Improved Particle Swarm Optimization Research Articles

Personalizing Students' Learning Needs by a Teaching Decision Optimization Method

With the rapid development of educational technology and the deepening of educational system reform, personalized education has gradually become an important topic in education. However, existing classroom teaching decision-making methods often fail to meet students’ personalized learning needs, resulting in some students being unable to reach their full potential in the classroom. To solve this problem, this study proposed a multi-conditional factor classroom teaching decision optimization method based on the improved particle swarm optimization (IPSO) algorithm, and predicted students’ personalized learning needs by combining with the improved ant colony optimization-support vector regression (IACO-SVR) model. First, the IACO-SVR model was used to collect students’ learning data, such as grades, interests, hobbies and learning progress, to accurately predict their needs in different teaching contexts. Second, the IPSO algorithm was used to optimize the multi-conditional factor classroom teaching decisions, thus meeting the personalized needs of students. The IPSO algorithm had strong global search ability, which effectively found the optimal solution to achieve personalized teaching strategies. It is expected that the teaching quality can be improved by predicting the personalized learning needs of students and optimizing classroom teaching decisions in this study, thus providing better support for their comprehensive development. In addition, the results of this study can provide theoretical basis and reference for administrative departments of education and schools to formulate personalized education policies.

Community Detection in Modular Complex Networks Using an Improved Particle Swarm Optimization Algorithm

Community detection is an important and interesting topic for better understanding and analyzing complex network structures. Detecting hidden partitions in complex networks is proven to be an NP-hard problem that may not be accurately resolved using traditional methods. So it is solved using evolutionary computation methods and modeled in the literature as an optimization problem. In recent years, many researchers have directed their research efforts toward addressing the problem of community structure detection by developing different algorithms and making use of single-objective optimization methods. In this study, we have continued that research line by improving the Particle Swarm Optimization (PSO) algorithm using a local improvement operator to effectively discover community structure in the modular complex networks when employing the modularity density metric as a single-objective function. The framework of the proposed algorithm consists of three main steps: an initialization strategy, a movement strategy based on perturbation genetic operators, and an improvement operator. The key idea behind the improvement operator is to determine and reassign the complex network nodes that are located in the wrong communities if the majority of their topological links do not belong to their current communities, making it appear that these nodes belong to another community. The performance of the proposed algorithm has been tested and evaluated when applied to publicly-available modular complex networks generated using a flexible and simple benchmark generator. The experimental results showed the effectiveness of the suggested method in discovering community structure over modular networks of different complexities and sizes.

Calculation and optimization of inter-regional available transfer capability considering wind power uncertainty and system improvement measures

The intermittency and uncertainty of wind energy make transmission congestion management increasingly complex. In this paper, we investigate the impact of wind energy on available transmission capacity (ATC) and then explore ways to mitigate transmission congestion from the grid and load side, respectively. The impact of a stochastic variable is considered by applying Latin Hypercube Sampling (LHS) and backward curtailment techniques to generate typical scenarios. ATC is improved by the optimal allocation of Thyristor Controlled Series Compensation (TCSC) on the grid side and the use of Demand Response (DR) on the load side. The source-grid-load cooperative optimisation model for ATC is solved by an improved particle swarm optimisation (PSO) algorithm. Based on the IEEE-30 bus system, an experimental scheme is designed and analytical calculations are performed. The results show that the joint application of TCSC and DR can help to improve ATC in a comprehensive way. The work done in this paper can achieve the purpose of promoting renewable energy consumption by improving the utilisation efficiency of the transmission network without changing the network structure.

Optimization of NC CAD Machining Parameters Based on Improved Particle Swarm Optimization Algorithm

Optimization of NC CAD Machining Parameters Based on Improved Particle Swarm Optimization Algorithm

Optimization of Power-System Parameters and Energy-Management Strategy Research on Hybrid Heavy-Duty Trucks

Hybrid heavy-duty trucks have attracted wide attention due to their excellent fuel economy and high mileage. For power-split hybrid heavy-duty trucks, the optimization of powertrain parameters is closely related to the control strategies of hybrid vehicles. In particular, the parameters of the powertrain system will directly affect the control of the vehicles’ power performance and economy. However, currently, research on hybrid heavy-duty trucks employing power-split configurations is lacking. Furthermore, few studies consider both the optimization of powertrain parameters and the control strategy at the same time to carry out comprehensive optimization research. In order to address these issues, this paper focuses on the fuel economy of hybrid heavy-duty trucks with power-split configurations. Improved particle swarm optimization (IPSO) and dynamic programming (DP) algorithms are introduced to optimize powertrain parameters. With these methods being applied, hybrid heavy-duty trucks show a 2.15% improvement in fuel consumption compared to that of the previous optimization. Moreover, based on the optimal powertrain parameters, a DP-based rule-control strategy (DP-RCS) and optimal DP-RCS scheme are presented and used in this paper to conduct our research. Simulation results show that the optimal DP-RCS reduces fuel consumption per hundred kilometers by 11.35% compared to the rule-based control strategy (RCS), demonstrating that the combination of powertrain parameter optimization and DP-RCS effectively improves the fuel economy of hybrid heavy-duty trucks.

The low latency networking method for task-driven MEC-enabled UAV swarm

Unmanned Aerial Vehicles (UAVs) are increasingly being used for special missions as Mobile Edge Computing (MEC) devices, and the utilization of multiple UAVs who are integrated into formations for collaboratively executing tasks that cannot be performed by single aircraft has also been a recent research hit. In this paper, we present a low latency networking method aims to shorten the preparation time so that MEC-enabled swarm can as quick as possible concentrate on the task execution. For reducing the time of swarm initialization, we consider applying the Leader-Follower (L-F) topology model in networking and formation control. Firstly, by combining the nature of wireless communication and correlation analysis, we improve the traditional L-F model to avoid too many nodes accessing the same node and raise the communication efficiency. Secondly, in order to choose the topology that can provide better performance in network, we adopt an improved Particle Swarm Optimization (PSO) algorithm to customize the appropriate factor coefficients during leader selection for swarms with different densities and communication initial conditions. Experiments and evaluations demonstrate that the proposed algorithm has significant effect in reducing network latency.

An active piezoelectric damping vibration control system for the sting used in wind tunnel

In wind tunnel tests, the cantilever sting supporting system often suffers from low-frequency and large-amplitude resonance due to its inherent low structural damping characteristic, resulting in the degradation of data quality and structural safety. To improve wind tunnel testing safety and data accuracy, this paper is dedicated to establish an active vibration control system using piezoelectric stack actuators. A novel methodology of vibration monitoring based on modal transformation, which uses measured strain and a Strain-to-Displacement Transformation (SDT) matrix to reconstruct dynamic displacement field, is proposed herein. Meanwhile, strain sensor positions are optimized by an improved Particle Swarm Optimization (PSO) algorithm to reduce systematic estimation errors of this method. Furthermore, a Back-Propagated Neutral Network (BPNN) is established to implement a self-adaptive control strategy. A series of verification tests are performed to demonstrate the validity of the proposed system. Experimental results indicate that the relative Root Mean Square Error (RMSE) between estimated vibration displacement and measured vibration displacement is less than 3%, and a vibration attenuation of over 14 dB/Hz is achieved in ground tests, proving the superiority of this intelligent active vibration suppression system.

Analysis of sustainable groundwater management policies for urban development

AbstractGroundwater is essential to human well‐being and sustains a variety of aquatic ecosystems in both urban and rural settings. Yet, it is commonly overlooked in these contexts. As a result, groundwater may be very important to the development and well‐being of many nations if it is properly evaluated and utilized. Although the possibility for sustainable groundwater resource development is still debated in the literature, these issues still lack quantitative understanding. In order to assess groundwater potential in two different urbanized regions of “Northern Kerala, Southern India,” this research uses the “Enhanced Fuzzy Analytic Hierarchy Process (E‐Fuzzy AHP) based Multi‐Criteria Decision Making (MCDM)‐Geospatial Framework employing Improved Particle Swarm Optimization (IPSO)”. The study area is a coastal region that is quickly becoming urbanized and relies on groundwater resources to meet its basic needs. By incorporating both spatial and non‐spatial data into the Geographic Information System (GIS) platform, a groundwater potential zone map with an accurate integer value was created, known as the “groundwater potential index (GWPI)”. The very excellent potential zone occupies 35% and 11%, respectively, of the “urban and peri‐urban zones,” according to the final groundwater potential map, which employed the quantile technique to split the research territory into four zones. The statistical effectiveness and dependability of results in the overall decision‐making phase were examined in the validation study using the data on mean water depth level, and it was found that the reliability was well within the tolerance threshold.

Task‐oriented resource prediction and adjustment algorithm for space‐based information network

SummaryThe tasks of a space‐based information network are complex and diverse, but the resources of a space‐based environment are minimal. The existing methods are challenging to match task demand to resource supply accurately. Aiming at the problem of accurate prediction from task to resource, we propose a resource prediction adjustment strategy. First, we propose a multidimensional resource prediction algorithm based on improved particle swarm optimization and back propagation (IPSO‐BP) neural network. The improved PSO is used to optimize the weight and threshold of BP neural network to make up for the defects that BP neural network is easy to fall into local minimum and the predicted output value is not unique. Second, to meet the quality of service (QoS) of tasks, we propose a density‐based performance evaluation algorithm (DPEA) to adjust resources. This method uses the idea of local sensitive hash to select the evaluation subset for the configuration task, then dynamically selects the nearest neighbors of the configuration task, and uses the idea of weighted average to evaluate the QoS performance index of the configuration task. Simulation results show that the proposed resource prediction and adjustment strategy effectively reduces the scheduling time overhead and improves resource utilization.

Exploration the route of information integration for vehicle design: A knowledge-enhanced energy management strategy

With the development of the technologies for Vehicle-to-Everything (V2X), driving information sharing leads to a potential solution for intelligent vehicles to enhance their controlling effect, particularly for energy management of the vehicles equipped with the complex powertrain. Transplanting the optimal and adaptive knowledge of the vehicle driving data into a target vehicle can enhance both the adaptability and economic optimization of energy management. In this paper, a V2X-based knowledge-enhanced energy management strategy (KE-EMS) is proposed to transplant offline optimal-oriented prognostic knowledge and adaptive-oriented heuristic knowledge into online driving in practice. Firstly, in the offline processing, based on micro trip segments collected from volunteered sharing vehicles by V2X, optimal-oriented prognostic knowledge and adaptive-oriented heuristic knowledge are generated by dynamic programming (DP) and improved particle swarm optimization algorithm (iPSO) respectively. In the online implementation, the KE-EMS invokes the generated knowledge accordingly by a driving segment matching method (DSM) estimating the similarity between the current driving condition and micro driving segments. Finally, an improved equivalent consumption minimization strategy (ECMS) embedded into KE-EMS further adjusts the electric charging tendency to accomplish the energy-saving potential release. A simulation evaluation and hardware-in-the-loop (HIL) test manifest that the proposed KE-EMS can improve economic performance and guarantee real-time performance for the studied plug-in hybrid electric vehicles (PHEVs).

An innovative talent training mechanism for maker education in colleges and universities based on the IPSO-BP-enabled technique

In this study, a path to improving students’ core literacy is explored, and a new mechanism is developed for maker education and teaching based on research on students’ core literacy and the essence of maker education. An evaluation model of college students’ maker ability is established, and an improved particle swarm optimisation (IPSO) algorithm is introduced into the backpropagation (BP) neural network to improve the accuracy and speed of the evaluation of students’ innovation ability. Finally, experimental verification is conducted. The results indicate that most students significantly improved their memory and understanding of knowledge, principle exploration and attitude formation after practising the core literacy training method. For an innovation ability evaluation dataset, the accuracy rate of the BP neural network model reached 76.42%. The prediction accuracy rate of the PSO-BP network designed above was 86.76%. The IPSO-BP neural network model had the highest accuracy rate, reaching 4.43%. Evidently, the combination of a talent training mechanism for maker education and information technology can improve the evaluation efficiency of students’ abilities.

Fault Diagnosis of Power Electronic Circuits Based on Improved Particle Swarm Optimization Algorithm Neural Network

Fault Diagnosis of Power Electronic Circuits Based on Improved Particle Swarm Optimization Algorithm Neural Network

Design and Optimization of a Novel Electronic Mechanical Brake Actuator Based on Cam

The electronic mechanical brake (EMB) is considered an ideal actuator for brake-by-wire systems. We applied the negative radius roller cam mechanism as the clamping mechanism of the EMB, solving the problem of large size, poor load-bearing capacity, and the inefficiency of the existing EMBs. When designing a cam as a clamping transmission mechanism, it is necessary to take the pressure angle, contact stress, motion law, etc., as goals and constraints. Existing design methods cannot easily solve this problem. Therefore, we propose a new analysis method from the cam profile and combine it with an improved particle swarm optimization (PSO) algorithm to design the cam profiles. This method can handle various complex goals and constraints of the EMB and obtain the required negative radius roller cam profile. Finally, the logical consistency of the profile-based analysis method was verified, and the EMB design objectives and accuracy were compared using ADAMS. Under the same conditions, the result showed that the optimized cam mechanism requires only 40.52% motor power and only 65.65% clearance elimination time compared to the EMB with the lead screw mechanism.

RESEARCH ON GAIT RECOGNITION OF SURFACE EMG SIGNAL BASED ON MPSO-LSTM ALGORITHM

The growing interest in gait recognition based on surface electromyography (sEMG) signals is attributed to their capability to anticipate motion characteristics during human movement. This paper focuses on gait pattern recognition using sEMG signals. Initially, the muscles responsible for collecting sEMG signals are determined based on the distinct characteristics of human gait, and data for 12 different gait patterns are collected. Subsequently, the acquired sEMG signals undergo preprocessing and feature extraction stages. Moreover, various algorithms relevant to gait classification based on surface myoelectric signals are investigated. In this study, we propose an improved particle swarm optimization algorithm (MPSO-LSTM) for accurately classifying gait patterns using surface myoelectric signals. Experimental results demonstrate the effectiveness of the MPSO-LSTM algorithm in gait recognition based on sEMG signals.

End-Cloud Collaboration Navigation Planning Method for Unmanned Aerial Vehicles Used in Small Areas.

Unmanned aerial vehicle (UAV) collaboration has become the main means of indoor and outdoor regional search, railway patrol, and other tasks, and navigation planning is one of the key, albeit difficult, technologies. The purpose of UAV navigation planning is to plan reasonable trajectories for UAVs to avoid obstacles and reach the task area. Essentially, it is a complex optimization problem that requires the use of navigation planning algorithms to search for path-point solutions that meet the requirements under the guide of objective functions and constraints. At present, there are autonomous navigation modes of UAVs relying on airborne sensors and navigation control modes of UAVs relying on ground control stations (GCSs). However, due to the limitation of airborne processor computing power, and background command and control communication delay, a navigation planning method that takes into account accuracy and timeliness is needed. First, the navigation planning architecture of UAVs of end-cloud collaboration was designed. Then, the background cloud navigation planning algorithm of UAVs was designed based on the improved particle swarm optimization (PSO). Next, the navigation control algorithm of the UAV terminals was designed based on the multi-objective hybrid swarm intelligent optimization algorithm. Finally, the computer simulation and actual indoor-environment flight test based on small rotor UAVs were designed and conducted. The results showed that the proposed method is correct and feasible, and can improve the effectiveness and efficiency of navigation planning of UAVs.

An improved particle swarm optimization with particle refactor operator for perishable food delivery problems by electric vehicles

ABSTRACT The increasing demand for perishable food and the popularity of electric vehicles have promoted the integration research of perishable food delivery services and electric vehicles. Aiming at minimizing the total delivery cost, a new model is formulated for perishable food delivery problems by electric vehicles (PFDP-EV), which considers vehicle capacity constraints, travel time constraints, time window constraints, and so on. An improved particle swarm optimization with particle refactor operator (IPSO-PRO) is developed to solve the proposed model. For the IPSO-PRO, a particle refactor operator is designed to help reconstruct the unqualified particles, and an elite selection strategy and an adaptive weighted strategy are used to improve the performance. Then, extensive efforts are conducted to verify the proposed method. First, the parameters of IPSO-PRO are tuned based on the Taguchi method. Second, small-scale, medium-scale, and large-scale perishable food delivery instances (19 instances) are simulated to evaluate the performance, and the results show that IPSO-PRO achieves the best average gap of 0%. Finally, based on a simulation case, the result and sensitivity analysis are conducted to reveal insightful management insights, which provides decision support for perishable food delivery problems.

Joint Task Allocation and Resource Optimization Based on an Integrated Radar and Communication Multi-UAV System

This paper investigates the joint task allocation and resource optimization problem in an integrated radar and communication multi-UAV (IRCU) system. Specifically, we assign reconnaissance UAVs and communication UAVs to perform the detection, tracking and communication tasks under the resource, priority and timing constraints by optimizing task allocation, power as well as channel bandwidth. Due to complex coupling among task allocation and resource optimization, the considered problem is proved to be non-convex. To solve the considered problem, we present a loop iterative optimization (LIO) algorithm to obtain the optimal solution. In fact, the mentioned problem is decomposed into three sub-problems, such as task allocation, power optimization and channel bandwidth optimization. At the same time, these three problems are solved by the divide-and-conquer algorithm, the successive convex approximation (SCA) algorithm and the improved particle swarm optimization (PSO) algorithm, respectively. Finally, numerical simulations demonstrate that the proposed LIO algorithm consumes fewer iterations or achieves higher maximum joint performance than other baseline schemes for solving the considered problem.

A Novel Sequence-to-Sequence Prediction Model for Lithium-Ion Battery Capacity Degradation Based on Improved Particle Swarm Optimization

The state of health (SOH) evaluation and remaining useful life (RUL) prediction for lithium-ion batteries (LIBs) are crucial for health management. This paper proposes a novel sequence-to-sequence (Seq2Seq) prediction method for LIB capacity degradation based on the gated recurrent unit (GRU) neural network with the attention mechanism. An improved particle swarm optimization (IPSO) algorithm is developed for automatic hyperparameter search of the Seq2Seq model, which speeds up parameter convergence and avoids getting stuck in local optima. Before model training, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm decomposes the capacity degradation sequences. And the intrinsic mode function (IMF) components with the highest correlation are employed to reconstruct the sequences, reducing the influence of noise in the original data. A real-cycle-life data set under fixed operating conditions is employed to validate the superiority and effectiveness of the method. The comparison results demonstrate that the proposed model outperforms traditional GRU and RNN models. The predicted mean absolute percent error (MAPE) in SOH evaluation and RUL prediction can be as low as 0.76% and 0.24%, respectively.

Optimal Energy Consumption Path Planning for Unmanned Aerial Vehicles Based on Improved Particle Swarm Optimization

In order to enhance the energy efficiency of unmanned aerial vehicles (UAVs) during flight operations in mountainous terrain, this research paper proposes an improved particle swarm optimization (PSO) algorithm-based optimal energy path planning method, which effectively reduces the non-essential energy consumption of UAV during the flight operations through a reasonable path planning method. First, this research designs a 3D path planning method based on the PSO optimization algorithm with the goal of achieving optimal energy consumption during UAV flight operations. Then, to overcome the limitations of the classical PSO algorithm, such as poor global search capability and susceptibility to local optimality, a parameter adaptive method based on deep deterministic policy gradient (DDPG) is introduced. This parameter adaptive method dynamically adjusts the main parameters of the PSO algorithm by monitoring the state of the particle swarm solution set. Finally, the improved PSO algorithm based on parameter adaptive improvement is applied to path planning in mountainous terrain environments, and an optimal energy-consuming path-planning algorithm for UAVs based on the improved PSO algorithm is proposed. Simulation results show that the path-planning algorithm proposed in this research effectively reduces non-essential energy consumption during UAV flight operations, especially in more complex terrain scenarios.

Overlapping peak separation algorithm for ion mobility spectra based on multistrategy JAYA.

In the field of separation science, ion mobility spectrometry (IMS) plays an important role as an analytical tool. However, the lack of sufficient structural resolution is a common problem in qualitative and quantitative analysis using IMS. A method is needed to solve the problem of overlapping peaks caused by insufficient resolution. The method uses multiple strategies to more effectively use population information to balance exploration and exploitation capabilities, prevent local optimization, accurately resolve overlapping peaks, quickly obtain optimal spectral peak model coefficients, and accurately identify compounds. Multistrategy JAYA algorithm's (MSJAYA) performance is compared with improved particle swarm optimization (IPSO), dynamic inertia weight particle swarm optimization (DIWPSO), and multiobjective dynamic teaching-learning-based optimization (MDTLBO). The analysis shows that MSJAYA's maximum separation error is within 0.6%, a level of accuracy not guaranteed by the other algorithms. In addition, the separation error fluctuates within a much smaller range, demonstrating MSJAYA's superior robustness. Compared with other overlapping peak separation algorithms, MSJAYA is more applicable because no special parameters are used. The method allows fast deconvolution analysis of strong overlapping peaks with multiple components, which greatly improves the resolution of IMS.