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The aim of this research is to predict the mechanical properties along with the behaviors of standard insulating paper used in power transformers under thermal aging. This is conducted by applying an artificial neural network (ANN) trained with a multiple regression model and a particle swarm optimization (MR-PSO) model. The aging of the paper insulation is monitored directly by the tensile strength and the degree of polymerization of the solid insulation and indirectly by chemical markers using 2-furfuraldehyde compound content in oil (2-FAL). A mathematical model is then developed to simulate the mechanical properties (degree of polymerization (DPV) and tensile index (Tidx)) of the aged insulation paper. First, the datasets obtained from experimental results are used to create the MR model, and then the optimizer method PSO is used to optimize its coefficients in order to improve the MR model. Then, an ANN method is trained using the MR-PSO to create a nonlinear correlation between the DPV and the time, temperature, and 2-FAL values. The acquired results are assessed and compared with the experimental data. The model presents almost the same behavior. In particular, it has the capability to accurately simulate the nonlinear property behavior of insulation under thermal aging with an acceptable margin of error. Since the life expectancy of power transformers is directly related to that of the insulating paper, the proposed model can be useful to maintenance planners. [ABSTRACT FROM AUTHOR]

This Special Issue compiles papers submitted by the Editorial Board Members of the Vehicular Sensing Section and outstanding scholars in this field. Thus, this paper proposes a deep learning approach, which consists of a two-stage process using an autoencoder and long short-term memory, to detect anomalies in electrical power steering sensor data. The presented topics range from physical models and components to advanced control systems for automated vehicles, including driver monitoring, comfort considerations, communication and cooperative systems, perception sensors and solutions to issues related to electric vehicles. The authors test these types of sensors under different weather conditions and employ fake targets between the sensor and the tracked vehicle. [Extracted from the article]

This paper presents a comprehensive study and analysis of the intelligent grouping of papers in adult higher education using a random matrix approach. Using the results of random matrix theory on the eigenvalues of the sample covariance matrix, the energy of each subspace is estimated, and the estimated energy is then used to construct a subspace weighting matrix. The statistical properties of the sample covariance matrix eigenvectors are analyzed using the first-order perturbation approximation, and then, asymptotic results from random matrix theory on the projection of the sample covariance matrix signal subspace to the real signal parametrization are used to obtain the weighting matrix based on the random matrix eigenvectors. Dynamic adjustment according to the fitness of individuals in the population is performed to ensure population diversity, while the combination of the small habitat technique can avoid the algorithm from falling into early convergence. The algorithm introduces chaos theory to optimize the population initialization process and uses the dynamic traversal randomness of chaos to select individuals in the population so that the initial population is close to the desired target solution. The design of the fitness function in the genetic algorithm generally maps the objective function of the problem to the fitness function. A good fitness function can directly reflect the quality of the individuals in the group. Based on the in-depth study of the basic attributes of the test questions and the principles of test paper evaluation, the mathematical model and objective function of intelligent paper grouping are determined by the difficulty, knowledge points, and cognitive level of the test questions as the main constraints, and NCAGA is applied to the intelligent paper grouping method, which better completes the intelligent paper grouping session for the computer system architecture course. In the process of designing the intelligent grouping algorithm, for the situations of premature convergence and convergence to locally optimal solutions that easily occur in the traditional genetic algorithm, this paper adopts the approach of adaptive adjustment of crossover probability and variation probability to improve the algorithm and achieves satisfactory results. Based on extensive business research, this paper completes the requirement analysis of the online practice system based on the intelligent grouping of papers and presents the functional design and database design of the key functional modules in the system in detail. Finally, this paper conducts functional tests on the system and analyses the test results. [ABSTRACT FROM AUTHOR]

Summary: This article proposes a methodology based on artificial intelligence to enhance methane production from waste paper. The proposed methodology combines fuzzy logic‐based modelling and modern optimization. Firstly, a robust Adaptive Network‐based Fuzzy Inference System model of methane production process through fuzzy logic modelling is created using experimental datasets. Second, a particle swarm optimizer was used to obtain the optimal process conditions. During the optimization procedure, the beating time and feedstock/inoculum ratio are employed as decision variables in order to maximize methane production. The obtained resulted from the proposed methodology are compared with those obtained by response surface methodology. The results of the comparison confirmed the superiority of the proposed methodology. The fuzzy model shows a better fitting to the experimental data compared to ANOVA. The fuzzy model showed a higher coefficient of determination and a lower value of root mean squared errors compared to ANOVA. Moreover, the proposed strategy, that is, modelling and optimization, is an effective method for increasing the biomethane yield at extended range conditions. [ABSTRACT FROM AUTHOR]

Our dependency on wood has reduced the number of forests, especially in Indonesia. Hence, many people tried to use another material as a replacement for wood. One of them is composite. Department of Industrial Engineering Universitas Sebelas Maret has developed paperbased composite using newspaper waste in Praktikum Perancangan Teknik Industri (PPTI) II. Unfortunately, the quality of the composite is not so good. Some of the composites are broken when dried up below the sunlight, so we have to do some reworks. Besides, based on historical data, the average tensile, bending, and impact strength of composites in the last practicum are 0, 42 N/mm2, 2,68 N/mm2, and 0,0203 J/mm2. It is still below the Indonesian National Standard (SNI) of particleboard. This paper tried to understand the effect of factors on responses and discover the best combination of factors to obtain the best responses. The responses of this experiment are tensile strength, bending strength and impact value. The factors of this experiment are paper pulp, PVAc glue and water. Taguchi method is used to find the appropriate orthogonal array and Particle Swarm Optimization method to find the optimum combination of the factors. The orthogonal array used in this experiment is L9 (34). Through analysis, all of the factors are significant to tensile and bending strength, but they are not significant to impact value. The best combination of factors is 175 grams paper pulp, 130 grams PVAc glue and 175 ml water. Optimum results obtained for tensile strength of 0.7595 N/mm2 and bending strength of 4.8411 N/mm2. [ABSTRACT FROM AUTHOR]

Antennas play a crucial role in designing an efficient communication system. However, reducing the maximum sidelobe level (SLL) of the beam pattern is a crucial challenge in antenna arrays. Pattern synthesis in smart antennas is a major area of research because of its widespread application across various radar and communication systems. This paper presents an effective technique to minimize the SLL and thus improve the radiation pattern of the linear antenna array (LAA) using the chaotic inertia-weighted Wild Horse optimization (IERWHO) algorithm. The wild horse optimizer (WHO) is a new metaheuristic algorithm based on the social behavior of wild horses. The IERWHO algorithm is an improved Wild Horse optimization (WHO) algorithm that combines the concepts of chaotic sequence factor, nonlinear factor, and inertia weights factor. In this paper, the method is applied for the first time in antenna array synthesis by optimizing parameters such as inter-element spacing and excitation to minimize the SLL while keeping other constraints within the boundary limits, while ensuring that the performance is not affected. For performance evaluation, the simulation tests include 12 benchmark test functions and 12 test functions to verify the effectiveness of the improvement strategies. According to the encouraging research results in this paper, the IERWHO algorithm proposed has a place in the field of optimization. [ABSTRACT FROM AUTHOR]

The paper proposes a new stochastic multiobjective technoeconomic model for integrating photovoltaic (PV) and wind energy resources in electricity price (EP)‐driven distribution systems. The primary goal of this paper is to determine the optimal location and capacity for renewable energy‐based distributed generation, specifically PV and wind resources, while considering weather and system uncertainties. These uncertainties include stochastic variations in PV illumination intensity, wind speed, EP, and load fluctuations. To address these uncertainties, the paper employs scenario modeling techniques named as Latin hypercube sampling with Cholesky decomposition. This technique generates multiple correlated scenarios that represent uncertain variables. Subsequently, a scenario reduction technique is applied to identify the scenario with the highest probability. Later, a mathematical model is developed to minimize an objective function that encompasses various factors like system losses, node voltage deviations, the cost of purchasing power from the grid; and simultaneously maximize the total annual energy savings. The objective is to find optimal solutions that strike a balance between different objectives. To obtain an efficient optimum solution, this paper employs an effective meta‐heuristic technique named as JAYA algorithm. The results obtained by the JAYA algorithm are juxtaposed with those obtained using particle swarm optimization and genetic algorithm techniques. The proposed method is evaluated using Institute of Electrical and Electronics Engineers (IEEE) 33‐node and IEEE 69‐node test feeders to validate its feasibility and effectiveness. However, the effectiveness of the proposed method is not limited to any size of test systems. [ABSTRACT FROM AUTHOR]

Civil aircraft represent intricate technical systems, and their flight test is a system engineering process. This process is essential for verifying their compliance with airworthiness regulations and operational requirements. In this process, planning flight test missions is a type of combinatorial optimization problem. The solution gives an optimal allocation plan adhering to operational constraints. Assigning flight test missions for civil aircraft involves numerous uncertainties and very often, requires unforeseen adjustments. The original missions need to be reassigned due to these adjustments in a scientific way. In this paper, considering the scenarios that emerged during real civil aircraft flight tests, a multi-objective flight test mission planning model is established based on the Flexible Job Shop Problem (FJSP) model. The model is specifically tailored to the flight text mission reassignment. Examples of flight test scenarios that require mission reassignments are addressed using the FJSP model proposed in this paper and optimized using the Particle Swarm Optimization algorithm. Two rounds of initial population updates were conducted to obtain overall extreme values. The results verify the feasibility of obtaining a viable solution for the flight test mission assignment problem under complex constraints derived from real engineering requirements. These solutions can be used to address similar types of engineering problems. [ABSTRACT FROM AUTHOR]

The problem of finding optimal block designs can be formulated as a combinatorial optimization, but its resolution is still a formidable challenge. This paper presents a general and user-friendly algorithm, namely Modified Particle Swarm Optimization (MPSO), to construct optimal or near-optimal block designs. It can be used for several classes of block designs such as binary, non-binary and test-control block designs with correlated or uncorrelated observations. In order to evaluate the algorithm, we compare our results with the optimal designs presented in some published papers. An advantage of our algorithm is its independency to the sizes of blocks and the structure of correlations. [ABSTRACT FROM AUTHOR]

In order to reduce pollution caused by ship emissions, the International Maritime Organization (IMO) implemented sulfur emission control areas (SECAs). In comparison to ordinary vessels, cruise ships with dual attributes of transportation and tourism generate a greater amount of marine pollution, which poses a significant threat to the marine environment in both berthing ports and the sailing area. In light of the fierce competition of the cruise tourism market, cruise lines are looking for strategies, such as designing more attractive cruise routes, to maintain their core competencies under the emission control policy. In order to achieve this goal, this paper presents a mixed-integer non-linear programming (MINP) model with two objectives and is derived from the traditional route optimization problem. The primary objective is to optimize the route and speed of a cruise liner, while simultaneously enhancing route competitiveness and minimizing carbon emissions both within and outside the SECAs. Subsequently, the multi-objective particle swarm optimization (MOPSO) algorithm was used to reach the objective, and simulations were carried out to verify the effectiveness of the model and method. The results show that speed and sailing route optimization can affect carbon emissions. This paper has a certain application value and guiding significance for cruise line decision makers that will be beneficial for the environment. [ABSTRACT FROM AUTHOR]

A Virtual Power Plant (VPP) is a centralized energy system that manages, and coordinates distributed energy resources, integrating them into a unified entity. While the physical assets may be dispersed across various locations, the VPP integrates them into a virtual unified entity capable of responding to grid demands and market signals. This paper presents a tri-level hierarchical coordinated operational framework of VPP. Firstly, an Improved Pelican Optimization Algorithm (IPOA) is introduced to optimally schedule Distributed Energy Resources (DERs) within the VPP, resulting in a significant reduction in generation costs. Comparative analysis against conventional algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) demonstrates IPOA's superior performance, achieving an average reduction of 8.5% in generation costs across various case studies. The second stage focuses on securing the optimized generation data from rising cyber threats, employing the capabilities of machine learning, preferably, a convolutional autoencoder to learn the normal patterns of the optimized data to detect deviations from the optimized generation data to prevent suboptimal decisions. The model exhibits exceptional performance in detecting manipulated data, with a False Positive Rate (FPR) of 1.92% and a Detection Accuracy (DA) of 98.06%, outperforming traditional detection techniques. Lastly, the paper delves into the dynamic nature of the day ahead market that the VPP participates in. In responding to the grid by selling its optimized generated power via the day-ahead market, the VPP employs the Prophet model, another machine learning technique to forecast the spot market price for the day-ahead to mitigate the adverse effects of price volatility. By utilizing Prophet forecasts, the VPP achieves an average revenue increase of 15.3% compared to scenarios without price prediction, emphasizing the critical role of predictive analytics in optimizing economic gains. This tri-level coordinated approach adopted addresses key challenges in the energy sector, facilitating progress towards achieving universal access to clean and affordable energy. [ABSTRACT FROM AUTHOR]

With the rapid development of computer technology, communication technology, and control technology, cyber-physical systems (CPSs) have been widely used and developed. However, there are massive information interactions in CPSs, which lead to an increase in the amount of data transmitted over the network. The data communication, once attacked by the network, will seriously affect the security and stability of the system. In this paper, for the data tampering attack existing in the linear system with multiple binary observations, in the case where the estimation algorithm of the defender is unknown, the optimization index is constructed based on information entropy from the attacker's point of view, and the problem is modeled. For the problem of the multi-parameter optimization with energy constraints, this paper uses particle swarm optimization (PSO) to obtain the optimal data tampering attack solution set, and gives the estimation method of unknown parameters in the case of unknown parameters. To implement the real-time improvement of online implementation, the BP neural network is designed. Finally, the validity of the conclusions is verified through numerical simulation. This means that the attacker can construct effective metrics based on information entropy without the knowledge of the defense's discrimination algorithm. In addition, the optimal attack strategy implementation based on PSO and BP is also effective. [ABSTRACT FROM AUTHOR]

The transverse leaf spring (TLS) suspensions are a promising option for van vehicles due to their high load-carrying capacity and excellent handling stability. However, its ride comfort remains a major challenge. This paper investigates and compares the effects of semi-active and active control strategies to enhance the ride comfort of TLS suspensions. Firstly, a four-degree-of-freedom (4-DOF) half-car model and a multi-body dynamics (MBD) model of the TLS suspensions are established. The MBD model has higher accuracy and can describe the medium and high frequency characteristics of the TLS suspensions, such as the suspension offset frequency and the frequency response function of the body vertical acceleration (BVA). Therefore, based on the MBD half-car model with TLS suspensions, this paper proposes an optimal fuzzy PID active control strategy considering the left and right suspension coupling. The optimization objectives are the BVA, the left and right suspensions dynamic deflection, and the left and right wheels dynamic displacement. The integral absolute error is used as the evaluation criterion. The left and right fuzzy PID controllers' parameters are obtained through particle swarm optimization. Simulation results demonstrate that the particle swarm optimization fuzzy PID active control strategy effectively controls the low-frequency vibration of the TLS suspensions and suppresses the medium- and high-frequency vibration characteristics compared with the traditional skyhook semi-active control strategy. This technology provides a reference for improving the ride comfort of the TLS suspensions. [ABSTRACT FROM AUTHOR]

As one of the dimensionality reduction techniques of hyperspectral image (HSI), band selection (BS) does not change the spectral characteristics and physical meaning of HSIs, which is beneficial to the identification and analysis of surface objects. Recently, many BS methods for target detection have achieved promising results by making full use of the priori spectral features of the target to be detected. Conversely, anomaly detection separates the anomaly based solely on the statistical distribution difference between anomaly and background without any prior information. Therefore, the development of BS for anomaly detection has lagged far behind that of BS for target detection. To this end, this paper proposes a novel BS algorithm dedicated to anomaly detection tasks, named anomaly‐background separation and particle swarm optimization (PSO)‐based BS. Specifically, an anomaly‐background separation framework (ABSF) is established to predetermine a priori knowledge of anomaly distribution. Then, three band prioritization criteria are constructed with the anomaly‐background constraints generated by ABSF. Finally, PSO is used to find the optimal subset of bands in the solution space. The experiments on two real datasets demonstrate that the proposed method yields better detection results and greater stability compared to other BS methods discussed in this paper. [ABSTRACT FROM AUTHOR]

Addressing the formidable challenges posed by multiple jammers jamming multiple radars, which arise from spatial discretization, many degrees of freedom, numerous model input parameters, and the complexity of constraints, along with a multi-peaked objective function, this paper proposes a cooperative jamming resource allocation method, based on evolutionary reinforcement learning, that uses joint multi-domain information. Firstly, an adversarial scenario model is established, characterizing the interaction between multiple jammers and radars based on a multi-beam jammer model and a radar detection model. Subsequently, considering real-world scenarios, this paper analyzes the constraints and objective function involved in cooperative jamming resource allocation by multiple jammers. Finally, accounting for the impact of spatial, frequency, and energy domain information on jamming resource allocation, matrices representing spatial condition constraints, jamming beam allocation, and jamming power allocation are formulated to characterize the cooperative jamming resource allocation problem. Based on this foundation, the joint allocation of the jamming beam and jamming power is optimized under the constraints of jamming resources. Through simulation experiments, it was determined that, compared to the dung beetle optimizer (DBO) algorithm and the particle swarm optimization (PSO) algorithm, the proposed evolutionary reinforcement learning algorithm based on DBO and Q-Learning (DBO-QL) offers 3.03% and 6.25% improvements in terms of jamming benefit and 26.33% and 50.26% improvements in terms of optimization success rate, respectively. In terms of algorithm response time, the proposed hybrid DBO-QL algorithm has a response time of 0.11 s, which is 97.35% and 96.57% lower than the response times of the DBO and PSO algorithms, respectively. The results show that the method proposed in this paper has good convergence, stability, and timeliness. [ABSTRACT FROM AUTHOR]

The software-defined network paradigm, ensembled with a wireless sensor network, has emerged as a promising phenomenon to enable multi-tasking, re-configuration, and scalability. Termed the software-defined wireless sensor network (SDWSN), it divides the network into two planes: data and control. The data plane consists of software-defined sensor nodes (SDSN) that sense monitoring activity and generate data. On the other hand, the control plane has controller/control Nodes (CN) which collect data from SDSN, perform data aggregation, and then transmit toward the sink node. These CNs consume more amount of energy as compared to SDSNs as they perform multiple tasks. Following this scenario, this paper proposes an energy-efficient multi-objective optimization approach to solve the CNs placement problem through a meta-heuristic algorithm by considering the nodes' location, energy, and load distribution. This paper presents a particle swarm optimization-based controller placement and re-placement (PSO-CPR) algorithm for SDWSN. The PSO-CPR elects SDSNs to become CN based on their distance, residual energy, and capacity in the network. Moreover, the placement of a CN rotates within the cluster to avoid its failure and balance energy consumption. The simulation results show improved CN placement with respect to the state-of-the-art algorithms in terms of average delay by 23.5–37.4%, energy consumption by 18.6–32.6%, and probabilistic load distribution by 17.7–54.1%. Moreover, the comparative study also indicates that PSO-CPR achieve promising result by reducing packet loss by 14.4–27.5% and network re-clustering period by 32.3–68.3% and enhance the network lifetime by 22.6–42.5%. [ABSTRACT FROM AUTHOR]

With the development of science and technology, vehicles have gradually become smart network vehicles. However, applications on vehicles require great network and computing performance. It poses a great challenge to the existing Internet of Vehicles (IoV). Cloud computing can relieve the pressure of data processing, but the response time could take a long time. In the edge computing, edge server processing requests can effectively reduce response time. For example, during rush hour, in order to respond quickly, a large number of requests can be sent from the vehicles layer to the edge server. However, due to the limited bandwidth resources of edge servers, it is necessary to develop an effective bandwidth allocation model. The research in this paper consists of two main parts. First, this paper proposes a multi-objective bandwidth allocation optimization model for addressing this issue. The goal is to minimize average response time and maximize bandwidth utilization. Second, a multi-objective sparrow search algorithm (KMSSA) based on K-means algorithm is proposed to solve multi-objective bandwidth allocation. This paper compares KMSSA with particle swarm optimization (PSO) and sparrow search algorithm (SSA) on iFogSim platform. The results show that KMSSA has good optimization performance. The algorithm can be successfully applied to solve global multi-objective optimization problems. To compare with PSO algorithm, the performance of KMSSA is improved by 10%. To compare with SSA, the performance of KMSSA is improved by 5%. Also, KMSSA has superiority in solving practical problems. Its global optimal solution has superior performance in terms of average response time, bandwidth utilization, energy consumption, and cost. [ABSTRACT FROM AUTHOR]

Large-scale particle swarm optimization (PSO) has long been a hot topic due to the following reasons: Swarm diversity preservation is still challenging for current PSO variants for large-scale optimization problems, resulting in difficulties for PSO in balancing its exploration and exploitation. Furthermore, current PSO variants for large-scale optimization problems often introduce additional operators to improve their ability in diversity preservation, leading to increased algorithm complexity. To address these issues, this paper proposes a dual-competition-based particle update strategy (DCS), which selects the particles to be updated and corresponding exemplars with two rounds of random pairing competitions, which can straightforwardly benefit swarm diversity preservation. Furthermore, DCS confirms the primary and secondary exemplars based on the fitness sorting operation for exploitation and exploration, respectively, leading to a dual-competition-based swarm optimizer. Thanks to the proposed DCS, on the one hand, the proposed algorithm is able to protect more than half of the particles from being updated to benefit diversity preservation at the swarm level. On the other hand, DCS provides an efficient exploration and exploitation exemplar selection mechanism, which is beneficial for balancing exploration and exploitation at the particle update level. Additionally, this paper analyzes the stability conditions and computational complexity of the proposed algorithm. In the experimental section, based on seven state-of-the-art algorithms and a recently proposed large-scale benchmark suite, this paper verifies the competitiveness of the proposed algorithm in large-scale optimization problems. [ABSTRACT FROM AUTHOR]

This paper proposes an improved dual-center particle swarm optimization (IDCPSO) algorithm which can effectively improve some inherent defects of particle swarm optimization algorithms such as being prone to premature convergence and low optimization accuracy. Based on the in-depth analysis of the velocity updating formula, the most innovative feature is the vectorial decomposition of the velocity update formula of each particle to obtain three different flight directions. After combining these three directions, six different flight paths and eight intermediate positions can be obtained. This method allows the particles to search for the optimal solution in a wider space, and the individual extreme values are greatly improved. In addition, in order to improve the global extreme value, it is designed to construct the population virtual center and the optimal individual virtual center by using the optimal position and the current position searched by the particle. Combining the above strategies, an adaptive mutation factor that accumulates the coefficient of mutation according to the number of iterations is added to make the particle escape from the local optimum. By running the 12 typical test functions independently 50 times, the results show an average improvement of 97.9% for the minimum value and 97.7% for the average value. The IDCPSO algorithm in this paper is better than other improved particle swarm optimization algorithms in finding the optimum. [ABSTRACT FROM AUTHOR]

Large thin-walled parts are generally featured with large in-plane dimension and thin thickness. This kind of parts deforms easily under the action of gravity due to their low out-of-plane stiffness in the assembly process. In order to reduce the part deformation, the "N-2–1" locating principle is used for large thin-walled parts. The part deformation leads to the assembly gap between two parts, which affects the assembly quality. In this paper, the influence of the fixture layout on the assembly gap is considered. The assembly gap is taken as the optimization objective to minimize by integrating the modified direct stiffness method (MDSM) and the improved particle swarm optimization (IPSO) algorithm. Then, the assembly of two ship parts is studied and the results show that the optimization method proposed in this paper can effectively reduce the assembly gap between two large thin-walled parts. [ABSTRACT FROM AUTHOR]

Finding the optimum conductor's pattern for unequally spaced grounding grids can be achieved by using empirical formulas or optimization techniques. Empirical formulas give an approximate solution depending on human experience and never cover the entire search space. On the other hand, the optimization techniques generally require a significant effort and time. To overcome the previous issues, this paper presents a closed-form solution based on optimized results. The main motivation behind this formula is the comprehensive study carried out by the authors to find optimum location of grounding grid conductors over different areas. In that work, particle swarm optimization technique (PSO) has been adapted with combined integration matrix method (CIMM) to facilitate the study. In this paper, the results obtained previously were summarized in the proposed formula. The proposed formula gives the optimum design of unequally grounding grids by specifying the conductor's locations as percentage of the side length for square and rectangular shapes. As long as the side length and the number of conductors are given, the location of each conductor as a percentage of the side length can be obtained. The effect of adding driven rods and the stratified soil conditions on the results of the optimized design were investigated. The validity of the proposed closed-form solution was verified experimentally. [ABSTRACT FROM AUTHOR]

Reservoir flood control scheduling is a challenging optimization task, particularly due to the complexity of various constraints. This paper proposes an innovative algorithm design approach to address this challenge. Combining the basic walrus optimization algorithm with the adaptive ε-constraint method and introducing the SPM chaotic mapping for population initialization, spiral search strategy, and local enhancement search strategy based on Cauchy mutation and reverse learning significantly enhances the algorithm's optimization performance. On this basis, innovate an adaptive approach ε A New Algorithm for Constraints and Multi Strategy Optimization Improvement (ε-IWOA). To validate the performance of the ε-IWOA algorithm, 24 constrained optimization test functions are used to test its optimization capabilities and effectiveness in solving constrained optimization problems. Experimental results demonstrate that the ε-IWOA algorithm exhibits excellent optimization ability and stable performance. Taking the Taolinkou Reservoir, Daheiting Reservoir, and Panjiakou Reservoir in the middle and lower reaches of the Luanhe River Basin as a case study, this paper applies the ε-IWOA algorithm to practical reservoir scheduling problems by constructing a three-reservoir flood control scheduling system with Luanxian as the control point. A comparative analysis is conducted with the ε-WOA, ε-DE and ε-PSO (particle swarm optimization) algorithms.The experimental results indicate that ε-IWOA algorithm performs the best in optimization, with the occupied flood control capacity of the three reservoirs reaching 89.32%, 90.02%, and 80.95%, respectively. The control points in Luan County can reduce the peak by 49%.This provides a practical and effective solution method for reservoir optimization scheduling models. This study offers new ideas and solutions for flood control optimization scheduling of reservoir groups, contributing to the optimization and development of reservoir scheduling work. [ABSTRACT FROM AUTHOR]

Accurately predicting the price of carbon is an effective way of ensuring the stability of the carbon trading market and reducing carbon emissions. Aiming at the non-smooth and non-linear characteristics of carbon price, this paper proposes a novel hybrid prediction model based on improved feature extraction and non-linear integration, which is built on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), fuzzy entropy (FuzzyEn), improved random forest using particle swarm optimisation (PSORF), extreme learning machine (ELM), long short-term memory (LSTM), non-linear integration based on multiple linear regression (MLR) and random forest (MLRRF), and error correction with the autoregressive integrated moving average model (ARIMA), named CEEMDAN-FuzzyEn-PSORF-ELM-LSTM-MLRRF-ARIMA. Firstly, CEEMDAN is combined with FuzzyEn in the feature selection process to improve extraction efficiency and reliability. Secondly, at the critical prediction stage, PSORF, ELM, and LSTM are selected to predict high, medium, and low complexity sequences, respectively. Thirdly, the reconstructed sequences are assembled by applying MLRRF, which can effectively improve the prediction accuracy and generalisation ability. Finally, error correction is conducted using ARIMA to obtain the final forecasting results, and the Diebold–Mariano test (DM test) is introduced for a comprehensive evaluation of the models. With respect to carbon prices in the pilot regions of Shenzhen and Hubei, the results indicate that the proposed model has higher prediction accuracy and robustness. The main contributions of this paper are the improved feature extraction and the innovative combination of multiple linear regression and random forests into a non-linear integrated framework for carbon price forecasting. However, further optimisation is still a work in progress. [ABSTRACT FROM AUTHOR]

The sand cat swarm optimization algorithm (SCSO) is a novel metaheuristic algorithm that has been proposed in recent years. The algorithm optimizes the search ability of individuals by mimicking the hunting behavior of sand cat groups in nature, thereby achieving robust optimization performance. It is characterized by few control parameters and simple operation. However, due to the lack of population diversity, SCSO is less efficient in solving complex problems and is prone to fall into local optimization. To address these shortcomings and refine the algorithm's efficacy, an improved multi-strategy sand cat optimization algorithm (IMSCSO) is proposed in this paper. In IMSCSO, a roulette fitness–distance balancing strategy is used to select codes to replace random agents in the exploration phase and enhance the convergence performance of the algorithm. To bolster population diversity, a novel population perturbation strategy is introduced, aiming to facilitate the algorithm's escape from local optima. Finally, a best–worst perturbation strategy is developed. The approach not only maintains diversity throughout the optimization process but also enhances the algorithm's exploitation capabilities. To evaluate the performance of the proposed IMSCSO, we conducted experiments in the CEC 2017 test suite and compared IMSCSO with seven other algorithms. The results show that the IMSCSO proposed in this paper has better optimization performance. [ABSTRACT FROM AUTHOR]

One of the limitations of the dung beetle optimization (DBO) is its susceptibility to local optima and its relatively low search accuracy. Several strategies have been utilized to improve the diversity, search precision, and outcomes of the DBO. However, the equilibrium between exploration and exploitation has not been achieved optimally. This paper presents a novel algorithm called the ODBO, which incorporates cat map and an opposition-based learning strategy, which is based on symmetry theory. In addition, in order to enhance the performance of the dung ball rolling phase, this paper combines the global search strategy of the osprey optimization algorithm with the position update strategy of the DBO. Additionally, we enhance the population's diversity during the foraging phase of the DBO by incorporating vertical and horizontal crossover of individuals. This introduction of asymmetry in the crossover operation increases the exploration capability of the algorithm, allowing it to effectively escape local optima and facilitate global search. [ABSTRACT FROM AUTHOR]

This paper presents an improved swarming algorithm that enhances low-illumination images. The algorithm combines a hybrid Harris Eagle algorithm with double gamma (IHHO-BIGA) and incomplete beta (IHHO-NBeta) functions. This paper integrates the concept of symmetry into the improvement steps of the image adaptive enhancement algorithm. The enhanced algorithm integrates chaotic mapping for population initialization, a nonlinear formula for prey energy calculation, spiral motion from the black widow algorithm for global search enhancement, a nonlinear inertia weight factor inspired by particle swarm optimization, and a modified Levy flight strategy to prevent premature convergence to local optima. This paper compares the algorithm's performance with other swarm intelligence algorithms using commonly used test functions. The algorithm's performance is compared against several emerging swarm intelligence algorithms using commonly used test functions, with results demonstrating its superior performance. The improved Harris Eagle algorithm is then applied for image adaptive enhancement, and its effectiveness is evaluated on five low-illumination images from the LOL dataset. The proposed method is compared to three common image enhancement techniques and the IHHO-BIGA and IHHO-NBeta methods. The experimental results reveal that the proposed approach achieves optimal visual perception and enhanced image evaluation metrics, outperforming the existing techniques. Notably, the standard deviation data of the first image show that the IHHO-NBeta method enhances the image by 8.26%, 120.91%, 126.85%, and 164.02% compared with IHHO-BIGA, the single-scale Retinex enhancement method, the homomorphic filtering method, and the limited contrast adaptive histogram equalization method, respectively. The processing time of the improved method is also better than the previous heuristic algorithm. [ABSTRACT FROM AUTHOR]

A lithium-ion battery–ultracapacitor hybrid energy storage system (HESS) has been recognized as a viable solution to address the limitations of single battery energy sources in electric vehicles (EVs), especially in urban driving conditions, owing to its complementary energy features. However, an energy management strategy (EMS) is required for the optimal performance of the HESS. In this paper, an EMS based on the particle swarm optimization (PSO) of the fuzzy logic controller (FLC) is proposed. It aims to minimize battery current and power peak fluctuations, thereby enhancing its capacity and lifespan, by optimizing the weights of formulated FLC rules using the PSO algorithm. This paper utilizes the battery temperature as the cost function in the optimization problem of the PSO due to the sensitivity of lithium-ion batteries (LIBs) to operating temperature variations compared to ultracapacitors (UCs). An evaluation of optimized FLC using PSO and a developed EV model is conducted under the Urban Dynamometer Driving Schedule (UDDS) and compared with the unoptimized FLC. The result shows that 5.4% of the battery's capacity was conserved at 25.5 °C, which is the highest operating temperature attained under the proposed strategy. [ABSTRACT FROM AUTHOR]

The emergence of microgrids arises from the growing integration of Renewable Energy Resources (RES) and Energy Storage Systems (ESSs) into Distribution Networks (DNs). Effective integration, coordination, and control of Multiple Microgrids (MMGs) whereas navigating the complexities of energy transition within this context poses a significant challenge. The dynamic operation of MMGs is a challenge faced by the traditional distributed hierarchical control techniques. The application of Artificial Intelligence (AI) techniques is a promising way to improve the control and dynamic operation of MMGs in future smart DNs. In this paper, an innovative hybrid optimization technique that originates from Cheetah Optimization (CHO) and Particle Swarm Optimization (PSO) techniques is proposed, known as HYCHOPSO. Extensive benchmark testing validates HYCHOPSO's superiority over CHO and PSO in terms of convergence performance. The objective for this hybridization stems from the complementary strengths of CHO and PSO. CHO demonstrates rapid convergence in local search spaces, while PSO excels in global exploration. By combining these techniques, the aim is to leverage their respective advantages and enhance the algorithm's overall performance in addressing complex optimization problems. The contribution of this paper offering a unique approach to addressing optimization challenges in microgrid systems. Through a comprehensive comparative study, HYCHOPSO is evaluated against various metaheuristic optimization approaches, demonstrating superior performance, particularly in optimizing the design parameters of Proportional-Integral (PI) controllers for hierarchical control systems within microgrids. This contribution expands the repertoire of available optimization methodologies and offers practical solutions to critical challenges in microgrid optimization, enhancing the efficiency, reliability, and sustainability of microgrid operations. HYCHOPSO achieves its optimal score within fewer than 50 iterations, unlike CHO, GWO, PSO, Hybrid-GWO-PSO, and SSIA-PSO, which stabilize after around 200 iterations. Across various benchmark functions, HYCHOPSO consistently demonstrates the lowest mean values, attains scores closer to the optimal values of the benchmark functions, underscoring its robust convergence capabilities.the proposed HYCHOPSO algorithm, paired with a PI controller for distributed hierarchical control, minimizes errors and enhances system reliability during dynamic MMG operations. Using HYCHOPSO framework, an accurate power sharing, voltage/frequency stability, seamless grid-to-island transition, and smooth resynchronization are achieved. This enhances the real application's reliability, flexibility, scalability and robustness. [ABSTRACT FROM AUTHOR]

Energy-intensive enterprises lack a scientific and effective energy efficiency assessment framework and methodology. This lack leads to an inaccurate understanding of energy usage and its benefits. As a result, there is energy wastage and loss. This wastage and loss negatively affect product costs. They also present a challenge to effective energy management. To address these issues, this paper introduces a novel, comprehensive energy efficiency evaluation system. This system integrates both qualitative and quantitative measures. It proposes an evaluation model based on the Particle Swarm Optimization (PSO) combined with the Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation (FCE), wherein PSO is employed to optimize the weights determined by AHP, ensuring that the significance attributed to various indicators is scientific, objective, and rational. The FCE method is utilized to convert diverse factors affecting corporate energy efficiency, across different types and scales, into standardized 0–1 values, enabling a comparative analysis of the impact of each process and indicator on energy efficiency. Furthermore, the paper introduces an energy efficiency prediction model employing a multivariate linear regression algorithm, which demonstrates a good fit, facilitating the transition from retrospective energy efficiency evaluation to proactive improvements. Utilizing data on actual consumption of water, electricity, and steam from an enterprise, along with expert assessments on the implementation levels of new processes, technologies, equipment, personnel scheduling proficiency, steam recovery rates, and adherence to policies and assessments, a simulation experiment of the proposed model was conducted using Python. The evaluation yielded an energy efficiency score of 0.68; this is consistent with the real-world scenario of the studied enterprise. The predicted mean square error of 9.035416039503998 ×   10 − 9 indicates a high model accuracy, validating the practical applicability and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Cryo-electron micrograph images have various characteristics such as varying sizes, shapes, and distribution densities of individual particles, severe background noise, high levels of impurities, irregular shapes, blurred edges, and similar color to the background. How to demonstrate good adaptability in the field of image vision by picking up single particles from multiple types of cryo-electron micrographs is currently a challenge in the field of cryo-electron micrographs. This paper combines the characteristics of the MixUp hybrid enhancement algorithm, enhances the image feature information in the pre-processing stage, builds a feature perception network based on the channel self-attention mechanism in the forward network of the Swin Transformer model network, achieving adaptive adjustment of self-attention mechanism between different single particles, increasing the network's tolerance to noise, Incorporating PReLU activation function to enhance information exchange between pixel blocks of different single particles, and combining the Cross-Entropy function with the softmax function to construct a classification network based on Swin Transformer suitable for cryo-electron micrograph single particle detection model (Swin-cryoEM), achieving mixed detection of multiple types of single particles. Swin-cryoEM algorithm can better solve the problem of good adaptability in picking single particles of many types of cryo-electron micrographs, improve the accuracy and generalization ability of the single particle picking method, and provide high-quality data support for the three-dimensional reconstruction of a single particle. In this paper, ablation experiments and comparison experiments were designed to evaluate and compare Swin-cryoEM algorithms in detail and comprehensively on multiple datasets. The Average Precision is an important evaluation index of the evaluation model, and the optimal Average Precision reached 95.5% in the training stage Swin-cryoEM, and the single particle picking performance was also superior in the prediction stage. This model inherits the advantages of the Swin Transformer detection model and is superior to mainstream models such as Faster R-CNN and YOLOv5 in terms of the single particle detection capability of cryo-electron micrographs. [ABSTRACT FROM AUTHOR]

This paper presents a novel hybrid algorithm that combines the Butterfly Optimization Algorithm (BOA) and Quantum-behavior Particle Swarm Optimization (QPSO) algorithms, leveraging [Math Processing Error] g b e s t to establish an algorithm communication channel for cooperation. Initially, the population is split into two equal subgroups optimized by BOA and QPSO respectively, with the latter incorporating the Lévy flight for enhanced performance. Subsequently, a hybrid mechanism comprising a weight hybrid mechanism, a elite strategy, and a diversification mechanism is introduced to blend the two algorithms. Experimental evaluation on 12 benchmark test functions and the Muskin model demonstrates that the synergy between BOA and QPSO significantly enhances algorithm performance. The hybrid mechanism further boosts algorithm performance, positioning the new algorithm as a high-performance method. In the Muskingum model experiment, the algorithm proposed in this article can give the best sum of the square of deviation (SSQ) and is superior in the comparison of other indicators. Overall, through benchmark test function experiments and Muskin model evaluations, it is evident that the algorithm proposed in this paper exhibits strong optimization capabilities and is effective in addressing practical problems. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive exploration of a hybrid energy system that integrates wind turbines with photovoltaics (PVs) to address the intermittent nature of electricity production from these sources. The necessity for such technology arises from the sporadic nature of electricity generated by PV cells and wind turbines. The envisioned outcome is an emissions-free, more efficient alternative to traditional energy sources. A variety of optimization techniques are utilized, specifically the Particle Swarm Optimization (PSO) algorithm and Electric Eel Foraging Optimization (EEFO), to achieve optimal power regulation and seamless integration with the public grid, as well as to mitigate anticipated loading issues. The employed mathematical modeling and simulation techniques are used to assess the effectiveness of EEFO in optimizing the operation of grid-connected PV and wind turbine hybrid systems. In this paper, the optimization methods applied to the system's architecture are described in detail, providing a clear understanding of the intricate nature of the approach. The efficacy of these optimization strategies is rigorously evaluated through simulations of diverse operating scenarios using MATLAB/SIMULINK. The results demonstrate that the proposed optimization strategies are not only capable of precisely and swiftly compensating for linked loads, but also effectively controlling the energy supply to maintain the load's power at the desired level. The findings underscore the potential of this hybrid energy system to offer a sustainable and reliable solution for meeting power demands, contributing to the advancement of clean and efficient energy technologies. The results demonstrate the capability of the proposed approach to improve system performance, maximize energy yield, and enhance grid integration, thereby contributing to the advancement of renewable energy technologies and sustainable energy systems. [ABSTRACT FROM AUTHOR]

This research paper outlines a method for automatically classifying wakefulness and deep sleep stage (N3) based on the American Academy of Sleep Medicine (AASM) standards. The study employed a single-channel EEG signal, leveraging the Wigner–Ville Distribution (WVD) for time–frequency analysis to determine EEG energy per second in specific frequency bands (δ , θ , α , and entire band). Particle Swarm Optimization (PSO) was used to optimize thresholds for distinguishing between wakefulness and stage N3. This process aims to mimic a sleep technician's visual scoring but in an automated fashion, with features and thresholds extracted to classify epochs into correct sleep stages. The study's methodology was validated using overnight PSG recordings from 20 subjects, which were evaluated by a technician. The PSG setup followed the 10–20 standard system with varying sampling rates from different hospitals. Two baselines, T1 for the wake stage and T2 for the N3 stage, were calculated using PSO to ascertain the best thresholds, which were then used to classify EEG epochs. The results showed high sensitivity, accuracy, and kappa coefficient, indicating the effectiveness of the classification algorithm. They suggest that the proposed method can reliably determine sleep stages, being aligned closely with the AASM standards and offering an intuitive approach. The paper highlights the strengths of the proposed method over traditional classifiers and expresses the intentions to extend the algorithm to classify all sleep stages in the future. [ABSTRACT FROM AUTHOR]

Along with the further integration of demand management and renewable energy technology, making optimal use of energy storage devices and coordinating operation with other devices are key. The present study takes into account the current situation of power storage equipment. Based on one year of measured data, four cases are designed for a composite energy storage system (ESS). In this paper, a two-tiered optimization model is proposed and is used to optimizing the capacity of power storage devices and the yearly production of the system. Furthermore, this paper performs a comparative analysis of the performance of the four cases from the energy, environmental and economic perspectives. It is concluded that this kind of energy storage equipment can enhance the economics and environment of residential energy systems. The thermal energy storage system (TESS) has the shortest payback period (7.84 years), and the CO2 emissions are the lowest. Coupled with future price volatility and the carbon tax, the electrothermal hybrid energy storage system (HESS) has good development potential. However, the current investment cost is very high, and it will not be possible to recover this cost in 10 years. Finally, it is recommended that the cost of equipment be reduced in combination with subsidies and incentives for further promotion. The research results not only fill a gap in the study area, but also provide some suggestions for further development of industry and research on user-side energy storage. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) play a prominent role in solving problems in various domains such as pattern recognition, image tasks, and natural language processing. In recent years, neural architecture search (NAS), which is the automatic design of neural network architectures as an optimization algorithm, has become a popular method to design CNN architectures against some requirements associated with the network function. However, many NAS algorithms are characterised by a complex search space which can negatively affect the efficiency of the search process. In other words, the representation of the neural network architecture and thus the encoding of the resulting search space plays a fundamental role in the designed CNN performance. In this paper, to make the search process more effective, we propose a novel compact representation of the search space by identifying network blocks as elementary units. The study in this paper focuses on a popular CNN called DenseNet. To perform the NAS, we use an ad-hoc implementation of the particle swarm optimization indicated as PSO-CNN. In addition, to reduce size of the final model, we propose a segmentation method to cut the blocks. We also transfer the final model to different datasets, thus demonstrating that our proposed algorithm has good transferable performance. The proposed PSO-CNN is compared with 11 state-of-the-art algorithms on CIFAR10 and CIFAR100. Numerical results show the competitiveness of our proposed algorithm in the aspect of accuracy and the number of parameters. [ABSTRACT FROM AUTHOR]

In recent years, global attention to carbon emissions has increased, becoming one of the main drivers of global climate change. Accurate prediction of carbon emission trends in small and medium-sized countries and scientific regulation of carbon emissions can provide theoretical support and policy references for the effective and rational use of energy and the promotion of the coordinated development of energy, environment, and economy. This paper establishes a grey prediction model using the classical Logistic mathematical model in a determined environment to investigate the carbon emission system. At the same time, we use the basic principle of fractional-order accumulation to establish a grey prediction model with fractional-order Logistic and obtain the parameter estimation and time-response equation of the new model by solving the model through the theory related to fractional-order operators. The particle swarm optimization algorithm is used to complete the optimization process of the order of the fractional order grey prediction model and obtain the optimal model order. Then, the new model is applied to predict carbon emissions in five medium-emission countries: Ethiopia, Djibouti, Ghana, Belgium, and Austria. The new model shows better advantages in the validity analysis process, and the simulation results indicate that the new model proposed in this paper has stronger stability and better simulation and prediction accuracy than other comparative models, proving the model's validity. Finally, the model is used to forecast the carbon emissions of these five countries for the five years of 2021–2025, and the results are analyzed, and relevant policy recommendations are made. [ABSTRACT FROM AUTHOR]

In this paper, we use tracked vehicles as an example to study complex systems and surrogate model building method. Due to the high cost and long development cycle associated with the traditional development approach, involving static design and experimental verification, there is an urgent need to explore an effective dynamic optimization design method to enhance vibration reduction performance. This paper introduces a methodology for constructing a model of complex equipment surrogate, aimed at streamlining subsequent parameter optimization processes. The dynamics model of the tracked vehicle is utilized, and the root-mean-square value of plumb vertical acceleration at the center of mass is selected as the dynamic performance index. The impact of structure parameters on dynamic performance is analyzed, and a range of design parameters are determined. The optimal Latin hypercube experimental design method is employed to generate sample data. A radial basis function neural network, optimized by particle swarm optimization, is used to construct a dynamic surrogate model for the tracked vehicle. The surrogate model constructed in this paper greatly reduces the time cost in optimizing complex equipment parameters. In the past, parameter optimization required conducting numerous simulations to collect data, with each simulation taking about 700 s. However, the surrogate model introduced in this paper only requires ∼100 s for completion. This significant reduction in time not only saves costs but also maintains a high level of accuracy. [ABSTRACT FROM AUTHOR]

For wireless sensor network (WSN) coverage problems, since the sensing range of sensor nodes is a circular area with symmetry, taking symmetry into account when deploying nodes will help simplify problem solving. In addition, in view of two specific problems of high node deployment costs and insufficient effective coverage in WSNs, this paper proposes a WSN coverage optimization method based on the improved grey wolf optimizer with multi-strategies (IGWO-MS). As far as IGWO-MS is concerned, first of all, it uses Sobol sequences to initialize the population so that the initial values of the population are evenly distributed in the search space, ensuring high ergodicity and diversity. Secondly, it introduces a search space strategy to increase the search range of the population, avoid premature convergence, and improve search accuracy. And then, it combines reverse learning and mirror mapping to expand the population richness. Finally, it adds Levy flight to increase the disturbance and improve the probability of the algorithm jumping out of the local optimum. To verify the performance of IGWO-MS in WSN coverage optimization, this paper rasterizes the coverage area of the WSN into multiple grids of the same size and symmetry with each other, thereby transforming the node coverage problem into a single-objective optimization problem. In the simulation experiment, not only was IGWO-MS selected, but four other algorithms were also selected for comparison, namely particle swarm optimization (PSO), grey wolf optimizer (GWO), grey wolf optimization based on drunk walk (DGWO), and grey wolf optimization led by two-headed wolves (GWO-THW). The experimental results demonstrate that when the number of nodes for WSN coverage optimization is 20 and 30, the optimal coverage rate and average coverage rate using IGWO-MS are both improved compared to the other four comparison algorithms. To make this clear, in the case of 20 nodes, the optimal coverage rate of IGWO-MS is increased by 13.19%, 1.68%, 4.92%, and 3.62%, respectively, compared with PSO, GWO, DGWO, and GWO-THW; while IGWO-MS performs even better in terms of average coverage rate, which is 16.45%, 3.13%, 11.25%, and 6.19% higher than that of PSO, GWO, DGWO, and GWO-THW, respectively. Similarly, in the case of 30 nodes, compared with PSO, GWO, DGWO, and GWO-THW, the optimal coverage rate of the IGWO-MS is increased by 15.23%, 1.36%, 5.55%, and 3.66%; the average coverage rate is increased by 16.78%, 1.56%, 10.91%, and 8.55%. Therefore, it can be concluded that IGWO-MS has certain advantages in solving WSN coverage problems, which is reflected in that not only can it effectively improve the coverage quality of network nodes, but it also has good stability. [ABSTRACT FROM AUTHOR]

In this paper, a novel Multi-Objective Hypergraph Particle Swarm Optimization (MOHGPSO) algorithm for structural health monitoring (SHM) systems is considered. This algorithm autonomously identifies the most relevant sensor placements in a combined fitness function without artificial intervention. The approach utilizes six established Optimal Sensor Placement (OSP) methods to generate a Pareto front, which is systematically analyzed and archived through Grey Relational Analysis (GRA) and Fuzzy Decision Making (FDM). This comprehensive analysis demonstrates the proposed approach's superior performance in determining sensor placements, showcasing its adaptability to structural changes, enhancement of durability, and effective management of the life cycle of structures. Overall, this paper makes a significant contribution to engineering by leveraging advancements in sensor and information technologies to ensure essential infrastructure safety through SHM systems. [ABSTRACT FROM AUTHOR]

In the era of continuous development in Internet of Things (IoT) technology, smart services are penetrating various facets of societal life, leading to a growing demand for interconnected devices. Many contemporary devices are no longer mere data producers but also consumers of data. As a result, massive amounts of data are transmitted to the cloud, but the latency generated in edge-to-cloud communication is unacceptable for many tasks. In response to this, this paper introduces a novel contribution—a layered computing network built on the principles of fog computing, accompanied by a newly devised algorithm designed to optimize user tasks and allocate computing resources within rechargeable networks. The proposed algorithm, a synergy of Lyapunov-based, dynamic Long Short-Term Memory (LSTM) networks, and Particle Swarm Optimization (PSO), allows for predictive task allocation. The fog servers dynamically train LSTM networks to effectively forecast the data features of user tasks, facilitating proper unload decisions based on task priorities. In response to the challenge of slower hardware upgrades in edge devices compared to user demands, the algorithm optimizes the utilization of low-power devices and addresses performance limitations. Additionally, this paper considers the unique characteristics of rechargeable networks, where computing nodes acquire energy through charging. Utilizing Lyapunov functions for dynamic resource control enables nodes with abundant resources to maximize their potential, significantly reducing energy consumption and enhancing overall performance. The simulation results demonstrate that our algorithm surpasses traditional methods in terms of energy efficiency and resource allocation optimization. Despite the limitations of prediction accuracy in Fog Servers (FS), the proposed results significantly promote overall performance. The proposed approach improves the efficiency and the user experience of Internet of Things systems in terms of latency and energy consumption. [ABSTRACT FROM AUTHOR]

To address the nonlinearities and external disturbances in unstructured and complex agricultural environments, this paper investigates an autonomous trajectory tracking control method for agricultural ground vehicles. Firstly, this paper presents the design and implementation of a lightweight, modular two-wheeled differential drive vehicle equipped with two drive wheels and two caster wheels. The vehicle comprises drive wheel modules, passive wheel modules, battery modules, a vehicle frame, a sensor system, and a control system. Secondly, a novel robust trajectory tracking method was proposed, utilizing an improved pure pursuit algorithm. Additionally, an Online Particle Swarm Optimization Continuously Tuned PID (OPSO-CTPID) controller was introduced to dynamically search for optimal control gains for the PID controller. Simulation results demonstrate the superiority of the improved pure pursuit algorithm and the OPSO-CTPID control strategy. To validate the performance, the vehicle was integrated with a seeding and fertilizing machine to realize autonomous wheat seeding in an agricultural environment. Experimental outcomes reveal that the vehicle of this study completed a seeding operation exceeding 1 km in distance. The proposed method can robustly and smoothly track the desired trajectory with an accuracy of less than 10 cm for the root mean square error (RMSE) of the curve and straight lines, given a suitable set of parameters, meeting the requirements of agricultural applications. The findings of this study hold significant reference value for subsequent research on trajectory tracking algorithms for ground-based agricultural robots. [ABSTRACT FROM AUTHOR]

This article discusses a special issue of the International Conference on Computing and Communications Networks (ICCCN 2022), which focuses on various topics in computing and networking. The special issue includes 12 selected papers that cover a range of subjects, such as code-mixed language identification, sentiment analysis, sleep quality assessment, gait analysis, cervical precancerous lesion classification, marine radar monitoring, text summarization, software optimization, fault diagnosis, imbalanced data analysis, neonatal hypoglycemia brain injury, and feature selection for classification problems. The authors present their research methodologies, experimental results, and conclusions for each paper. The guest editors hope that the research contributions in this special issue will be beneficial to readers. [Extracted from the article]

A prerequisite to ensure the stability of the power supply system is suitable functioning of transmission line equipment. However, the increasing deployment of transmission lines in modern power systems has introduced significant challenges to line inspection. While deep learning-based image detection techniques have shown promise in improving the efficiency and accuracy of insulator detection, they often require substantial computational resources and energy. This limitation hinders the consistent guarantee of accuracy and real-time performance on resource-constrained drones. To address this issue, this paper investigates the co-optimization problem of energy consumption and analytic accuracy in insulator image detection on unmanned aerial vehicles (UAVs). We propose a latency-aware end-edge cooperative insulator detection task offloading scheme with high energy efficiency and accuracy that aims to achieve optimal performance. Initially, we conducted an experimental analysis to examine the influence of input image resolution on the accuracy and latency of the CNN-based insulation detection model. Subsequently, we develop a model that takes into account the latency, analytic accuracy and energy consumption for image detection task offloading. Finally, we formalized a nonlinear integer optimization problem and designed a particle swarm optimization (PSO)-based task offloading scheme to optimize task accuracy and energy consumption while adhering to latency constraints. Extensive experiments validated the effectiveness of the proposed end-edge cooperative insulator detection method in optimizing accuracy and energy consumption. [ABSTRACT FROM AUTHOR]

This paper introduces a novel meta-heuristic algorithm named Rhinopithecus Swarm Optimization (RSO) to address optimization problems, particularly those involving high dimensions. The proposed algorithm is inspired by the social behaviors of different groups within the rhinopithecus swarm. RSO categorizes the swarm into mature, adolescent, and infancy individuals. Due to this division of labor, each category of individuals employs unique search methods, including vertical migration, concerted search, and mimicry. To evaluate the effectiveness of RSO, we conducted experiments using the CEC2017 test set and three constrained engineering problems. Each function in the test set was independently executed 36 times. Additionally, we used the Wilcoxon signed-rank test and the Friedman test to analyze the performance of RSO compared to eight well-known optimization algorithms: Dung Beetle Optimizer (DBO), Beluga Whale Optimization (BWO), Salp Swarm Algorithm (SSA), African Vultures Optimization Algorithm (AVOA), Whale Optimization Algorithm (WOA), Atomic Retrospective Learning Bare Bone Particle Swarm Optimization (ARBBPSO), Artificial Gorilla Troops Optimizer (GTO), and Harris Hawks Optimization (HHO). The results indicate that RSO exhibited outstanding performance on the CEC2017 test set for both 30 and 100 dimension. Moreover, RSO ranked first in both dimensions, surpassing the mean rank of the second-ranked algorithms by 7.69% and 42.85%, respectively. Across the three classical engineering design problems, RSO consistently achieves the best results. Overall, it can be concluded that RSO is particularly effective for solving high-dimensional optimization problems. [ABSTRACT FROM AUTHOR]

The human–computer interaction attribute of the interactive genetic algorithm (IGA) allows users to participate in the product design process for which the product needs to be evaluated, and requiring a large number of evaluations would lead to user fatigue. To address this issue, this paper utilizes an XGBoost proxy model modified by particle swarm optimization and the graphical interaction mechanism (GIM) to construct an improved interactive genetic algorithm (PXG-IGA), and then the PXG-IGA is applied to 3D vase design. Firstly, the 3D vase shape has been designed by using a bicubic Bézier surface, and the individual genetic code is binary and includes three parts: the vase control points, the vase height, and the texture picture. Secondly, the XGBoost evaluation of the proxy model has been constructed by collecting user online evaluation data, and the particle swarm optimization algorithm has been used to optimize the hyperparameters of XGBoost. Finally, the GIM has been introduced after several generations, allowing users to change product styles independently to better meet users' expectations. Based on the PXG-IGA, an online 3D vase design platform has been developed and compared to the traditional IGA, KD tree, random forest, and standard XGBoost proxy models. Compared with the traditional IGA, the number of evaluations has been reduced by 58.3% and the evaluation time has been reduced by 46.4%. Compared with other proxy models, the accuracy of predictions has been improved up from 1.3% to 20.2%. To a certain extent, the PXG-IGA reduces users' operation fatigue and provides new ideas for improving user experience and product design efficiency. [ABSTRACT FROM AUTHOR]

Uncertainty analysis deals with the fluctuations and unpredictability of the electrical power generated from renewable resources (RRs), such as solar PV and wind energy systems. This paper gives an insight into various techniques used for the uncertainty analysis and a probabilistic Monte Carlo Simulation is applied for modelling the uncertainties concerned with RRs and electric vehicle (EV) load in the MATLAB platform. The uncertainty associated with the price sensitivity of EV charging and the state of charge of EVs is taken as a prime factor for analysis in the present work. Despite the fluctuations and unpredictability of electricity generation and consumption, the considered system ensures that the total amount of electricity supplied by solar PV, wind and grid matches the total amount of electricity demanded by EV load. Rao‐1, Rao‐2 and Rao‐3 algorithms are applied in this work to optimize the operation cost of charging stations under uncertain conditions and without any uncertainties. The results obtained without uncertainties by Rao algorithms are compared with the existing particle swarm optimisation method. In the presence of uncertainties, Rao‐1 and Rao‐2 algorithms are compared with Rao‐3 and it is found that the Rao‐3 algorithm performed better. [ABSTRACT FROM AUTHOR]

In this paper, we report a graphene-based plasmonic photodetector optimized using the particle swarm optimization (PSO) algorithm and compatible with complementary metal–oxide–semiconductor (CMOS) technology. The proposed photodetector structure is designed to minimize fabrication challenges and reduce production costs compared to more complex alternatives. Graphene has been used for its unique properties in the detection region, titanium nitride (TiN) as a CMOS-compatible metal, and both to aid in plasmonic excitation. Photodetectors have key parameters influenced by multiple independent variables. However, practical constraints prevent thorough adjustment of all variables to achieve optimal parameter values, often resulting in analysis based on several simplified models. Here we optimize these variables by presenting a new approach in the field of photodetectors using the capabilities of the PSO algorithm. As a result, for the proposed device at the wavelength of 1550 nm, the voltage responsivity is 210.6215 V/W, the current responsivity is 3.7213 A/W, the ultra-compressed length is less than 3 μ m , and the specific detectivity is 2.566× 10 7 Jones were obtained. Furthermore, the device in question works under the photothermoelectric effect (PTE) at zero bias and has zero dark current, which ultimately resulted in a very low noise equivalent power (NEP) of 4.5361 pW / Hz . [ABSTRACT FROM AUTHOR]

This paper proposes an innovative approach to improve the performance of grid-connected photovoltaic (PV) systems operating in environments with variable atmospheric conditions. The dynamic nature of atmospheric parameters poses challenges for traditional control methods, leading to reduced PV system efficiency and reliability. To address this issue, we introduce a novel integration of fuzzy logic and sliding mode control methodologies. Fuzzy logic enables the PV system to effectively handle imprecise and uncertain atmospheric data, allowing for decision-making based on qualitative inputs and expert knowledge. Sliding mode control, known for its robustness against disturbances and uncertainties, ensures stability and responsiveness under varying atmospheric conditions. Through the integration of these methodologies, our proposed approach offers a comprehensive solution to the complexities posed by real-world atmospheric dynamics. We anticipate applications in grid-connected PV systems across various geographical locations and climates. By harnessing the synergistic benefits of fuzzy logic and sliding mode control, this approach promises to significantly enhance the performance and reliability of grid-connected PV systems in the presence of variable atmospheric conditions. On the grid side, both PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) algorithms were employed to tune the current controller of the PI (Proportional-Integral) current controller (inverter control). Simulation results, conducted using MATLAB Simulink, demonstrate the effectiveness of the proposed hybrid MPPT technique in optimizing the performance of the PV system. The technique exhibits superior tracking efficiency, achieving a convergence time of 0.06 s and an efficiency of 99.86%, and less oscillation than the classical methods. The comparison with other MPPT techniques highlights the advantages of the proposed approach, including higher tracking efficiency and faster response times. The simulation outcomes are analyzed and demonstrate the effectiveness of the proposed control strategies on both sides (the PV array and the grid side). Both PSO and GA offer effective methods for tuning the parameters of a PI current controller. According to considered IEEE standards for low-voltage networks, the total current harmonic distortion values (THD) obtained are considerably high (8.33% and 10.63%, using the PSO and GA algorithms, respectively). Comparative analyses with traditional MPPT methods demonstrate the superior performance of the hybrid approach in terms of tracking efficiency, stability, and rapid response to dynamic changes. [ABSTRACT FROM AUTHOR]

Detecting cardiac abnormalities promptly is critical for preventing unexpected and premature fatalities. In this research, four types of cardiac arrhythmias such as Ventricular Tachycardia, Premature Ventricular Contraction, Normal Sinus Rhythm and Supraventricular Tachycardia are detected from the amassed Physiobank MIT-BIH cardiac arrhythmia database. Dimensionality reduction techniques like Stochastic Neighbour Embedding (SNE), Neighbourhood Preserving Embedding, Linear Local Tangent Space Alignment and Gaussian Process Latent Variable Model are used to reduce the dimension of the ECG signals. The appropriate features of dimensionally reduced ECG signals are selected by the Elephant Search Optimization (ESO) technique. Finally, classification is performed using the relevant classifiers, such as Support Vector Machine, Adaboost, Modest Adaboost based on Ridge Regression (Modest Adaboost.RR), Extreme Gradient Boost (XGboost) and Naïve Bayes (NBC) classifiers. Multiple classifiers without and with ESO feature selection for different cardiac cases have an average classification accuracy of 62.23% and 73.61%, respectively. These multiple classifiers are defined by a set of control parameters known as hyper-parameters, which must be tuned in order to achieve optimal results. Experts have developed many approaches for detecting cardiac arrhythmias, but these multiple classifiers do not always perform well when the usual parameters for machine learning classification models are employed. In this paper, various classifiers are used in conjunction with the Stochastic Gradient Descent (SGD), Particle Swarm Optimization (PSO) and Bayesian Tree-structured Parzen Estimator (BTPE) to enhance the cardiac arrhythmia classification accuracy via hyper-parameter tuning. Multiple classifiers with SGD, PSO and BTPE hyper-parameters tuning techniques for various cardiac cases have an average classification accuracy of 80.13%, 90.67% and 94.96%, respectively. The Classifier's performance is analysed based on metrics like Classification Accuracy, F1 score, Error Rate, Matthew's correlation coefficient, Jaccard Index and Cohen's Kappa Coefficient with and without ESO features selection method and hyper-parameters tuning techniques. The analysis utilizes the MATLAB R2014a software for result evaluation. The results show that the SNE-ESO approach, along with the XGboost-BTPE, achieved the highest classification accuracy of 99.89% for detecting {Ventricular Tachycardia}-{Normal Sinus Rhythm} cases. In terms of classification benchmarks, the results exhibit that the BTPE hyper-parameter tuning technique surpasses the SGD and PSO techniques. [ABSTRACT FROM AUTHOR]

Ensuring a secure online environment hinges on the timely detection of network attacks. Nevertheless, existing detection methods often grapple with the delicate balance between speed and accuracy. In this paper, we introduce a novel intrusion detection algorithm that marries quantum particle swarm optimization with an extreme learning machine (QPSO-ELM). Firstly, we present a feature selection algorithm grounded in partitioned gains to distill vital features from data samples, thereby diminishing feature count to amplify both model training speed and accuracy. Subsequently, we unveil an intrusion detection scheme underpinned by QPSO-ELM, capable of achieving exceptional levels of training and detection speed, all while maintaining high accuracy. Finally, we fine-tune the trained model using the proposed hidden layer node selection algorithm, reducing the detection model size without compromising detection accuracy, thus further elevating its speed. The experiment results indicate that compared to the current baseline, our proposed intrusion detection scheme achieves the best results in terms of accuracy, precision, recall, and detection latency. Furthermore, the ablation experiment results demonstrate the effectiveness of our proposed method in improving both detection speed and detection accuracy. [ABSTRACT FROM AUTHOR]