment algorithm to improve the measurement accuracy. A fringe projection scanner, integrated on the end of a mobile robot, is utilized to scan the surface and obtain the local scanning point clouds. The local scanning point clouds' coarse alignment is implemented using the robot's joint data and the laser sensor's positioning data. After that, a fine alignment algorithm combining the fruit fly optimization algorithm (FOA) with the improved iterative closest point (IICP) algorithm is proposed for point clouds' fine alignment. Practical measurement was performed on a sectional wind turbine blade, and a broad region of 4.6 m ×2.6 m was measured. An accuracy evaluation method based on point-to-plane distance was adopted to evaluate the measurement error. The result that the mean distance error is less than 0.4 mm shows the proposed mobile robotic measurement system and the aligning method are of high accuracy.

Hybrid model of support vector regression and fruitfly optimization algorithm for predicting ski-jump spillway scour geometry

ABSTRACT Accurate prediction of the scour hole depth and dimensions downstream of ski-jump spillways has been an important issue among hydraulic researchers for decades. In recent years, computing methods such as Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference Systems (ANFISs) and Support Vector Regression (SVR) have shown a powerful performance in the prediction of scour characteristics owing to their flexibility and learning nature. In the present paper, a new hybrid approach has been proposed for the first time in order to improve the estimation power of the SVR tool for scour hole geometry prediction below ski-jump spillways. The principal characteristics of the scour hole pattern in the equilibrium phase have been predicted using SVR optimized with Fruitfly Optimization Algorithms (FOAs). The hybrid model is compared with the corresponding simple SVR model. To evaluate the proposed hybrid model further, it is also compared with other machine learning and empirical methods, such as ANNs, ANFISs and regression equations. The results show that the proposed SVR-FOA method performs well, improves remarkably on Support Vector Machines (SVMs) results, estimates scour hole geometrical parameters more accurately than the simple SVR model, and can be applied as an alternative reliable scheme for estimations on which simple SVR and other methods demonstrate shortcomings. The proposed hybrid method improves the precision level for scour depth prediction by about 8% compared with simple SVM in terms of the correlation coefficient.

Study on the application of BP neural network in air quality prediction based on adaptive chaos fruit fly optimization algorithm

BP neural network is optimized by improved drosophila algorithm, and a prediction model for air quality in Nanchang is established based on the air quality data and meteorological data of Nanchang city in recent three years. The experimental results show that the improved algorithm has improved performance compared with the BP algorithm, and has improved accuracy 4%, with a small difference in time consumption. The performance of the indirect prediction method is slightly better than that of the direct prediction method

A Hybrid Coordinated Design Method for Power System Stabilizer and FACTS Device Based on Synchrosqueezed Wavelet Transform and Stochastic Subspace Identification

The occurrence of low-frequency electromechanical oscillations is a major problem in the effective operation of power systems. The scrutiny of these oscillations provides substantial information about power system stability and security. In this paper, a new method is introduced based on a combination of synchrosqueezed wavelet transform and the stochastic subspace identification (SSI) algorithm to investigate the low-frequency electromechanical oscillations of large-scale power systems. Then, the estimated modes of the power system are used for the design of the power system stabilizer and the flexible alternating current transmission system (FACTS) device. In this optimization problem, the control parameters are set using a hybrid approach composed of the Prony and residual methods and the modified fruit fly optimization algorithm. The proposed mode estimation method and the controller design are simulated in MATLAB using two test case systems, namely IEEE 2-ar-ea 4-generator and New England-New York 68-bus 16-genera-tor systems. The simulation results demonstrate the high performance of the proposed method in estimation of local and interarea modes, and indicate the improvements in oscillation damping and power system stability.

Gene selection and classification combining information gain ratio with fruit fly optimisation algorithm for single-cell RNA-seq data

There are a wide range of genes in single-cell data, but some are not beneficial to classification. In order to eliminate these redundant genes and select beneficial genes, this study first utilise...

Gene selection and classification combining information gain ratio with fruit fly optimisation algorithm for single-cell RNA-seq data

Gene selection and classification combining information gain ratio with fruit fly optimisation algorithm for single-cell RNA-seq data

Parking diversion method based on Internet of Things and multi-word combination

Abstract With the continuous improvement of living standards, motor vehicles have become an indispensable transportation in our daily life. The number of cars in each city continues to rise, causing variable degrees of parking difficulties in different regions. It has become a common phenomenon that no parking spot near popular places. With this, there are long queues of vehicles in front of the parking lot, which delays the time of car owners and causes traffic jams. This article introduces a new parking diversion method, based on the support vector machine (SVM) and simulated annealing fruit fly optimization algorithm forecasting models to predict short-term traffic flow near the parking lot. Through predictions, recommending parking lots with low traffic flow for car owners. Meanwhile, the method based on the Internet of Thing to lock pots, let vehicles enter according to the queue number and so on. Through this way, achieving online queuing and advance reservation. This method can solve the queuing phenomenon in front of the parking lots and give car owners the most suitable travel time, therefore, park will be more orderly and diversion will be realized.

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

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

A bioinformatic variant fruit fly optimizer for tackling optimization problems

The fruit fly optimization algorithm (FOA) is a swarm-based algorithm inspired by fruit flies’ food search behaviors in nature. The conventional FOA is attracting widespread interests due to its briefness, and simplicity in structure. However, FOA still has some disadvantages presented in the exploration and exploitation abilities when it is used to solve different optimization problems. To optimize these drawbacks, the performance FOA can be improved by employing different operators that can help it explore more promising areas in the search space and in finding better solutions in the local area of the candidate optimal solutions. In this paper, an improved FOA approach (called BSSFOA) that employs (1) bat sonar strategy to strengthen the exploration, (2) hybrid distribution that combined Gaussian distribution with student distribution to enhance the exploitation is proposed. In BSSFOA, the FOA uses the bat sonar strategy to search for the global optima, while the hybrid distribution mechanism is used to search the local area of the global optima in the hope of finding better solutions. To assess the performance of the proposed approach, a comprehensive set of 30 benchmark functions was used with the continuous version of the BSSFOA. Moreover, a discrete version of BSSFOA was proposed as a searching mechanism in the feature selection process, where 17 well-known datasets were used to assess the ability of the BSSFOA to search the best performing features among these datasets. The obtained results reveal the superiority of the BSSFOA in solving both continuous and discrete optimization problems. Therefore, it can be concluded that the employed mechanisms have constructive impacts in mitigating the core problems of FOA.

An upgraded fruit fly optimisation algorithm for solving task scheduling and resource management problem in cloud infrastructure

In this manuscript, an upgraded Fruit Fly Optimization Algorithm (FFOA) is proposed for optimising task scheduling and resource management processes. The proposed FOA algorithm is utilized to address the issues. In the proposed algorithm, every solution is represented by fruit fly. Every fruit fly upgrades their status through well-organized smell search process. First, a basic approach is put forward to allocate every task for numerous resources and execution time is measured for every task. Second, the overloaded virtual machines (VMs) are identified and the load is balanced to obtain optimal system resource utilisation. The ability of Fly Task Scheduling Algorithm is to schedule the VMs execution time of tasks is minimal. The results of the Fruit Fly-based algorithms such as Fruit Fly Task Scheduling Algorithm, Modified Fruit Fly Task Scheduling Algorithm, Improved Fruit Fly Task Scheduling Algorithm and Multi-Swarm Fruit Fly Task Scheduling Algorithm (MSFFTSA) are proposed and analysed. The upgraded Fruit Fly Optimization algorithm of MSFFTSA is compared with different Fruit Fly algorithms. Finally, the proposed algorithm is compared with other algorithms and the experimental results shows that the proposed MSFFTSA technique is better than the other algorithms of the fruit fly algorithms.

Renewable energy system based on IFOA-BP neural network load forecast

In order to improve wind energy utilization and the accuracy of load forecasting, a wind turbine model was established and parameters were optimized. The back propagation neural network based on improved fruit fly optimization algorithm (IFOA-BP) is applied to load forecasting. Aiming at the optimization problem that the fruit fly optimization algorithm is easy to fall into the local or global optimum during the optimization process, use the improved fruit fly optimization algorithm to increase the search distance first to improve the diversity of the fruit fly optimization algorithm population, and then reduces the search distance, to enhance its search ability. Taking the load data of a power plant as an example, the algorithm was simulated and analyzed in matlab/simulink. The simulation results show that the algorithm can improve the prediction accuracy of wind energy and load.

Bodacious-Instance Coverage Mechanism for Wireless Sensor Network

Due to unavoidable environmental factors, wireless sensor networks are facing numerous tribulations regarding network coverage. These arose due to the uncouth deployment of the sensor nodes in the wireless coverage area that ultimately degrades the performance and confines the coverage range. In order to enhance the network coverage range, an instance (node) redeployment-based Bodacious-instance Coverage Mechanism (BiCM) is proposed. The proposed mechanism creates new instance positions in the coverage area. It operates in two stages; in the first stage, it locates the intended instance position through the Dissimilitude Enhancement Scheme (DES) and moves the instance to a new position, while the second stage is called the depuration, when the moving distance between the initial and intended instance positions is sagaciously reduced. Further, the variations of various parameters of BiCM such as loudness, pulse emission rate, maximum frequency, grid points, and sensing radius have been explored, and the optimized parameters are identified. The performance metric has been meticulously analyzed through simulation results and is compared with the state-of-the-art Fruit Fly Optimization Algorithm (FOA) and, one step above, the tuned BiCM algorithm in terms of mean coverage rate, computation time, and standard deviation. The coverage range curve for various numbers of iterations and sensor nodes is also presented for the tuned Bodacious-instance Coverage Mechanism (tuned BiCM), BiCM, and FOA. The performance metrics generated by the simulation have vouched for the effectiveness of tuned BiCM as it achieved more coverage range than BiCM and FOA.

Fruit Fly Optimization Algorithm Based on Single-Gene Mutation for High-Dimensional Unconstrained Optimization Problems

The fruit fly optimization (FFO) algorithm is a new swarm intelligence optimization algorithm. In this study, an adaptive FFO algorithm based on single-gene mutation, named AFFOSM, is designed to aim at inefficiency under all-gene mutation mode when solving the high-dimensional optimization problems. The use of a few adaptive strategies is core to the AFFOSM algorithm, including any given population size, mutation modes chosen by a predefined probability, and variation extents changed with the optimization progress. At first, an offspring individual is reproduced from historical best fruit fly individual, namely, elite reproduction mechanism. And then either uniform mutation or Gauss mutation happens by a predefined probability in a randomly selected gene. Variation extent is dynamically changed with the optimization progress. The simulation results show that AFFOSM algorithm has a better accuracy of convergence and capability of global search than the ESSMER algorithm and several improved versions of the FFO algorithm.