Two-step Optimization Algorithm Research Articles

Multi-UAV path planning using DMGWO ensuring 4D collision avoidance and simultaneous arrival

Purpose Multi-unmanned aerial vehicle (UAV) systems have succeeded in gaining the attention of researchers in diversified fields, especially in the past decade, owing to their capability to operate in complex scenarios in a coordinated manner. Path planning for UAV swarms is a challenging task depending upon the environmental conditions, the limitations of fixed-wing UAVs and the swarm constraints. Multiple optimization techniques have been studied for path-planning problems. However, there are local optimum and convergence rate problems. This study aims to propose a multi-UAV cooperative path planning (CoPP) scheme with four-dimensional collision avoidance and simultaneous arrival time. Design/methodology/approach A new two-step optimization algorithm is developed based on multiple populations (MP) of disturbance-based modified grey-wolf optimizer (DMGWO). The optimization is performed based on the objective function subject to multi constraints, including collision avoidance, same minimum time of flight and threat and obstacle avoidance in the terrain while meeting the UAV constraints. Comparative simulations using two different algorithms are performed to authenticate the proposed DMGWO. Findings The critical features of the proposed MP-DMGWO-based CoPP algorithm are local optimum avoidance and rapid convergence of the solution, i.e. fewer iterations as compared to the comparative algorithms. The efficiency of the proposed method is evident from the comparative simulation results. Originality/value A new algorithm DMGWO is proposed for the CoPP problem of UAV swarm. The local best position of each wolf is used in addition to GWO. Besides, a disturbance is introduced in the best solutions for faster convergence and local optimum avoidance. The path optimization is performed based on a newly designed objective function that depends upon multiple constraints.

Age of Information for Partial Earliest Relay Aided Short Packet Status Update With Energy Harvesting

This paper focuses on the timely delivery of status updates for a multi-relay assisted energy harvesting (EH) Internet of Things system with short packet communications (SPC), where an EH-powered source transmits status updates to a destination with the aid of multiple EH-powered relays. To improve the energy efficiency and information freshness, an earliest-ℓ partial relay selection scheme is proposed, in which the earliest ℓ relays that successfully receive the status update from the source will be selected to forward the status update to the destination, and the selection combining (SC) and maximal-ratio combining (MRC) schemes are used at the destination. Firstly, we investigate the update packet delivery schemes of source-relay (S-R) and relay-destination (R-D) links, and present the packet error probabilities under the SC and MRC schemes based on SPC theory. Secondly, after characterizing the full charge time durations of both the source and relay, we investigate the statistical description of the number of the packet transmission attempts from the source to relay as well as the one from the relay to destination by using order statistics. With the analysis of the evolution of the instantaneous age of information (AoI), the expression for average AoI is derived. Finally, we investigate the average AoI minimization, which is formulated as a coupled triplet problem of the packet lengths at the source and relays and the value of ℓ. To overcome the coupling, we present the convexity approximation of the average AoI as well as the concise proof. Then, an efficient gradient-based two-step optimal search algorithm is proposed to solve the optimization problem, which searches the local optimal packet lengths for S-R and R-D links by iteratively updating the value of ℓ until the global optimal objective parameters are found. Numerical analyses give the effect of the key parameters on the average AoI, and the comparison demonstrates that the proposed gradient-based method converges quickly than the conventional greedy search method.

How to Tame Mobility in Federated Learning Over Mobile Networks?

Federated learning (FL) over mobile networks has attracted intensive attention recently. User mobility is a fundamental feature of mobile networks, which leads to dynamic network topology and wireless connectivity losses. As such, user mobility is usually considered a “trouble maker" and a great challenge to FL over mobile networks. Interestingly, we found that small user mobility can positively contribute to improving FL performance. This is because the total dataset size and the data diversity that the FL can utilize are increased by user mobility. Based on this observation, we aim to tame and exploit mobility instead of treating it as a hostile “trouble maker”. To this end, we first investigate how the FL performance changes with user mobility theoretically by jointly taking into account the positive and negative aspects of mobility. Specifically, a closed-form expression to quantify the impact of mobility on the FL loss is derived, which explains when negative or positive aspects of mobility dominate the FL performance. Next, a joint FL and communication optimization problem is formulated based on theoretical analyses to minimize the FL loss function by optimizing wireless resource allocation. Finally, we propose a two-step optimization algorithm to solve the formulated problem. The simulation results verify the theoretical analyses. It is also shown that the proposed method can significantly enhance learning performance considering users with high mobility. When the average velocity is larger than 150 km/h, the proposed method achieves more than 80% accuracy in the MNIST dataset, while the existing methods may fail during training.

A novel two-step optimization approach for film water cooling of a photovoltaic module in real ambient conditions

Efficiency of photovoltaic (PV) modules decreases as they get warmer in real working conditions. One way to address this issue is to apply water-film cooling on the front surface of a PV module. In order to circulate water over the surface of a module, a power-consuming pumping system is required. This paper presents a novel two-step optimization algorithm for improving the water-film cooling system to maximize the net energy generation of a photovoltaic (PV) panel. In the first step, the fluctuations of the optimal water flow rate during a specified day are calculated and then ideal water flow rates on daily and monthly basis are determined. Since the value of the optimum specific water flow rate is a nonlinear function of ambient conditions, it was calculated through an iterative algorithm. In the second step, the appropriate activation time interval for a water-cooling system was determined to furthermore maximize power gain and minimize energy consumption. By optimizing both water flow rate and cooling system timing, a significant improvement in net efficiency was achieved, increasing from 13.98%, 13.85%, and 13.87% in June, July, and August to 14.82%, 14.86%, and 14.80% in Shiraz, Iran.

A novel method for co-optimizing battery sizing and charging strategy of battery electric bus fleets: An application to the city of Paris

Battery-electric buses (BEBs) are a promising technology for replacing diesel buses and reducing their environmental burden. However, their charging process can take several hours, depending on the charging technique and strategy, making them susceptible to schedule disruptions. Furthermore, the selection of the charging strategy and battery size can increase the total capital investment and operational expenses of a BEB fleet, which is a major obstacle to its adoption. Therefore, to minimize the total cost of ownership (TCO) and prevent schedule disruptions, it is essential to establish a well-defined approach to determine an appropriate battery size and charging strategy for BEB fleets.This paper presents a method for reducing the TCO of BEB by determining the optimal battery size and charging strategy for each bus while satisfying operating constraints. The method involves a two-step optimization algorithm that uses Dynamic Programming and Genetic Algorithm. The study applies this approach to the bus fleet serving line 21 in Paris and generates the optimal battery sizing, charging strategy, and required charging infrastructure. The results indicate that 100 kWh batteries offer the best trade-off between capital and operational expenditure for the fleet deployment if used with 65–85 kW chargers at bus terminals.

Redesign of high-precision reference orbit for interferometric SAR satellite with injection error

A two-step optimization algorithm is proposed to redesign a precision reference repeating sun-synchronous orbit for interferometric synthetic aperture radar satellites with injection error. This purpose is to design a new reference orbit for the satellite that deviates from the original preset orbit. Manoeuvring satellites to this new orbit consumes less propellant than manoeuvring to the original orbit. In addition, the repeatability of the orbit is a guarantee for obtaining good interference images, which puts higher requirements on the revisit accuracy of the redesign reference orbit. In the first step, the initial guess for reference orbital elements is designed by considering the global coverage of the Earth and the propellant consumption, taking into account the J2 and J4 perturbations. A search table of feasible solutions in the neighbourhood of the injection orbit is established. This approach, which is based on a search table, transforms the optimization of continuous variables in infinite space into the traversal of discrete variables in finite space, thus greatly reducing the optimization time. In the second step, the revisit accuracy of the satellite’s full-dimensional state vector is optimized under the orbit propagation of a high-order gravity field. Unlike traditional methods that only optimize the revisit error of position, this paper also optimizes that of velocity. A new optimization method for full-dimensional revisit error of state vector is proposed, which combines nested differential correction with non-dominated sorting genetic algorithm-II. Compared with the traditional genetic algorithms, the proposed nested differential correction method provides the non-dominated sorting genetic algorithm-II with more accurate initial values, which reduces the iterations. Finally, the simulation results show that the total velocity change from the actual injection orbit to the redesigned reference orbit is halved compared to the original preset orbit. Moreover, the revisit errors of position and velocity are within 1m and 1m/s, respectively.

Two-step optimization algorithm for energy management and active-reactive power commands for real-time operation of hybrid microgrids

This paper proposes a two-step optimization algorithm for energy management and optimal control of activereactive power commands in microgrids. The first step ensures an optimal energy management and provides the active power setpoints for power plants and energy storage. The second step calculates the voltage setpoints for power plants and reactive power flows whilst minimizing power transmission losses. This work focuses on the second step of the algorithm and the framework to combine both. A previous study introduces the first step based on a receding-horizon scheme. It is analysed and discussed the performance of the algorithm against two decentralized methods widely used in the literature. The results show that the proposed algorithm reduces power transmission losses by more than 20% compared to other methods.

Building an Effective Classifier for Phishing Web Pages Detection: A Quantum-Inspired Biomimetic Paradigm Suitable for Big Data Analytics of Cyber Attacks.

To combat malicious domains, which serve as a key platform for a wide range of attacks, domain name service (DNS) data provide rich traces of Internet activities and are a powerful resource. This paper presents new research that proposes a model for finding malicious domains by passively analyzing DNS data. The proposed model builds a real-time, accurate, middleweight, and fast classifier by combining a genetic algorithm for selecting DNS data features with a two-step quantum ant colony optimization (QABC) algorithm for classification. The modified two-step QABC classifier uses K-means instead of random initialization to place food sources. In order to overcome ABCs poor exploitation abilities and its convergence speed, this paper utilizes the metaheuristic QABC algorithm for global optimization problems inspired by quantum physics concepts. The use of the Hadoop framework and a hybrid machine learning approach (K-mean and QABC) to deal with the large size of uniform resource locator (URL) data is one of the main contributions of this paper. The major point is that blacklists, heavyweight classifiers (those that use more features), and lightweight classifiers (those that use fewer features and consume the features from the browser) may all be improved with the use of the suggested machine learning method. The results showed that the suggested model could work with more than 96.6% accuracy for more than 10 million query-answer pairs.

Modeling and Calibration of Pressure-Sensing Insoles via a New Plenum-Based Chamber.

This paper proposes a novel method to reliably calibrate a pair of sensorized insoles utilizing an array of capacitive tactile pixels (taxels). A new calibration setup is introduced that is scalable and suitable for multiple kinds of wearable sensors and a procedure for the simultaneous calibration of each of the sensors in the insoles is presented. The calibration relies on a two-step optimization algorithm that, firstly, enables determination of a relevant set of mathematical models based on the instantaneous measurement of the taxels alone, and, then, expands these models to include the relevant portion of the time history of the system. By comparing the resulting models with our previous work on the same hardware, we demonstrate the effectiveness of the novel method both in terms of increased ability to cope with the non-linear characteristics of the sensors and increased pressure ranges achieved during the experiments performed.

Controlling Bias Between Categorical Attributes in Datasets: A Two-Step Optimization Algorithm Leveraging Structural Equation Modeling

Controlling Bias Between Categorical Attributes in Datasets: A Two-Step Optimization Algorithm Leveraging Structural Equation Modeling

Reliable Fuzzy Sampled-Data Control for Nonlinear Suspension Systems Against Actuator Faults

This article investigates the reliable fuzzy sampled-data control issue of nonlinear vehicle suspension systems subject to actuator faults. Considering the high nonlinear characteristics in the springs and the shock absorbers, the Takagi–Sugeno fuzzy method is utilized to describe the nonlinear suspension system. To deal with the discrete phenomenon of nonuniform sampling, an input-delay is employed to convert the continuous-discrete suspension system into a continuous-time form. Motivated by the above system model, a Lyapunov functional candidate is employed to obtain sufficient conditions that can meet system stability and performance requirements simultaneously. Furthermore, a novel two-step optimization algorithm is proposed for the sampled-data controller design in the structure of output feedback. Finally, experimental tests are implemented via an active suspension test rig, and the test results illustrate that the proposed method contributes greatly to improve the ride comfort, handling performance, and road holding capability simultaneously.

Low-Complexity Two-Step Optimization in Active-IRS-Assisted Uplink NOMA Communication

Recently, the active intelligent reflecting surface (IRS) has been proposed to adjust the phase and amplify the magnitude of the incident signal simultaneously. It has a more reasonable hardware overhead compared with the conventional relay. This letter aims to maximize the sum rate of multiple users in the active-IRS-assisted uplink non-orthogonal multiple access (NOMA) system. We propose a low-complexity two-step optimization algorithm to decompose the original non-convex problem into two sub-problems. First, the extreme low-complexity fixed point iteration (FPI) method is proposed to optimize the phase shifts. Then, two algorithms are proposed to solve the amplification optimization problem: the convergence-guaranteed quadratic transform (QT) and the low-complexity generalized eigenvalue decomposition (GEVD) algorithms. Simulation results show that the performance can be enhanced significantly compared with the orthogonal multiple access (OMA) and the passive-IRS scheme.

Optimal Composition of Li Argyrodite with Harmonious Conductivity and Chemical/Electrochemical Stability: Fine‐Tuned Via Tandem Particle Swarm Optimization (Adv. Sci. 28/2022)

Tandem Particle Swarm Optimization In article number 2201648 by Kee-Sun Sohn, Myoungho Pyo, and co-workers, a two-step optimization algorithm (tandem particle swarm optimization) is applied to rapidly identify the optimal argyrodite with the highest attainable σion in a multidimensional search space involving a huge number of compositions and processing conditions. Each particle (bee) is searching for the best candidate by exchanging information through the social behavior. The identified argyrodite exhibits enhanced σion and improved stability against moisture and high voltage and, as an all-solid-state battery, would deliver higher capacities, particularly during fast charge–discharge cycles.

Natural Reweighted Wake–Sleep

Helmholtz Machines (HMs) are a class of generative models composed of two Sigmoid Belief Networks (SBNs), acting respectively as an encoder and a decoder. These models are commonly trained using a two-step optimization algorithm called Wake–Sleep (WS) and more recently by improved versions, such as Reweighted Wake–Sleep (RWS) and Bidirectional Helmholtz Machines (BiHM). The locality of the connections in an SBN induces sparsity in the Fisher Information Matrices associated to the probabilistic models, in the form of a finely-grained block-diagonal structure. In this paper we exploit this property to efficiently train SBNs and HMs using the natural gradient. We present a novel algorithm, called Natural Reweighted Wake–Sleep (NRWS), that corresponds to the geometric adaptation of its standard version. In a similar manner, we also introduce Natural Bidirectional Helmholtz Machine (NBiHM). Differently from previous work, we will show how for HMs the natural gradient can be efficiently computed without the need of introducing any approximation in the structure of the Fisher information matrix. The experiments performed on standard datasets from the literature show a consistent improvement of NRWS and NBiHM not only with respect to their non-geometric baselines but also with respect to state-of-the-art training algorithms for HMs. The improvement is quantified both in terms of speed of convergence as well as value of the log-likelihood reached after training.

A robust power allocation strategy based on benefit–cost ratio for multiple target guidance in the C-MIMO radar system under blanket jamming

How to utilize the limited power budget to accurately track more targets plays a critical role for the radar system in air defense applications, especially in the weapon guidance application. In this paper, we propose a robust power allocation (RPA) strategy in the collocated multiple-input and multiple-output (C-MIMO) radar system for multiple target guidance (MTG) under blanket jamming. The optimization model is established with the aim of increasing the number of effective tracking targets (ETTs) and improving the overall tracking accuracy among those targets. Since the mutual information (MI) quantifies the parameter estimation performance and can be predicted, the MI under blanket jamming is derived and utilized as the optimization criterion. We then propose a two-step optimization algorithm based on benefit–cost ratio (BCR) to solve the non-convex problem. Finally, numerical results are provided to demonstrate the effectiveness of the proposed algorithm.

Two-Step Dynamic Cell Optimization Algorithm for HAPS Mobile Communications

High altitude platform stations are attracting much attention as novel mobile communication platforms for ultra-wide coverage areas and disaster-resilient networks. Multi-cell configurations can increase communication capacity in a wide area. Conventional work optimizes the cell configuration for a multi-cell configuration based on a genetic algorithm (GA). This method identifies an optimal cell configuration in terms of spectral efficiency depending on the number of cells under a uniform user distribution. However, user distributions differ depending on location; thus, cell configuration optimization is also required for non-uniform user distributions. The problem is an increased number of parameters because each cell requires different antenna parameters for non-user distributions compared to a uniform user distribution. This increases the difficulty of optimization, even when using a GA in some cases. Therefore, We propose a GA-based two-step cell optimization algorithm that comprises both search space reduction and antenna parameter optimization steps to address this problem. The proposed method employs the concept of co-evolution, i.e., a divide-and-conquer method. The proposed method divides multiple cells into several groups to reduce the number of optimized parameters and optimizes each group in order. In addition, the search space is reduced based on a marginal histogram obtained via optimization with a large step size. Simulation results demonstrate that this technique can reduce the number of combinations compared to the case without sub-area division. In addition, there are cases where the search space reduction further reduces the number of combinations without degrading the sum of the square root of throughput.

Reliability analysis and ABC based optimization for CoMP-enabled systems over Nakagami-[formula omitted] fading

Providing reliable communications has become one of the major goals for machine-type-oriented applications. In this article, reliable analysis and joint optimization over Nakagami-m fading are investigated for coordinated multi-point (CoMP) assisted multi-cell systems. First, the reliability analysis model for user equipment (UE) served by multiple base stations (BSs) via CoMP technique over Nakagami-m fading is presented. The exact closed-form expression for reliability estimation based on the received signal-to-interference-plus-noise ratio (SINR) value over Nakagami-m fading is derived and verified. Then, the joint sub-carrier assignment and power allocation problem for reliability optimization is formulated. The formulated problem is proved to be NP-hard. Bio-inspired artificial bee colony algorithm (ABC) is thus invoked to tackle this problem, and three ABC based joint optimization frameworks, namely Two-Step ABC Optimization algorithm (TSABC), ABC Combinatorial Optimization algorithm (ABCCO), and Heuristic Two-Step Optimization algorithm (HTSO), are proposed. Simulation results show that the UE reliability can be significantly enhanced by these proposed frameworks. It is also showcased that the proposed ABCCO obtains optimized reliability of 16 nines within 700 generations for most scenarios, which outperforms TSABC, HTSO, and conventional genetic algorithm (GA).

Sparse Bayesian learning for damage identification using nonlinear models: Application to weld fractures of steel‐frame buildings

Sparse Bayesian learning (SBL) is a well-established technique for tackling supervised learning problems, while taking advantage of the prior knowledge that the expected solution is sparse. Based on the premise that initial damage of a structure appears only in a limited number of locations, SBL has been explored for identifying structural damage, showing promising results. Existing SBL methods for structural damage identification use measurements related to modal properties and are thus limited to linear models. In this paper, we present a methodology that allows for application of SBL in nonlinear models, using time history measurements. We develop a two-step optimization algorithm in which the most probable values of the structural model parameters and the hyperparameters are iteratively obtained. An equivalent, single-objective, minimization problem that results in the most probable model parameter values is also derived. We consider the example problem of identifying damage in the form of weld fractures in a 15-story moment-resisting steel-frame building, using a nonlinear finite-element model and simulated acceleration data. Fiber elements and a bilinear material model are used to account for the change in local stiffness when cracks at the welds are subjected to tension, and the model parameters characterize the loss of stiffness as the cracks open under tension. The damage identification results demonstrate the effectiveness and robustness of the proposed methodology in identifying the existence, location, and severity of damage for a variety of different damage scenarios and levels of model and measurement error.

Inversion of bottom parameters using a backscattering model based on the effective density fluid approximation

An inversion method using backscattering strength is used to obtain bottom parameters. A backscattering model based on the effective density fluid approximation was established as the forward model. The objective function used to scale the mismatch between the model predictions and the measured data (obtained by employing two monostatic transducers with narrow directivity) was established on the assumption that the data errors follow a Gaussian distribution. The maximum a posteriori estimations of inversion parameters were obtained by using a two-step hybrid optimization algorithm combining the differential evolution algorithm and the particle swarm optimization algorithm. The uncertainty and the correlation of inversion parameters were presented using marginal probability distributions and a covariance matrix through Bayesian inversion. Using this method, we can acquire not only geoacoustic parameters (including sound speed and attenuation) but also partial physical parameters of marine sediments and statistical character parameters of seafloor roughness and sediment heterogeneity. Finally, the efficiency of this inversion method was verified through comparison between inversion results and sampling data from sand sediment at the bottom of a water tank.

Deep Learning-Enabled Variational Optimization Method for Image Dehazing in Maritime Intelligent Transportation Systems

Image dehazing has become a fundamental problem of common concern in computer vision-driven maritime intelligent transportation systems (ITS). The purpose of image dehazing is to reconstruct the latent haze-free image from its observed hazy version. It is well known that the accurate estimation of transmission map plays a vital role in image dehazing. In this work, the coarse transmission map is firstly estimated using a robust fusion-based strategy. A unified optimization framework is then proposed to estimate the refined transmission map and latent sharp image simultaneously. The resulting constrained minimization model is solved using a two-step optimization algorithm. To further enhance dehazing performance, the solutions of subproblems obtained in this optimization algorithm are equivalent to deep learning-based image denoising. Due to the powerful representation ability, the proposed method can accurately and robustly estimate the transmission map and latent sharp image. Numerous experiments on both synthetic and realistic datasets have been performed to compare our method with several state-of-the-art dehazing methods. Dehazing results have demonstrated the proposed method’s superior imaging performance in terms of both quantitative and qualitative evaluations. The enhanced imaging quality is beneficial for practical applications in maritime ITS, for example, vessel detection, recognition, and tracking.