Improved Fruit Fly Optimization Research Articles

An Effective Financial Crisis Early Warning Model Based on an IFOA-BP Neural Network

<p>Existing financial crisis models have weak effects and low accuracy in prediction. We propose a financial crisis early warning model based on the improved fruit fly optimization algorithm-back-propagation neural network (IFOA-BP). First, we select listed enterprises in the manufacturing industry as the research object and use quantum computing to select financial crisis indicators. In view of the shortcomings of the FOA, which has low convergence accuracy and easily falls into the local optimum, we propose the IFOA, which introduces orthogonal experimental arrays in the initialization of the population to improve the diversity of the solutions and optimizes the individual crossing to avoid the algorithm falling into the local optimum. Finally, the IFOA is used for the optimization of the BP neural network’s weights and thresholds to improve the model prediction performance. In the simulation experiments, we verify the IFOA’s performance with the benchmark function. In the simulation experiments, we verify that the IFOA has good performance, and the IFOA-BP model has a good advantage over the ACO-BP, PSO-BP, FOA-BP, WOA-BP, and CSO-BP models in terms of MSE, MAE, and MAPE indices.</p> <p> </p>

Deep Learning Enabled Microarray Gene Expression Classification for Data Science Applications

In bioinformatics applications, examination of microarray data has received significant interest to diagnose diseases. Microarray gene expression data can be defined by a massive searching space that poses a primary challenge in the appropriate selection of genes. Microarray data classification incorporates multiple disciplines such as bioinformatics, machine learning (ML), data science, and pattern classification. This paper designs an optimal deep neural network based microarray gene expression classification (ODNN-MGEC) model for bioinformatics applications. The proposed ODNN-MGEC technique performs data normalization process to normalize the data into a uniform scale. Besides, improved fruit fly optimization (IFFO) based feature selection technique is used to reduce the high dimensionality in the biomedical data. Moreover, deep neural network (DNN) model is applied for the classification of microarray gene expression data and the hyperparameter tuning of the DNN model is carried out using the Symbiotic Organisms Search (SOS) algorithm. The utilization of IFFO and SOS algorithms pave the way for accomplishing maximum gene expression classification outcomes. For examining the improved outcomes of the ODNN-MGEC technique, a wide ranging experimental analysis is made against benchmark datasets. The extensive comparison study with recent approaches demonstrates the enhanced outcomes of the ODNN-MGEC technique in terms of different measures.

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.

Follicles Classification To Detect Polycystic Ovary Syndrome Using Glcm And Novel Hybrid Machine Learning

A complex endocrine disorder is Polycystic ovary syndrome (PCOS). Women’s health is seriously affected by this. Female’s fertile nature is hindered by this ad many more issues are caused by this. Ultrasound can be used for detecting Polycystic Ovaries. Females should be aware of this disease to leads to a good life. In general, around 70% of this cases are undiagnosed. Earlier, adaptive k-means clustering technique is used for follicile segmentation with ultrasound images, but follicles abnormal and normal classification are not focused. So, for follicles abnormal and normal classification, an artificial neural network (ANN) technique with Improved Fruit Fly Optimization (IFFOA) is proposed in this work and named as (IFFOA-ANN) that avoids those risks. In this technique, for enhancing image quality, input ultrasound images are resized and noise in it are removed. Then, adaptive k-means clustering technique is used for processing follicles segmentation. In addition, statistical GLCM is used for introducing feature extraction model. At last, for classification, ANN will be trained using these features. With respect to accuracy, recall and precision, proposed model’s effectiveness is demonstrated in experimental results.

Optimization Based Multi-Objective Weighted Clustering For Remote Monitoring System in WSN

Wireless Sensor Network (WSN) is generally considered as a standout amongst the most critical advancements for the twenty-first century, it normally comprises multifunctional wireless sensor nodes, with detecting, communications, and calculation capacities. Clustering the random nodes in WSN is a challenging task with high performance. This paper presents the new clustering model to monitor the eco-friendly mobile network by clustering the sensor nodes and to enhance the Quality of Service of that optimal network in WSN. The proposed Multi-Objective Weighted Clustering model groups the arbitrary nodes and afterward the optimal network is achieved by the optimization of network parameters. For optimizing the network parameters, a metaheuristic algorithm i.e. Improved Fruit Fly Optimization is introduced. With the goal of assessing the Coverage Efficiency (CE) and network user satisfaction of the accomplished optimal mobile network in WSN, the remote sensor monitoring process is applied. Sensor monitoring helps to know the network users and furthermore to improve the CE of WSN, contrasted with existing work.

Modified semi-supervised affinity propagation clustering with fuzzy density fruit fly optimization

Affinity propagation (AP) is a clustering method that takes as input measures of similarity between pairs of data points. As the oscillations and preference value need to be preset, the algorithm precision could not be controlled exactly. To improve the performance of AP, this study utilizes priori pairwise constraints to obtain the reliable similarity matrix named semi-supervised affinity propagation (SAP). To find the best solution in domain of preference value, this study also proposes an improved fruit fly optimization (IFO) to optimize the unknown parameters of the SAP model. The IFO algorithm has introduced the fuzzy density mechanism to enhance the searching capacities of fruit fly individuals. The benchmark functions experiments indicate that the IFO algorithm has better precision and convergence speed than other compared swarm intelligence algorithms. We used SAP that based on IFO to identify UCI datasets and synthetic datasets. The simulation results show that proposed clustering algorithm produces significantly better clustering quality and accuracy results. In addition, we utilized the improved model to analyse the seismic data. The clustering results indicated that the proposed model had the better research potential and the good application value.

An Improved Fruit Fly Optimization (IFFOA) based Cluster Head Selection Algorithm for Internet of Things

ABSTRACT Efficient data broadcasting to nodes of the target is feasible due to the Internet of Things (IoT) which includes a battery priced at a small value and having a smaller size, along with nodes that are hinged with aggregate for sensing which consume a small amount of power. In technique related to routing an essential part is played by clustering such that power is conserved. At this context, transmitting of data that is organized by a dependable network, which is being sustained, one cluster head is being chosen. The concern that is crucial in IoT, namely choosing a Cluster Head (CH) is focused at present work. CH is chosen based on energy that is of a residue in it, length of the path towards base-station, the node to node link for communicating as well as how farther it is to the vicinity. Network’s longevity can be achieved formerly using optimizing. Now, Improved Fruit Fly Optimization Algorithm (IFFOA) is the proposal for choosing cluster-head. Scheme proposed indicates by results simulated that present analysis conserves power, lengthening the longevity of the network as well as doing choosing proper CH. The simulations are checked by looking at IFFOA with FCPSO, PSO, and LEACH.

An improved fruit fly optimization algorithm for continuous function optimization problems

This paper presents an improved fruit fly optimization (IFFO) algorithm for solving continuous function optimization problems. In the proposed IFFO, a new control parameter is introduced to tune the search scope around its swarm location adaptively. A new solution generating method is developed to enhance accuracy and convergence rate of the algorithm. Extensive computational experiments and comparisons are carried out based on a set of 29 benchmark functions from the literature. The computational results show that the proposed IFFO not only significantly improves the basic fruit fly optimization algorithm but also performs much better than five state-of-the-art harmony search algorithms.