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The inverter module, as a critical device in arc plasma power supplies for converting DC to AC, often encounters issues such as stability errors and system disturbances due to the impact of shock loads when tracking rated voltage and frequency signals. This paper proposes an improved Particle Swarm Optimization (PSO) strategy for Fractional-Order PID (FOPID) control with limited parameter selection, aiming to address the poor performance of inverter modules in arc plasma power supply systems. The proposed strategy incorporates the spiral position updating mode from the Whale Optimization Algorithm into the position updating process of PSO, which improves particle diversity, global search capability and optimization stability. The effectiveness of the proposed method is verified by comparing it with the Ziegler–Nichols tuning method and the standard PSO algorithm in the MATLAB/Simulink simulation environment. A model of an inverter system is established for validation purposes. The results indicate that applying the improved PSO-based FOPID control strategy to the inverter process of the arc plasma power supply system significantly enhances the output waveform quality, reduces harmonic distortion, and achieves excellent control performance. [ABSTRACT FROM AUTHOR]

Traffic engineering is critical for optimizing network performance and resource utilization. Software-Defined Networking (SDN) offers a flexible architecture that allows dynamic traffic management. However, traditional traffic engineering methods can struggle as SDN networks grow in size and complexity, making congestion management a key challenge. This paper presents a novel traffic engineering solution for SDN that utilizes Particle Swarm Optimization (PSO) within a scalable hierarchical framework. The approach addresses the difficulties of dynamic traffic patterns and resource allocation in large-scale SDN deployments by breaking down traffic management tasks into manageable domains. By leveraging PSO's ability to explore large solution spaces, the algorithm optimizes routing paths and bandwidth allocation based on a fitness function designed to minimize congestion, latency, and other key performance metrics. The proposed method, called Particle Swarm Optimization for Congestion Management (PSOCB), focuses on optimizing cluster switches using meta-heuristic techniques. PSOCB achieves a 13% increase in throughput and an 8% improvement in link performance, while reducing delays compared to traditional methods. Although PSOCB occasionally showed higher congestion than baseline approaches, it quickly restored normal network conditions through its congestion removal strategies. Simulations on realistic network topologies demonstrate substantial performance improvements over conventional static routing, highlighting the effectiveness and scalability of our PSO-based hierarchical traffic engineering solution. [ABSTRACT FROM AUTHOR]

Traditional clustering algorithms such as K-means need to input the number of clusters before the start of the algorithm. Affinity propagation (AP) clustering algorithm solves this problem by considering each data point as a prospective cluster head (exemplar) and finding a set of appropriate exemplars by message passing. However, the AP clustering algorithm requires two parameters: preference and damping factor. Providing the parameters in advance poses the same issue faced in the traditional clustering algorithm. Moreover, all data points are not equally relevant for becoming cluster heads. To overcome these problems, we propose two parameter-free particle swarm optimization-based algorithms, PSO-APver1 and PSO-APver2. Furthermore, we introduce a novel version of mutant PSO where two cluster validity indices are used to judge the quality of the clustering solution. In PSO-APver2, we consider the internal data distribution using the square wave function to determine the initial preference value of data points. We conducted experiments on 8 real-world datasets to show the efficacy of our proposed algorithms over classic algorithms and two AP-based algorithms. We conducted the Friedman test followed by post hoc analysis to exhibit the significance of our work. [ABSTRACT FROM AUTHOR]

The motion response of offshore floating wind turbines significantly influences their structural integrity, power generation efficiency, operational complexity, safety, and stability. Therefore, predicting the motion response of offshore floating wind turbines is of paramount importance. In engineering practice, especially in extreme marine environments, the motion of wind turbines becomes more complex, making accurate prediction more challenging. In this era of rapid development in deep learning technology, some solutions have emerged for this problem. In this paper, we propose a hybrid model, namely the OVMD-FE-PSO-LSTM model. We begin by conducting numerical simulations of a 5 MW-OC4 semi-submersible floating wind turbine in extreme sea conditions, obtaining motion data for the turbine's six degrees of freedom. We then decompose the initial motion data using an optimized traditional VMD method, assess the modal complexity with the FE method, combine modal components with similar complexity to reduce computational load, and make predictions using the PSO-LSTM model. Finally, we analyze and compare the predictive results of different models. The results demonstrate that the proposed hybrid model outperforms other comparative models in terms of accuracy, providing new insights into the prediction of the motion response of offshore floating wind turbines. [ABSTRACT FROM AUTHOR]

In this investigation, an optimized heat treatment following rolling was applied to an A390/6Ti₂AlC composite, and its influence on the microstructure and properties was studied. These composites, known for their enhanced mechanical properties, are used in critical applications like face plate assemblies and brake rotors. Analysis using field emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDX) and X-ray diffraction (XRD) techniques indicated a uniform distribution of particulates. Notably, composites with a 6% Ti₂AlC weight fraction exhibited a substantial improvement, with a 16% increase in tensile strength and a 4% increase in yield strength compared to the base alloy. To further enhance the material's mechanical properties for superior durability and performance, an optimized regimen of rolling followed by heat treatment was applied, refining the microstructure and improving the composite material's balance of strength and flexibility for specific applications. [ABSTRACT FROM AUTHOR]

The cable-driven redundant space manipulator (CDRSM) has a wide range of potential applications in on-orbit servicing due to its light weight, low inertia, and intrinsic compliance. Especially for capturing and detumbling large-momentum tumbling targets in space, it requires agile approaching and synchronizing, compliant contact and dexterous post-capture manipulation. Due to the limited energy resources of the space robot, we propose a coordinated configuration and end-effector (EE) trajectory optimization method to minimize base disturbance, as well as maximize EE stiffness for guaranteeing the accuracy of post-capture manipulation. The dynamics coupling between the free-floating base and the CDRSM is derived, along with the kinematic modeling and stiffness analysis of the cable-driven joints. Considering the equiangular constraint relationship and the kinematic coupling characteristics of the cable-driven joints, the configuration of the CDRSM and the EE trajectory are parametrically represented using the arm angle and the rational Bézier curves, respectively. Further, based on these parameter variables, the particle swarm optimization and multiple-objective particle swarm optimization algorithms are used to minimize the base disturbance and maximize EE stiffness during post-capture manipulation. Finally, several simulation results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

With the wide application of Residence Time Difference (RTD) fluxgate sensors in Unmanned Aerial Vehicle (UAV) aeromagnetic measurements, the requirements for their measurement accuracy are increasing. The core characteristics of the RTD fluxgate sensor limit its sensitivity; the high-permeability soft magnetic core is especially easily interfered with by the input noise. In this paper, based on the study of the excitation signal and input noise characteristics, the stochastic resonance is proposed to be realized by adding feedback by taking advantage of the high hysteresis loop rectangular ratio, low coercivity and bistability characteristics of the soft magnetic material core. Simulink is used to construct the sensor model of odd polynomial feedback control, and the Particle Swarm Optimization (PSO) algorithm is used to optimize the coefficients of the feedback function so that the sensor reaches a resonance state, thus reducing the noise interference and improving the sensitivity of the sensor. The simulation results show that optimizing the odd polynomial feedback coefficients with PSO enables the sensor to reach a resonance state, improving sensitivity by at least 23.5%, effectively enhancing sensor performance and laying a foundation for advancements in UAV aeromagnetic measurement technology. [ABSTRACT FROM AUTHOR]

This article presents an innovative asymmetric multilevel inverter (MLI) topology that outperforms conventional counterparts. The introduced topology presents a breakthrough in implementing power electronics control by maximizing specific levels while minimizing switching components. A cutting-edge control scheme for optimal operation of the cascaded half-bridge MLI is presented. The ant lion optimization (ALO) algorithm was implemented to optimize the switching control to reduce the total harmonic distortion (THD) and improve power quality. For verification, the performance and effectiveness of the ALO technique are assessed by comparing its results to those obtained using the simplified sinusoidal pulse width modulation (SSPWM) technique, genetic algorithm (GA), and particle swarm optimization (PSO) in existing literature. Simulation results verified the efficacy of ALO in finding the optimal parameters. The suggested method showcases a remarkable reduction in the THD compared to SSPWM. The quality of the resulting waveform was enhanced, and both filter size and cost were significantly reduced. To meet stringent IEEE standards, an LC filter has been designed with minimal size and proper requirements. Experimental results validation of the suggested scheme, using a dSPACE R&D controller board unequivocally, confirmed its robustness and effectiveness. This groundbreaking study not only introduces a superior asymmetric MLI topology but also validates its exceptional performance through comprehensive analysis and experimentation. The experimental waveforms showed good matching with the simulation outcomes. The findings hold immense promise for advancing the field of power system control and revolutionizing the designing and implementation of efficient and cost-effective inverter systems. [ABSTRACT FROM AUTHOR]

This study employs four metaheuristic optimization methods to optimize the dimensional synthesis of Ackermann steering mechanisms for three-axle, six-wheeled vehicles with front-axle steering mode and reverse-phase steering mode. The employed optimization methods include Particle Swarm Optimization (PSO), Hybrid Particle Swarm Optimization (HPSO), Differential Evolution with golden ratio (DE-gr), and Linearly Ensemble of Parameters and Mutation Strategies in Differential Evolution (L-EPSDE). With a front-wheel steering angle range of 70 degrees, two hundred optimization experiments were conducted for each method, and statistical analyses revealed that DE-gr and L-EPSDE methods outperformed PSO and HPSO methods in terms of standard deviation, mean value, and minimum error. These two methods exhibited superior convergence stability, faster convergence, and higher accuracy compared to PSO and HPSO. Reverse-phase (K = 1) steering mode outperformed front-axle steering mode, delivering reduced steering errors and turning radii. Considering the transmission ratio of front to rear axle (K) as a design variable in reverse-phase steering mode increased design flexibility and significantly lowered steering errors for the front and rear axle steering mechanisms. However, this comes with a slight increase in the turning radius of the vehicle's front part compared to when K = 1. The optimized mechanism, designed using the DE-gr method, was validated through kinematic simulations and steering analyses using MSC-ADAMS v2015 software, further confirming the effectiveness and reliability of the proposed design. [ABSTRACT FROM AUTHOR]

For civil engineering structures, structural damage usually occurs at limited positions in the preliminary stage of the structural failure. Compared with the numerous elements of the entire structure, the damaged elements are sparsely distributed in space. Based on this important prior information, this paper proposed to utilize a sparse Bayesian learning method to identify the damage to structures while considering the measurement noise and modeling error. The particle Swarm Optimization (PSO) algorithm was first introduced to address the associated computational efficiency issue, and the optimization performances of PSO and Sequential Quadratic Programming (SQP) algorithm in the process of model updating were compared, positive outcomes revealed that the PSO algorithm has the stronger searching ability and better robustness. To investigate the effectiveness and practicality of the sparse Bayesian learning with a PSO algorithm in structural damage detection, an asymmetrical frame in different scenarios (e.g. with single and multiple damages) was constructed in the laboratory. The encouraging results of the experimental case studies compellingly demonstrate that the presented methodology not only can detect the location and extent of structural damage with high precision and efficiency, but also can proficiently assess the posterior uncertainties associated with the damage detection results. [ABSTRACT FROM AUTHOR]

In this study, a hybrid particle swarm optimization (HPSO) approach is presented for enhancing the performance of a three-phase, 12-slot, 19-pole yoke-less axial-field flux-switching permanent magnet (YASA-AFFSPM) motor. The aim of the optimization process is to achieve a motor that demonstrates high efficiency and stable operation. To facilitate this, a finite element analysis model is developed, and a multi-objective optimization function utilizing weight coefficients is created. The variables in this study include the split ratio, stator axial length, sandwiching pole angle, rotor pole angle, permanent magnet arc, and the number of conductors per slot. The optimization objectives encompass efficiency, power factor, cogging torque, and average torque, evaluated under both no-load and load conditions. Findings indicate that the hybrid particle swarm optimization method excels in its search capabilities and convergence speed, resulting in motor designs that align closely with the intended specifications. Finite element simulations further validate the proposed methodology's effectiveness and practicality. Finally, experimental results are presented to confirm the reliability of the suggested algorithm. [ABSTRACT FROM AUTHOR]

In this study, a linked simulation optimization (SO) model is presented for identification of groundwater contaminant sources. The SO model consists of two steps namely, simulation and optimization. The simulation step entails developing a groundwater contaminant transport model in which the advection–dispersion-reaction equation (ADRE) is solved for predicting the concentration of the contaminant. The system parameters (hydraulic conductivity, dispersivity, etc.) and control variables (pumping, recharge, etc.,) are given as model inputs. A meshless technique called the meshless Local Radial Point Interpolation Method (LRPIM) is employed to solve the contaminant transport equation. The simulation model is linked with three different swarm intelligence-based optimization models namely, teaching–learning based optimization (TLBO), grey wolf optimization (GWO) and particle swarm optimization (PSO) to form three SO models namely LRPIM-PSO, LRPIM-GWO and LRPIM-TLBO. The SO model minimizes the difference between the predicted and observed concentrations to determine the unknown source locations and release histories. The applicability of the developed SO models for source identification (SI) is demonstrated with a hypothetical and real aquifer problems to identify the groundwater contaminant sources. All the 3 models are able to locate the sources and release histories satisfactorily. However, the LRPIM-TLBO has been found to be more accurate followed by LRPIM-PSO and LRPIM-GWO. [ABSTRACT FROM AUTHOR]

This paper addresses the Capacitated Location Tree Problem with Interconnections, a new combinatorial optimization problem with applications in network design. In this problem, the required facilities picked from a set of potential facilities must be opened to serve customers using a tree-shaped network. Costs and capacities are associated with the opening of facilities and the establishment of network links. Customers have a given demand that must be satisfied while respecting the facilities and link capacities. The problem aims to minimize the total cost of designing a distribution network while considering facility opening costs, demand satisfaction, capacity constraints, and the creation of interconnections to enhance network resilience. A valid mixed-integer programming was proposed and an exact solution method based on the formulation was used to solve small- and medium-sized instances. To solve larger instances two metaheuristic approaches were used. A specific decoder procedure for the metaheuristic solution approaches was also proposed and used to help find solutions, especially for large instances. Computational experiments and results using the three solution approaches are also presented. Finally, a case study on the design of electrical transportation systems was presented and solved. [ABSTRACT FROM AUTHOR]