Network Optimizations Research Articles

Designing an automatic pollen monitoring network for direct usage of observations to reconstruct the concentration fields

We consider several approaches to a design of a regional-to-continent-scale automatic pollen monitoring network in Europe. Practical challenges related to the arrangement of such a network limit the range of possible solutions. A hierarchical network is discussed, highlighting the necessity of a few reference sites that follow an extended observations protocol and have corresponding capabilities.Several theoretically rigorous approaches to a network design have been developed so far. However, before starting the process, a network purpose, a criterion of its performance, and a concept of the data usage should be formalized. For atmospheric composition monitoring, developments follow one of the two concepts: a network for direct representation of concentration fields and a network for model-based data assimilation, inverse problem solution, and forecasting. The current paper demonstrates the first approach, whereas the inverse problems are considered in a follow-up paper.We discuss the approaches for the network design from theoretical and practical standpoints, formulate criteria for the network optimality, and consider practical constraints for an automatic pollen network. An application of the methodology is demonstrated for a prominent example of Germany's pollen monitoring network. The multi-step method includes (i) the network representativeness and (ii) redundancy evaluation followed by (iii) fidelity evaluation and improvement using synthetic data.

Multi-objective operation of solar-based microgrids incorporating artificial neural network and grey wolf optimizer in digital twin

A microgrid (MG) represents an outstanding possibility to transform the power sector into a more reliable, efficient, and accessible set of services for economic growth and environmental protection. The use of renewable energy resources contributes to the greening of the environment and the reduction of power costs. This article shows that a MG can likewise benefit from cloud/fog computing when it comes to computing-intensive tasks. By employing an artificial neural network with a modified gray wolf optimizer, a fog-driven energy management problem can be solved by an economical method. An area of several smart homes is considered in this study. Every house has a microgrid for generating energy. Meanwhile, it connects to the fog server so that data can be shared and stored. A fog server allows smart power users to exchange information about surplus power. Simulations are conducted to validate the suggested method for reducing costs and reduction of electricity imports considering the digital twin of smart homes.

A trusted security approach to detect and isolate routing attacks in mobile ad hoc networks

BackgroundSecure routing is of utmost importance in protecting mobile devices from external threats. However, the dynamic characteristics of Mobile Ad hoc Networks (MANETs) pose significant challenges in achieving a secure routing path. ObjectivesThis paper aims to propose a Trust and Anonymous Model (TAM) for efficient and secure data routing in MANETs. The objective is to address the limitations of existing routing approaches and enhance the security of the network. MethodsThe TAM protocol is designed with a Two-Tier Security Mechanism (TTSM). In the first level of security, trustable nodes are selected based on their ability to process control messages. The recommended trust value of a node is determined by estimating the speed at which it processes control messages, with higher energy nodes being considered more trustable. In the second level of security, the original node identity is concealed, and data is transmitted through selected trusted nodes with duplicate identities generated using a factorial recursive function. This ensures secure transmission, as malicious observers are unable to identify participating nodes in the routing operation. ResultsThe proposed TAM model is evaluated and compared with state-of-the-art schemes such as A Multi-attribute-based Trusted Routing for Embedded devices (EMBTR) and a cognitive energy-efficient-based trusted model (CEMT). Experimental evaluation demonstrates that the TAM model achieves better performance in terms of energy consumption, delay, packet delivery rate, and false node detection rate, thereby improving network optimality. ConclusionThe Trust and Anonymous Model (TAM) with its Two-Tier Security Mechanism (TTSM) presents a promising approach for secure data routing in MANETs. By selecting trustable nodes and utilizing duplicate identities, the TAM model enhances network security while achieving improved performance compared to existing schemes. The findings of this study contribute to the advancement of secure routing techniques in MANETs and provide insights for future research in this domain.

Smart Energy Management for a Microgrid System Based on Echo State Networks─Gray Wolf Optimizer Hybrid Algorithm

This work present a smart energy management system for a microgrid based on a hybrid intelligent adaptive algorithm composed of an echo state network and gray wolf optimizer techniques. The microgrid connected to the grid is composed of renewable sources such as photovoltaic and wind conversion associated with a hybrid battery hydrogen fuel generation storage system. An adaptive intelligent control unit is developed to manage the power flow between the hybrid power sources and other devices in the system. The controller chooses the optimal operating mode of power sources ensuring the continuous supply of the injecting power in the grid and maintaining the battery state of charge at acceptable levels as the hydrogen tank level. Complete mathematical modeling for the proposed system is implemented in MATLAB/Simulink to track the microgrid performance. Numerical and graphical results are presented to show the effectiveness of the proposed control system

A Time Delay Prediction Model of 5G Users Based on the BiLSTM Neural Network Optimized by APSO-SD

To address the problems of 5G network planning and optimization, a 5G user time delay prediction model based on the BiLSTM neural network optimized by APSO-SD is proposed. First, a channel generative model based on the ray-tracing model and the statistical channel model is constructed to obtain a large amount of time delay data, and a 5G user ray data feature model based on three-dimensional stereo mapping is proposed for input feature extraction. Then, an adaptive particle swarm optimization algorithm based on a search perturbation mechanism and differential enhancement strategy (APSO-SD) is proposed for the parameters’ optimization of BiLSTM neural networks. Finally, the APSO-SD-BiLSTM model is proposed to predict the time delay of 5G users. The experimental results show that the APSO-SD has a better convergence performance and optimization performance in benchmark function optimization compared with the other PSO algorithms, and the APSO-SD-BiLSTM model has better user time delay prediction accuracy in different scenarios.

Compressor Performance Prediction Based on the Interpolation Method and Support Vector Machine

Compressors are important components in various power systems in the field of energy and power. In practical applications, compressors often operate under non-design conditions. Therefore, accurate calculation on performance under various operating conditions is of great significance for the development and application of certain power systems equipped with compressors. To calculate and predict the performance of a compressor under all operating conditions through limited data, the interpolation method was combined with a support vector machine (SVM). Based on the known data points of compressor design conditions, the interpolation method was adopted to obtain training samples of the SVM. In the calculation process, preliminary screening was conducted on the kernel functions of the SVM. Two interpolation methods, including linear interpolation and cubic spline interpolation, were used to obtain sample data. In the subsequent training process of the SVM, the genetic algorithm (GA) was used to optimize its parameters. After training, the available data were compared with the predicted data of the SVM. The results show that the SVM uses the Gaussian kernel function to achieve the highest prediction accuracy. The prediction accuracy of the SVM trained with the data obtained from linear interpolation was higher than that of cubic spline interpolation. Compared with the back propagation neural network optimized by the genetic algorithm (GA-BPNN), the genetic algorithm optimization of extreme learning machine neural network (GA-ELMNN), and the genetic algorithm optimization of generalized regression neural network (GA-GRNN), the support vector machine optimized by the genetic algorithm (GA-SVM) has a better generalization, and GA-SVM is more accurate in predicting boundary data than the GA-BPNN. In addition, reducing the number of original data points still enables the GA-SVM to maintain a high level of predictive accuracy.

Prediction of CO2 emissions in China by generalized regression neural network optimized with fruit fly optimization algorithm.

As global warming becomes more prominent, the need to reduce carbon emissions to achieve China's carbon peak target is increasing. It is imperative to seek effective methods to predict carbon emissions and propose targeted emission reduction measures. In this paper, a comprehensive model integrating grey relational analysis (GRA), generalized regression neural network (GRNN) and fruit fly optimization algorithm (FOA) is constructed with carbon emission prediction as the research objective. Firstly, GRA is used for feature selection to find out the factors that have a strong influence on carbon emissions. Secondly, the parameter of GRNN is optimized using FOA algorithm to improve the prediction accuracy. The results show that (1) fossil energy consumption, population, urbanization rate and GDP are important factors affecting carbon emissions; (2) FOA-GRNN outperforms GRNN and back propagation neural network (BPNN), verifying the effectiveness of FOA-GRNN model for CO2 emission prediction. Finally, by analyzing the key influencing factors and combining scenario analysis with forecasting algorithms, the carbon emission trends in China for 2020-2035 are forecasted. The results can provide guidance for policy makers to set reasonable carbon emission reduction targets and adopt corresponding energy saving and emission reduction measures.

Two-timescale recurrent neural networks for distributed minimax optimization

In this paper, we present two-timescale neurodynamic optimization approaches to distributed minimax optimization. We propose four multilayer recurrent neural networks for solving four different types of generally nonlinear convex–concave minimax problems subject to linear equality and nonlinear inequality constraints. We derive sufficient conditions to guarantee the stability and optimality of the neural networks. We demonstrate the viability and efficiency of the proposed neural networks in two specific paradigms for Nash-equilibrium seeking in a zero-sum game and distributed constrained nonlinear optimization.

TREND: Transferability-Based Robust ENsemble Design

Deep Learning models hold state-of-the-art performance in many fields, but their vulnerability to adversarial examples poses a threat to their ubiquitous deployment in practical settings. Additionally, adversarial inputs generated on one classifier have been shown to transfer to other classifiers trained on similar data, which makes the attacks possible even if model parameters are not revealed to the adversary. This property of transferability has not yet been systematically studied, leading to a gap in our understanding of robustness of neural networks to adversarial inputs. In this work, we study the effect of network architecture, %initialization, optimizer, input, weight and activation quantization on transferability of adversarial samples. We also study the transferability of different attacks. Our experiments reveal that transferability is significantly hampered by input quantization and architectural mismatch between source and target, %is unaffected by initialization but% and the choice of optimizer turns out to be critical. We observe that transferability is architecture-dependent for both weight and activation quantized models. To quantify transferability, we use simple metric and demonstrate the utility of the metric in designing a methodology to build ensembles with improved adversarial robustness. When attacking ensembles we observe that ``gradient domination" by a single ensemble member model hampers existing attacks. To combat this we propose a new state-of-the-art ensemble attack. We compare the proposed attack with existing attack techniques to show its effectiveness. Finally, we show that an ensemble consisting of carefully chosen diverse networks achieves better adversarial robustness than would otherwise be possible with a single network.

Multiobjective bilevel programming model for multilayer perceptron neural networks

The architecture of multilayer perceptron (MLP) neural networks dictates the network's performance. However, aiming at the specific classification problems, suitable architectures of MLPs must be determined beforehand. More often, the design decisions rely on a trial and error learning and the experience knowledge. To automatically design an MLP's network architecture and optimize network parameters, a multiobjective bilevel programming model is built. In this model, a multiobjective optimization problem is constructed in the upper level for obtaining a set of Pareto optimal architectures of the MLPs, considering network complexity, training error rate, and validation error rate, while a single-objective optimization problem is established in the lower level to search for the optimum network parameters for a given network architecture. For dealing with this model efficiently, a novel multiobjective hierarchical learning algorithm (MOHLA) is proposed, in which an integer-coding NSGA-II is developed as the upper-level optimizer for a set of Pareto optimal network structures of the MLPs, while a non-iterative method is regarded as the lower-level solver for the MLPs' connection parameters. After a set of trained MLPs is obtained finally by using MOHLA, a selective ensemble strategy is adopted for improving identification accuracy. Three types of multiobjective bilevel programming models are investigated and compared in the experiments. Moreover, the proposed MOHLA is compared with several state-of-the-art learning approaches on various classification problems. The experimental results confirm that MOHLA performs well.

Work Flow, Advisory System Optimizes Intelligent Production Network

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 205735, “A Digital Oil Field Comprehensive Study: Automated Intelligent Production Network Optimization,” by Aulia A. Naufal, SPE, and Sabrina Metra, Schlumberger. The paper has not been peer reviewed. _ Leveraging technologies from a flow-assurance simulator, a Python application programming interface (API) toolkit, open-source machine-learning (ML) packages in Python, and a commercial visualization dashboard, the complete paper proposes a series of work flows to simplify model deployment and set up an automatic advisory system to provide insight as a means of justifying day-to-day engineering decisions. The complete paper discusses this methodology and provides a case study of its implementation, although this synopsis concentrates on the former. Background The authors identify four steps necessary for a valid network model that can describe production-system fluid-flow behavior accurately and support day-to-day decision making. These four steps, detailed in the complete paper, include the following: - Well-model building and matching - Well-basis gas lift evaluation - Network modeling - Gas-lift-optimization scenarios Work Flow Objective The objectives of creating an automated, intelligent production-network-optimization work flow include the following: - Minimize risk for data-quality issues caused by human error - Quicken pre- and post-deployment stages of the work flow, thus saving time and resources - Increase the functionality of a digital-oilfield work flow, including adding a production-surveillance dashboard, extracting publicly available temperature data from a weather-service API, and adding an ML autocalibration work flow Theoretical Basis Multiphase Steady-State Flow Simulator. A commercial flow simulator is used to simulate multiphase flow simulation throughout the production system from the reservoir to the surface facilities to enable comprehensive production- (and injection-) system analysis. By the term “steady state,” the authors mean that the mass flow rate is conserved throughout the system, with no accumulation of mass in any system component. This simulator is used from design to the optimization period of the oil, gas, and water production and injection systems. It is most often used by reservoir, production, and facilities engineers to model well performance, design artificial-lift systems, model pipeline networks and facilities, analyze field-development plans, and optimize production. Network modeling is necessary if the interaction of different components (such as compressors, separators, and wells) producing into a common gathering system must be considered (Fig. 1); the wellhead pressure and the deliverability of any well are influenced by the backpressure imposed by the production system. Network modeling also allows engineers to determine the effects of adding new wells and compression, looping flowlines, and changing separation systems. To analyze all components, the network should be solved based on the boundary conditions set on each component. The network will be converged when the pressure balance and mass balance at each node are within the specified tolerance. Then, parameters along the flow path (profile) and at each node in the network will be output. The authors discuss simulation settings, a network optimizer, and a flow-simulation Python toolkit in the complete paper.

Hybrid deep learning with improved Salp swarm optimization based multi-class grape disease classification model

The recent revolutions in Computer Vision (CV) and Artificial Intelligence (AI) techniques have found many applications in grapevine and smart agriculture processes. Recently, Deep Learning (DL) techniques like Convolutional Neural Networks (CNN) have been broadly applied in smart agriculture, leaf disease recognition, and scene perception. In this background, the current study develops a Hybrid Deep Learning with Improved Salp Swarm Optimization-based Multi-class Grape Disease Classification (HDLISSA-MGDC) model. The proposed HDLISSA-MGDC model focuses on the classification of grape leaf images into four distinct classes such as black measles, black rot, Isariopsis leaf spot and healthy. Initially, the Median Filtering (MF) technique is applied for image pre-processing, which eliminates the noise present in the images. In addition, the HDLISSA-MGDC model designs a feature extractor with the help of Dilated Residual Network (DRN) and Adam optimizer. For grape disease classification, the Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) model is employed in this study. Finally, the ISSA is exploited to adjust the hyperparameter values of the CNN-GRU method. The proposed HDLISSA-MGDC method was simulated using the plant leaf disease dataset. The simulation results show the significant performance of the proposed HDLISSA-MGDC model.

Application of hybrid ANN paradigms built with nature inspired meta-heuristics for modelling soil compaction parameters

For monitoring soil compaction in civil engineering projects, the determination of optimum moisture content (OMC) and maximum dry density (MDD) are very significant. The present method of determining the OMC and MDD through laboratory testing is a time-consuming task and requires skilled workmanship. Thus, in order to replace the conventional approach of OMC and MDD determination in the laboratory, this study presents a hybrid intelligence model of artificial neural network (ANN) and grey wolf optimizer (GWO). These two computational intelligence techniques collaborate to construct the ANN-GWO model for estimating OMC and MDD of soils. A database of 126 experimental results with six influencing factors was employed in the current work. To compare the outcomes of the developed ANN-GWO model, four additional hybrid ANNs were built utilizing particle swarm optimization, slime mould algorithm, Harris hawks optimization, and salp swarm algorithm. Additional laboratory experiments were conducted to investigate the generalization ability of the constructed ANNs. Results for the testing dataset indicate that the ANN-GWO achieves the best accurate estimation in cases of OMC (RMSE = 0.0986, PFI = 1.2569, and R2 = 0.7273) and MDD (RMSE = 0.1017, PFI = 1.2318, and R2 = 0.7147) of soils. The ANN-GWO model yields superior results than other hybrid ANNs constructed in this work. Overall, the suggested ANN-GWO can assist geotechnical engineers to estimate the OMC and MDD of soils in civil engineering works. The developed MATLAB models are also attached as a supplementary material which can readily be used to determine soil compaction parameters along with their ranges for different soil types.

PID Control Model Based on Back Propagation Neural Network Optimized by Adversarial Learning-Based Grey Wolf Optimization

In processes of industrial production, the online adaptive tuning method of proportional-integral-differential (PID) parameters using a neural network is found to be more appropriate than a conventional controller with PID for controlling different industrial processes with varying characteristics. However, real-time implementation and high reliability require the adjustment of specific model parameters. Therefore, this paper proposes a PID controller that combines a back-propagation neural network (BPNN) and adversarial learning-based grey wolf optimization (ALGWO). To enhance the unpredictable behavior and capacity for exploration of the grey wolf, this study develops a new parameter-learning technique. Alpha gray wolves use the random walk of levy flight as their hunting method. In beta and delta gray wolves, a search strategy centering on the top gray wolf is employed, and in omega gray wolves, the decision wolves handle the confrontation strategy. A fair balance between exploration and exploitation can be achieved, as evidenced by the success of the adversarial learning-based grey wolf optimization technique in ten widely used benchmark functions. The effectiveness of different activation functions in conjunction with ALGWO were evaluated in resolving the parameter adjustment issue of the BPNN model. The results demonstrate that no unique activation function outperforms others in different controlled systems, but their fitnesses are significantly inferior to those of the conventional PID controller.

Long-term forecasts of residential energy profiles based on Conv2D and LSTM models (electricity- and gas-based households)

For power system operation and expansion of grid-import systems, an accurate forecast model plays an essential role in the better management of household electricity demands. With the aim of finding an accurate forecast model in the proper representation of various household energy profiles, our research objective is centered on the development of a reliable forecast system for a group of 24-household energy consumers. In this energy study, we proposed long-term forecasts of (1) residential energy profiles within the multi-classification framework and (2) energy costing of the household demands using the Keras two-dimensional convolutional neural network (Conv2D) model and long short-term memory (LSTM) models. These high-level Keras neural networks are built to extract multivariate features for household energy consumption modeling and forecasting. The proposed forecast systems utilized a similar model hyperparameter configuration, while the forecast skills are validated with spatial–temporal variation datasets of ten remote locations. The actual costs of household demand and supply are estimated and compared with Conv2D predictions. The finding results (hourly and seasonal predictions and model evaluation) revealed that Conv2D and LSTM forecast systems are promising for household energy forecast solutions. Experimental results of the Conv2D predictive system achieved better forecast skills [correlation coefficient (0.727–0.994) and root mean square error (0.190–0.868)] than LSTM forecasts (0.308–0.987 and 0.278–1.212). However, experimental findings revealed that forecast skills of the predictive systems in residential energy demand predictions are highly influenced by the (1) quality of input datasets, (2) model hyperparameter tuning approach, and (3) learning rate of selected network optimizer(s).

Application of deep NN optimized by multi-parameter fusion in ideological and political construction of professional courses in colleges and universities

Curriculum ideology and politics is an inherent requirement to achieve the goal of "cultivating morality and cultivating people" in colleges and universities, and it is a beneficial exploration to realize the three-round education. The ideological and political construction of professional courses in colleges and universities not only teaches students knowledge and skills, but also helps students form correct values. Aiming at how to build ideological and political courses in colleges and universities, a design method based on multi-parameter fusion to gradually optimize deep NN is proposed. Firstly, the initial NN model without hidden layer is determined by analyzing the samples and categories, and then the hidden layer is gradually added on the basis of the initial NN to construct a deep NN with multi-parameter fusion optimization. Based on the TensorFlow framework, taking handwritten digit recognition as an example, a deep NN model is gradually designed. During the whole experiment, the network structure, activation function, loss function, optimizer, learning rate and sample batch size are continuously adjusted, and finally a multi-parameter design is designed. The fusion optimized deep NN model with high accuracy provides an effective idea for building a NN. As the learning rate increases, the performance of the NN gradually improves. In the training set and test set, the accuracy rate is almost the highest when the learning rate is 0.3, and the accuracy rate is 93.30% and 92.58% respectively when the number of iterations is 30, which shows that The NN optimized by multi-parameter fusion can be well applied to the ideological and political construction of professional courses in colleges and universities, and has strong application prospects.

Applying Neural Networks on Biometric Datasets for Screening Speech and Language Deficiencies in Child Communication

Screening and evaluation of developmental disorders include complex and challenging procedures, exhibit uncertainties in the diagnostic fit, and require high clinical expertise. Although typically, clinicians’ evaluations rely on diagnostic instrumentation, child observations, and parents’ reports, these may occasionally result in subjective evaluation outcomes. Current advances in artificial intelligence offer new opportunities for decision making, classification, and clinical assessment. This study explores the performance of different neural network optimizers in biometric datasets for screening typically and non-typically developed children for speech and language communication deficiencies. The primary motivation was to give clinicians a robust tool to help them identify speech disorders automatically using artificial intelligence methodologies. For this reason, in this study, we use a new dataset from an innovative, recently developed serious game collecting various data on children’s speech and language responses. Specifically, we employed different neural network approaches such as Artificial Neural Networks (ANNs), K-Nearest Neighbor (KNN), Support Vector Machines (SVM), along with state-of-the-art Optimizers, namely the Adam, the Broyden–Fletcher–Goldfarb–Shanno (BFGS), Genetic algorithm (GAs), and Particle Swarm Optimization algorithm (PSO). The results were promising, while Integer-bounded Neural Network proved to be the best competitor, opening new inquiries for future work towards automated classification supporting clinicians’ decisions on neurodevelopmental disorders.

Distributed finite-time optimization algorithms with a modified Newton–Raphson method

In this paper, we propose a distributed finite-time optimization protocol for single-integrator continuous-time multi-agent systems, which utilizes a modified Newton–Raphson method. The inverse of Hessian matrix, sign function and the gradient are adopted for the design of the algorithms. The proposed algorithms make agents converge to the network optimizer under any initial state and finite time, respectively. Lyapunov method and the properties of sign function are employed to verify the convergence of the proposed algorithms. Besides, the adaptive method is also considered to avoid complex parameter conditions and centralized parameters. Finally, numerical simulations are provided to testify the presented results.

Prediction of tool wear in milling process based on BP neural network optimized by firefly algorithm

Long-time discontinuous contact is easy to cause tool wear during milling. To decrease the impact of severe wear on workpiece quality and processing efficiency, cutting tools should be replaced timely. Therefore, tool wear prediction is an important aspect in improving process efficiency, ensuring machining precision and realizing intelligent manufacturing. To boost the precision of online prediction of tool wear, this paper suggests a novel approach to monitor tool wear by optimizing backpropagation (BP) neural network via firefly algorithm (FA). Specifically, the progressive semi-soft threshold function is applied to the process of cutting force signal noise reduction, which reduces redundant signals and noise interference in the signal. Time-domain analysis, frequency-domain analysis, and wavelet packet decomposition are utilized, cutting force features are extracted, and Pearson correlation coefficient is used to sift out signal features that are highly connected to tool wear. The FA is used to improve the BP neural network's weights and thresholds. Through learning nonlinear mapping, relationship between tool flank wear and signal features is realized. A prediction model of tool wear of FA-BP is constructed. Milling experiments validate the prediction model in the milling process. The experimental outcomes confirm the precision and reliability of the method. In comparison to BP neural network, genetic algorithm optimized BP neural network and particle swarm optimization algorithm optimized BP neural network prediction method, it has a greater prediction accuracy and a stronger training impact, and has superior performance. The research results can give a theoretical foundation and technological assistance for predicting tool wear, which is crucial for improving workpiece quality, processing efficiency, and promoting intelligent development.

MiFi: Bounded Update to Optimize Network Performance in Software-Defined Data Centers

A controller needs to solve the multi-commodity flow problem and globally update the network under tight time constraints to maintain optimal network configurations. This centralized optimization in data centers involves many variables and constraints, which has a slow convergence speed and little scalability. In this paper, we propose MiFi, which aims to Minimize Flow cost or intuitively average transmission delay (delay or latency of flows), under reconfiguration budget constraints in data centers. Thus, we formulate this optimization problem as a constrained Markov Decision Process and propose a set of algorithms to solve it in a scalable manner. We first develop a propagation algorithm to identify the flows mostly affected in terms of latency and configuration in the next update. Then, we set a limitation range (the subset of switches requiring network updates) for updating them to improve adaptability and scalability by updating a less number of flows each time to achieve fast operations. Further, based on the Drift-Plus-Penalty method in Lyapunov theory, we propose a heuristic policy without prior information of flow demand and a renewal policy with a performance guarantee to minimize the additive optimality gap. To the best of our knowledge, MiFi is the first paper that studies the range and frequency of flow reconfigurations, which has both theoretical and practical significance in the area. Emulations and numerical simulations, which are much better than the estimated theoretical bound, show that MiFi outperforms the state of the art algorithms in terms of latency by over 45% while making improvements in adaptability and scalability.