Convergence Time Of Algorithm Research Articles

Mean-Field Learning for Edge Computing in Mobile Blockchain Networks

Blockchain has been leveraged to secure transactions for the m-commerce. However, the intensive computation in the mining process restricts the participation of mobile devices. Currently, some studies have deployed edge computing services to support the mining process, where edge servers managed by one Service Provider (SP) are considered. This paper investigates a more practical scenario with multiple SPs, where servers managed by different SPs have distinct capacities and prices, making miners’ offloading decisions rather complicated. To tackle the above challenges, we consider task offloading, block propagation and miner mobility comprehensively to maximize utilities of miners. Specifically, we first formulate a Markov game, and then design a learning-based offloading algorithm for off-chain computation, where a novel learning model is constructed by integrating Deep Reinforcement Learning (DRL) and Mean Field Theory (MFT) to guarantee a Nash equilibrium. Different from existing studies, each miner merely needs to respond to the average effect from others in our system, insteading of knowing policies of others. Finally, both theoritical and performance results show that our designed algorithm has superiority on average miner utilities and algorithm convergence time compared with other representative algorithms.

A robust multiple Unmanned Aerial Vehicles 3D path planning strategy via improved particle swarm optimization

Path planning algorithms for Unmanned Aerial Vehicles (UAVs) are essential in various domains, such as search and rescue operations, agriculture, and delivery services. Nonetheless, finding optimal flight paths in complex environments with obstacles remains challenging. This paper proposes a novel path planning algorithm that uses a Nash equilibrium approach based on game theory to strike a balance between the exploitation and exploration capabilities of UAVs. The algorithm is designed in a simulated flight environment with obstacles, utilizing the self-awareness and group awareness coefficients from particle swarm optimization(PSO) as participants in the game theory model. The proposed path planning algorithm significantly improves the navigation of multiple UAVs in complex scenarios with obstacles by achieving a balance between their exploitation and exploration capabilities. Simulation experiments demonstrate that the proposed method surpasses traditional approaches, exhibiting an average improvement of 32.57%, 32.17%, and 29.33% in the algorithm convergence time, average flight distance, and average flight time, respectively. The significance of this research lies in its contribution to advancing UAV path planning algorithms. By integrating game theory and PSO, the proposed method optimizes the exploration and exploitation capabilities of UAVs, leading to improved navigation and resource utilization.

Design of Log Analysis System Based on Multi-Object Clustering Algorithm

With the rapid development of the information age and the continuous expansion of data, all walks of life have begun to collect and analyze massive amounts of data to extract information that is valuable to them. Clustering technology in data mining is an important means of data analysis. Because different data sets have different distribution characteristics, traditional single-object clustering cannot adapt to the effective processing of different data sets, so multi-object clustering has gradually become a research hotspot. The purpose of this paper is the design of log analysis system based on a multi-objective clustering algorithm. This article first determines the main goals of the log analysis system and performs a detailed analysis of the system’s demanding functional analysis and demanding non-functionality respectively. The log analysis system is mainly divided into five modules for the detailed design of the log analysis system. After the system design is completed, various functional modules are completed through corresponding technologies, the entire log system is tested for attacks, and the system is further improved. Finally, the system can completely analyze the attack type through log analysis and carry out relevant early warnings to meet the needs of users. When the data set is 1.8MB, the convergence time of the K-medoid algorithm is 3678.49, and the convergence time of the algorithm in this paper is 2536.42. When the data reaches 165MB, the convergence time of the algorithm in this paper is 4326.28, and the convergence time of the K-medoid algorithm is 8184. This shows that as the scale of data continues to increase, the processing time difference changes and the processing speed of this algorithm has obvious advantages.

Theoretical analysis of garden balsam optimization algorithm

Garden balsam optimization (GBO) is a new proposed evolutionary algorithm based on swarm intelligence. Convergence and time complexity analyses are very important in evolutionary computation, but the research on GBO is still blank. Same as other evolutionary algorithms, the optimization process of the GBO algorithm can be regarded as a Markov process. In this paper, a Markov stochastic model of the GBO algorithm is defined and used to prove the convergence of GBO algorithm. Finally, the approximation region of the estimated convergence time of GBO algorithm is calculated, which characterizes the evolution of the evolutionary process of the proposed algorithm.

Hybrid cuckoo–red deer algorithm for multiobjective localization strategy in wireless sensor network

SummaryFor the past 5 years, localization has become an emerging area in the field of wireless sensor networks (WSNs), which can be applicable for diverse applications, such as outdoor environments and monitoring of objects located in indoor environments. The major constraint in localization is allocating a location for each node as diverse sensor nodes are employed to retrieve the information in WSN. Recent research works in node localization have generally moved by multihop range‐free localization approaches for achieving high accuracy through reducing the localization error among the actual and estimated position of nodes. The conventional approaches are aimed to improve the localization accuracy without considering efficiency concerned with algorithm's convergence time and energy costs. The main intent of this paper is to model the WSN localization model, which focuses on the localization of anchor nodes concerning the target nodes. The multiobjective function is developed by considering the distance function, received signal strength (RSS), and energy. Here, a hybrid metaheuristic algorithm named hybrid cuckoo–red deer algorithm (HC‐RDA) with the integration of red deer algorithm (RDA) and cuckoo search algorithm (CSA) is adopted for localization of unknown nodes. The fitness function of the developed model is the minimization of the derived multiobjective function. Both simulation studies and theoretical analysis demonstrate that the proposed approach can improve localization performance with a better convergence rate, thus ensuring high localization accuracy compared to conventional metaheuristic models.

Multilayer event‐based distributed control system for DC microgrids with non‐uniform delays and directional communication

The secondary control layer of microgrids is often modelled as a multi-agent distributed system, coordinated based on consensus protocols. Convergence time of consensus algorithm significantly affects transient stability of microgrids, due to changes in communication topology, switching of distributed generations (DGs), and uncertainty of intermittent energy sources. To minimise convergence time in consensus protocol, this work proposes a multilayer event-based consensus control framework, which is resilient to communication delays and supports plug-and-play (P&P) addition or removal of DGs in DC microgrids of cellular energy systems. A novel bi-layer optimisation algorithm minimises convergence time by selecting an optimal communication topology graph and then adjusts controllers' parameters. Average consensus is achieved among distributed controllers using an event-based consensus protocol, considering non-uniform delays between agents. A realisation method has also been introduced using the directional beamforming technique for topology assignment algorithm based on modern telecommunication technologies. Provided feasibility case study has been implemented on a real-time hardware-in-the-loop (HIL) experimental testbed, to validate the performance of the proposed framework for key purposes of voltage stabilisation and balanced power-sharing in DC microgrids.

An improved denoising model for convolutional neural network

In recent years, computer vision and many other research fields have put forward higher and higher requirements for image quality, and image denoising has become an important research direction. Aiming at the problems of general image denoising effect and long algorithm convergence time, this paper makes an improvement on the original network structure of denoising convolutional neural network DnCNN model and adds channel attention mechanism in the penultimate layer. The channel attention mechanism further improves the signal-to-noise ratio (SNR) and optimizes the noise reduction effect through convolution and deconvolution neural networks. The experimental results show that, under the same noise reduction level of 25, the PSNR of the proposed DnCNN2 algorithm is 1.25dB and 1.62dB higher than that of the DnCNN algorithm after the verification of the BSD68 and SET12 test data sets. Structural similarity (SSLM) was increased by 0.0011 and 0.0103, respectively. Meanwhile, the convergence time of the algorithm becomes shorter. Compared with traditional deep learning image denoising algorithms, the noise reduction effect and operation efficiency of the proposed method are also improved, which has certain advantages.

Bidirectional Positioning Assisted Hybrid Beamforming for Massive MIMO Systems

The integration of the massive multiple-input multiple-output (MIMO) and millimeter-wave (mmWave) communication can increase the throughput of 5G networks. As an attractive technique in the MIMO systems, hybrid beamforming (HBF) can improve the 5G capacity by employing spatial domain resources. However, with the increase of the number of antennas, the traditional beamforming algorithms fail to efficiently keep a balance between the hardware complexity and beamforming gains. In this paper, with the aid of bidirectional location information, a bidirectional positioning assisted HBF (BPA-HBF) scheme is proposed. Specifically, we first propose a new scheme to decouple the optimal problem of traditional HBF as two phases. In the analog beamforming (ABF) phase, the dominated path among the multi-path components is determined by the transmitter and receiver. In addition, the codebook-based beamforming weight vectors are bidirectionally and synchronously determined according to the angle parameters of the dominated path. In the second phase, based on the ABF matrices, the digital beamformers are designed to maximize the energy efficiency. Simulation results indicate that the proposed BPA-HBF scheme can lead to a lower convergence time and complexity than the conventional schemes. In addition, the results show that the algorithm convergence time can be significantly reduced by increasing the positioning precision.

High Availability Deployment of Virtual Network Function Forwarding Graph in Cloud Computing Environments

Network Function Virtualization (NFV) stands out quickly as a promising innovation in telecommunication networks. It leverages the concept of cloud technology and virtualization techniques. However, the continuity of cloud network services has become an essential availability requirement for NFV. Failure of Virtual Network Functions (VNFs) may cause critical quality assurance problems for such network services. VNF chaining and placement can be considered as the VNF Forwarding Graph (VNF-FG) mapped on a cloud provider infrastructure, also called NFV Infrastructure (NFVI). This mapping is addressed while neglecting the massive link utilization and bandwidth consumption that can be encountered during VNF recovery phase. In this paper, an Integer Linear Programming (ILP) approach is developed to model VNF-FG deployment problem while guaranteeing high availability against node/VM failures. Then, the Redundant VNF-FG Deployment (RVNF-FGD) heuristic algorithm is proposed that takes VNF migration into consideration. RVNF-FGD algorithm attempts to find a near-optimal solution that achieves a trade-off between availability and scalability with a reasonable convergence time. Thus, facilitating the practical deployment of the proposed approach. Simulation results confirm that RVNF-FGD algorithm is capable of simultaneously reducing link utilization and bandwidth consumption across the core layer of the cloud network. In addition, it reduces VNF-FG communication cost and overall energy consumption. The convergence time of RVNF-FGD algorithm is assessed by applying it to broader cloud network architectures. This assessment indicates the viability of the proposed approach in responding quickly to failures.

Distributed Fixed-time Formation-containment Control for Multiple Euler-Lagrange Systems with Directed Graphs

In this paper, the distributed fixed-time formation-containment (DFTFC) problem of multiple Euler-Lagrange systems with parametric uncertainties and input disturbances is studied under directed graphs. First, a distributed fixed-time sliding-mode estimator is constructed to estimate the desired states of each robot with that only parts of followers can access the information of leaders. Next, based on the estimator, a distributed fixed-time formation control algorithm is developed to drive leaders to achieve a specified formation. Then, the distributed fixed-time containment control algorithm is proposed for followers with its estimated states being tracked in a fixed time. Note that an explicit upper bound of the convergence time of DFTFC control algorithm is obtained independent of initial conditions. Finally, numerical simulations are performed to show the effectiveness of the obtained theoretical results.

Robustness to misalignment of low-cost, compact quantitative phase imaging architectures.

Non-interferometric approaches to quantitative phase imaging could enable its application in low-cost, miniaturised settings such as capsule endoscopy. We present two possible architectures and both analyse and mitigate the effect of sensor misalignment on phase imaging performance. This is a crucial step towards determining the feasibility of implementing phase imaging in a capsule device. First, we investigate a design based on a folded 4f correlator, both in simulation and experimentally. We demonstrate a novel technique for identifying and compensating for axial misalignment and explore the limits of the approach. Next, we explore the implications of axial and transverse misalignment, and of manufacturing variations on the performance of a phase plate-based architecture, identifying a clear trade-off between phase plate resolution and algorithm convergence time. We conclude that while the phase plate architecture is more robust to misalignment, both architectures merit further development with the goal of realising a low-cost, compact system for applying phase imaging in capsule endoscopy.

Research on the Algorithm of Online Route Re-planning for UAV Formation

In order to solve the real-time and robust problem of online flight path re-planning when UAV encounters new threat sources in the course of navigation, an online flight route re-planning algorithm based on sparse A* algorithm is proposed. Based on the threat model of UAV, the online route re-planning strategy is analyzed. When the UAV detects a new threat in the course of navigation, according to the current position of the UAV formation and the position of the threat source, a route that can safely bypass the threat source is quickly re-planned. In order to meet the requirements of real-time and robustness, the node expansion and update strategy of the improved sparse A* algorithm and the hierarchical planning combined with the size step size are adopted. The simulation system of UAV formation navigation is established to verify the process of online route re-planning. The number of expansion nodes and convergence time of algorithm before and after the improvement are compared, and the track accuracy of fine planning based on rough planning is compared. The experimental results show that the improved algorithm can speed up the search speed and meet the real-time requirements of online route re-planning.

Evaluation of different methods for solar cells/modules parameters extraction

Reliable modelling techniques are required to optimise the performance of photovoltaic (PV) systems. This paper considers the implicit form of the single diode model for solar cell to introduce and compare four new approaches for extracting the five characteristics parameters of PV cells/modules. Measured I-V curves for three different PV samples (mono-crystalline cell, polycrystalline module and amorphous module) under different irradiance conditions and temperatures were used to implement and validate each method. Numerical and optimisation methods were applied to solve different formulations of the single diode equivalent model. All methods are mathematically described, and the primary considerations when writing the MATLAB codes are highlighted. Comparison factors such as the mean absolute percentage error (MAPE), the algorithm convergence time (ACT), the coefficient of determination (R2) and the error at the maximum power point (δ), are used to evaluate each method. All proposed methods were verified, and the parameters were extracted with excellent performance. The method based on the least square fitting and solved by Newton–Raphson method provides the best results for the mono-crystalline sample compared to the other three methods while using particle swarm optimisation as a solving algorithm provides better results for the other two sample modules. The result of this work represents a guideline for extracting the five parameters of the single diode model from the measured solar cells/modules I-V curves. Although the SDM model does not describe the physical structure of the amorphous solar module, since this consists of a tandem p-i-n diode structure, the developed methods still deliver results with errors comparable to that of the single junction PV devices.

Global smooth sliding mode controller for flexible air-breathing hypersonic vehicle with actuator faults

A global smooth sliding mode controller (GSSMC) is proposed for flexible air-breathing hypersonic vehicle (FAHV) under actuator faults and parametric uncertainties, consisting of global fast finite-time integral sliding surface (GFFIS), generalized smooth second-order sliding mode reaching law (GSRL) and smooth fixed-time observer. Firstly, nonlinear control-oriented model of FAHV is processed using input/output feedback linearization with flexible effects and actuator faults modeling as lumped matched disturbances. Secondly, a GFFIS is established to ensure finite-time convergence of states without singularity based on a newly proposed fast finite-time high-order regulator (FFR). The FFR is improved from standard finite-time high-order regulator via dilation rescaling, which can accelerate response speed avoiding complicated parameters selection. Meanwhile, GFFIS can eliminate initial reaching phase to enhance robustness of system due to characteristic of global convergence. Thirdly, a GSRL is presented to ensure finite-time convergence of sliding mode vector and its derivative without chattering based on a generalized smooth second-order sliding mode control algorithm, the stability and finite convergence time of which is analyzed via Lyapunov criteria in detail. Then, a smooth fixed-time observer is applied to estimate lumped disturbances in fixed time and avoid effects of parametric uncertainties. With the three components, GSSMC can drive FAHV subject to actuator faults and parametric uncertainties to follow desired values in finite time with smooth control signals. Ultimately, three sets of simulations are performed to verify the effectiveness of the methods proposed.

Joint rate control and power allocation for low-latency reliable D2D-based relay network

Emerging 5G applications impose stringent requirements on network latency and reliability. In this work, we propose a low-latency reliable device-to-device (D2D) relay network framework to improve the cell coverage and user satisfaction. Particularly, we develop a cross-layer low-complexity resource allocation algorithm, which jointly optimizes the rate control and power allocation from a long-term perspective. The long-term optimization problem is transformed into a series of short-term subproblems by using Lyapunov optimization, and the objective function is separated into two independent subproblems related to rate control in network layer and power allocation in physical layer. Next, the Karush-Kuhn-Tucher (KKT) conditions and alternating direction method of multipliers (ADMM) algorithm are employed to solve the rate control subproblem and power allocation subproblem, respectively. Finally, simulation results demonstrate that the proposed algorithm can reach 99.9% of the optimal satisfaction of D2D pairs with lower average network delay compared to the baseline algorithm. Furthermore, the convergence time of the ADMM-based power allocation algorithm is only about 1.7% of that by using the CVX toolbox.

A combined hierarchical algorithm of mammograms registration using mutual information and a point based matching approach

Mammographic image registration is an important process in the comparative analysis of mammograms that aims to correctly interpret them. In this paper, we proposed a combined hierarchical framework for a non-rigid mammograms registration. So, the conjoint use of the multi-resolution and the multigrid hierarchical strategies allows to increase progressively the warp complexity with a fixe size of the manipulated data contributed then to a finest registration with acceptable computing cost. To perform registration process, a hybridisation of an intensity-based registration method using mutual information and a point-based matching approach using the Thin Plate Spline approach is achieved. This hybridisation allows not only to outperform the registration accuracy by using the mutual information as a similarity measure adapted to not similar images but also to decrease the algorithm convergence time. Registration performance has been evaluated by aligning mammograms from the MIAS database and results demonstrate the effectiveness of the proposed algorithm for mammograms registration.

A combined hierarchical algorithm of mammograms registration using mutual information and a point based matching approach

Mammographic image registration is an important process in the comparative analysis of mammograms that aims to correctly interpret them. In this paper, we proposed a combined hierarchical framework for a non-rigid mammograms registration. So, the conjoint use of the multi-resolution and the multigrid hierarchical strategies allows to increase progressively the warp complexity with a fixe size of the manipulated data contributed then to a finest registration with acceptable computing cost. To perform registration process, a hybridisation of an intensity-based registration method using mutual information and a point-based matching approach using the Thin Plate Spline approach is achieved. This hybridisation allows not only to outperform the registration accuracy by using the mutual information as a similarity measure adapted to not similar images but also to decrease the algorithm convergence time. Registration performance has been evaluated by aligning mammograms from the MIAS database and results demonstrate the effectiveness of the proposed algorithm for mammograms registration.

Analysis of Toothbrush Rig Parameter Estimation Using Different Model Orders in Real-Coded Genetic Algorithm (RCGA)

System identification is a method to build a model for a dynamic system from the experimental data. In this paper, optimization technique was applied to optimize the objective function that lead to satisfying solution which obtain the dynamic model of the system. Real-coded genetic algorithm (RCGA) as a stochastic global search method was applied for optimization. Hence, the model of the plant was represented by the transfer function from the identified parameters obtained from the optimization process. For performance analysis of toothbrush rig parameter estimation, there were six different model orders have been considered where each of model order has been analyzed for 10 times. The influence of conventional genetic algorithm parameter - generation gap has been investigated too. The statistical analysis was used to evaluate the performance of the model based on the objective function which is the Mean Square Error (MSE). The validation test-through correlation analysis was used to validate the model. The model of model order 2 is chosen as the best model as it has fulfilled the criteria involved in selecting the accurate model. Generation gap used was 0.5 has shorten the algorithm convergence time without affecting the model accuracy.

Analysis of Known Linear Distributed Average Consensus Algorithms on Cycles and Paths.

In this paper, we compare six known linear distributed average consensus algorithms on a sensor network in terms of convergence time (and therefore, in terms of the number of transmissions required). The selected network topologies for the analysis (comparison) are the cycle and the path. Specifically, in the present paper, we compute closed-form expressions for the convergence time of four known deterministic algorithms and closed-form bounds for the convergence time of two known randomized algorithms on cycles and paths. Moreover, we also compute a closed-form expression for the convergence time of the fastest deterministic algorithm considered on grids.

On the Performance of Multi-Message Algebraic Gossip Algorithms in Dynamic Random Geometric Graphs

In this letter, we investigate the performance of algebraic gossip algorithms for information dissemination in mobile ad hoc networks (MANETs). In the gossip algorithms, in each time slot, each node communicates with a neighboring node, which is randomly selected by an Exchange algorithm, to exchange their information. The goal of the algorithm is to efficiently disseminate all the messages to all the nodes as fast as possible. We analyze the convergence time of network coding (NC) based gossip algorithm by taking into account the node mobility in MANETs, and derived a lower bound on the convergence time. It showed that the convergence time of the NC gossip algorithm with node mobility can be reduced by the order of $O(n^{1/2}/\log ^{3/2}n)$ over that without considering node mobility. But it is only $O(\log n)$ quicker than the gossip algorithm with node mobility but no NC.