Improved Particle Swarm Optimization Algorithm Research Articles

Reactive Power Optimization of Distributed PVConnected to Three-Phase Unbalanced Distribution Network

This study presents an approach to optimize reactive power in three-phase unbalanced distribution networks using an enhanced particle swarm optimization algorithm. The study analyzes the characteristics of reactive power optimization and establishes a reactive power optimization model for the distribution network using a multi-objective optimization method. Inequality constraints are utilized to determine the reactive power optimization model constraints for three-phase unbalanced distribution networks. The study applies an improved particle swarm optimization algorithm to solve the reactive power model and achieve optimization. The experimental results demonstrate that the proposed method considerably reduces the unused power of the optimized power network within 0-500ms and achieves a maximum reactive power optimization time of fewer than 8.2s, thereby enhancing the efficiency of reactive power optimization in distribution networks.

Design of Engine Cooling System Using Improved Particle Swarm Optimization Algorithm

Design of Engine Cooling System Using Improved Particle Swarm Optimization Algorithm

Multi-source monitoring data helps revealing and quantifying the excavation-induced deterioration of rock mass

There exist many high-steep rock slopes in Southwest China due to the construction of large-scale hydropower projects. The study on excavation-induced damage of high rock slopes is vital for disaster prevention and mitigation. Due to the complexity of high-steep rock slopes, assessing the excavation-induced deterioration of rock mass is often challenging. This study used the space-borne Interferometric synthetic aperture radar (InSAR) and multipoint displacement meters to monitor the slope displacements before and after the excavation. Based on the multi-source monitoring data and the parameter estimation method, we evaluated the changes in rock mass properties due to slope excavation. We employed an improved particle swarm optimization algorithm with dynamic topology and adaptive parameter adjustment and a neural network-based surrogate model in the parameter estimation. The degradation of rock mass strength and stiffness coincides with the increased sensitivity of slope deformation to rainfall infiltration. An in-depth analysis indicates that the absence of vegetation protection and excavation-induced deterioration of rock masses are responsible for the noticeable slope deformation during the rainy season. As slope excavation profoundly influences slope stability and deformation, we should pay closer attention to the excavation way and support style. This study also demonstrates the benefits of using multi-source monitoring data in slope deformation analysis. The findings can aid in comprehending the deformation evolution and instability mechanisms of rock slopes excavated at the hydropower sites, thereby contributing to the prevention and mitigation of landslide disasters.

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.

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

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).

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

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.

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.

An Improved Particle Swarm Optimization Algorithm for the Urban Transit Routing Problem

The Urban Transit Routing Problem (UTRP) is a challenging discrete problem that revolves around designing efficient routes for public transport systems. It falls under the category of NP-hard problems, characterized by its complexity and numerous constraints. Evaluating potential route sets for feasibility is a demanding and time-consuming task, often resulting in the rejection of many solutions. Given its difficulty, metaheuristic methods, such as swarm intelligence algorithms, are considered highly suitable for addressing the UTRP. However, the effectiveness of these methods depends heavily on appropriately adapting them to discrete problems, as well as employing suitable initialization procedures and solution-evaluation methods. In this study, a new variant of the particle swarm optimization algorithm is proposed as an efficient solution approach for the UTRP. We present an improved initialization function and improved modification operators, along with a post-optimization routine to further improve solutions. The algorithm’s performance is then compared to the state of the art using Mandl’s widely recognized benchmark, a standard for evaluating UTRP solutions. By comparing the generated solutions to published results from 10 studies on Mandl’s benchmark network, we demonstrate that the developed algorithm outperforms existing techniques, providing superior outcomes.

3D Aided Art Design Method Based on Improved Particle Swarm Optimization Algorithm

3D Aided Art Design Method Based on Improved Particle Swarm Optimization Algorithm

Design of Uniform Field Coils Based on the Ferromagnetic Coupling Effect Inside Single-Ended Open Magnetic Shielding Cylinder

This research proposes a novel method for designing uniform field coils inside a single-ended open magnetic shielding cylinder (MSC). It solves the problems of distorted magnetic field distribution and decreased uniformity resulting from the coupling between the coils and the single-ended open MSC. The magnetic field analytical model of the uniform field coils inside the single-ended open MSC with high-permeability materials is first established. Then, minimizing the sum of relative errors of the magnetic field in the target area is taken as the objective function, and the improved particle swarm optimization algorithm is adopted to optimize the structural parameters for ensuring high uniformity of the coils. The results of the simulation indicate that comparing this work with the Lee-Whiting coil in the single-ended open MSC, the maximum relative error of the magnetic field is reduced from 6 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> to 2.3 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> and 6.1 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> . The experimental results also demonstrate the efficacy of the proposed method, and practicality for real-time compensation of magnetic field fluctuations within single-ended open MSC. The research content provides the basis for improving the performance of the magnetocardiography device.

Path Planning for Mount Robot Based on Improved Particle Swarm Optimization Algorithm

To address the problem of cooperative work among right-angle coordinate robots in spacecraft structural plate mount tasks, an improved particle swarm optimization (PSO) algorithm was proposed to assign paths to three robots in a surface-mounted technology (SMT) machine. First, the optimization objective of path planning was established by analyzing the working process of the SMT machine. Then, the inertia weight update strategy was designed to overcome the early convergence of the traditional PSO algorithm, and the learning factor of each particle was calculated using fuzzy control to improve the global search capability. To deal with the concentration phenomenon of particles in the iterative process, the genetic algorithm (GA) was introduced when the particles were similar. The particles were divided into elite, high-quality, or low-quality particles according to their performance. New particles were generated through selection and crossover operations to maintain the particle diversity. The performance of the proposed algorithm was verified with the simulation results, which could shorten the planning path and quicken the convergence compared to the traditional PSO or GA. For large and complex maps, the proposed algorithm shortens the path by 7.49% and 11.49% compared to traditional PSO algorithms, and by 3.98% and 4.02% compared to GA.

Strategies employed in the design and optimization of pump as turbine runner

The low hydraulic efficiency of the pump as turbine (PAT) poses a major challenge to the runner design. This paper introduces a new strategy for designing and optimizing the performance of PAT runner. The strategy process is mainly divided into four steps, namely blade parametric design, significance evaluation of parameters, construction of optimizing surrogate model and optimization of improved PSO algorithm. Compared with the current design methodologies, not only does this optimization strategy propose the system design and optimization process, but also it breaks down the process into well-defined steps and simplifies them. The hydraulic efficiency of PAT optimized by this optimization strategy reaches 84.76%, which improves 3.93%. A test bench has been built to test the optimizing strategy. The maximum difference of experimental data is 1.5%, which meets the requirements of practical engineering application, and verifies the reliability of the optimization strategy process. The optimizing strategy process can be extended to apply to pump, fan, conventional turbine and other blade fluid models, and it can be further extended to apply to the optimization design of 3D blades with a high specific speed.

Assembly Sequence Intelligent Planning based on Improved Particle Swarm Optimization Algorithm

Assembly Sequence Intelligent Planning based on Improved Particle Swarm Optimization Algorithm

Inverse Optimization Method for Safety Resource Allocation and Inferring Cost Coefficient Based on a Benchmark

Due to cost-push inflation, the trade-off between safety costs and risk prevention (safety) has become difficult worldwide. Most companies experience the difficulty of safety cost overruns and allocate safety resource inefficiently. In this paper, a forward model maximizing safety input is formulated. Because there is a wide range of variation of safety resource cost coefficient parameters, it is hard to determine safety resource cost coefficients in the forward model, to make the decisions on which types of safety resources are allocated to which potentially risky locations with what prices, and to ensure total input is as close to the benchmark as possible. Taking allocation, themes, resources, and cost coefficient parameters as new decision variables, the inverse optimization model is formulated based on a bi-level model. With consideration of quaternion decision, bi-level programming, and NP-hard problem, based on the comparison of exact penalty algorithm and an improved PSO algorithm, in which the inertia weight is adaptively changing with the number of iterations, the PSO is suitable for solving the specific inverse model. Numerical experiments demonstrated the effectiveness of the PSO algorithm, proving that it can allocate the right amount and types of safety resources with the right prices at the right places.

An IPSO-assisted DNN operation scheme in UAV-mounted MEC networks

With the growth of the Internet in recent years, applications have higher and higher requirements on the computation capacity of User Equipments (UEs). Offloading compute-intensive tasks to edge servers becomes a promising solution. Therefore, Unmanned Aerial Vehicles (UAVs)-mounted Mobile Edge Computing (MEC) emerges as the times require, with the ability to provide task offloading for ground UEs while providing short-term network services for emergencies. However, due to the changing environment, how to provide fast decision solutions remains a hot topic of research. In this paper, we propose an Improved Particle Swarm Optimization (IPSO)-assisted Deep Neural Network (DNN) operation scheme, which combines a heuristic algorithm with neural networks to provide an efficient operation scheme. We consider the need to shorten UE latency and reduce UE energy consumption while ensuring fairness in offloading, which is constructed as a Mixed Integer NonLinear Programming (MINLP) problem. We solve it by the IPSO algorithm and provide high-quality labeled data for training the neural network. The runtime leverages the trained neural network to make fast decisions. Finally, simulation experiments show that our scheme has some superiority in adapting quickly to changing environments.

Prediction Model of Greenhouse Tomato Yield Using Data Based on Different Soil Fertility Conditions

Tomato yield prediction plays an important role in agricultural production planning and management, market supply and demand balance, and agricultural risk management. To solve the problems of low accuracy and high uncertainty of tomato yield prediction methods in solar greenhouses, based on experimental data for water and fertilizer consumption by greenhouse tomatoes in different regions over many years, this paper investigated the prediction models of greenhouse tomato yields under three different soil fertility conditions (low, medium, and high). Under these three different soil fertility conditions, greenhouse tomato yields were predicted using the neural network prediction model (NN), the neural network prediction model based on particle swarm optimization (PSO–NN), the neural network prediction model based on an adaptive inertia weight particle swarm optimization algorithm (AIWPSO–NN), and the neural network prediction model based on the improved particle swarm optimization algorithm (IPSO–NN). The experimental results demonstrate that the evaluation indexes (mean square error, mean absolute error, and R2) of the IPSO–NN prediction model proposed in this paper were superior to the other three prediction models (i.e., NN prediction model, AIWPSO–NN prediction model, and IPSO–NN prediction model) under three different soil fertility conditions. Among them, compared with the NN prediction model, the MSE of the other three prediction models under high soil fertility decreased to 0.0082, 0.0041, and 0.0036; MAE decreased to 0.0759, 0.0511, and 0.0489; R2 decreased to 0.8641, 0.9323, and 0.9408. These results indicated that the IPSO–NN prediction model had a higher predictive ability for greenhouse tomato yields under three different soil fertility conditions. In view of the important role of tomato yield prediction in greenhouses, this technology may be beneficial to agricultural management and decision support.

Safety BIM intelligent modeling analysis of prestressed concrete continuous girder bridge construction scene

BIM intelligent modeling is used to analyze the safety of the construction scene of prestressed concrete continuous girder bridge, so as to realize the safety management of the bridge construction scene and improve the construction efficiency. The construction safety analysis system is used to analyze the binding data of construction safety, and the data is combined with the self-applicable equilibrium control and the game equilibrium control to build the construction scene safety objective function model. On this basis, combined with the control constraints of the whole life cycle, the statistical analysis regression model is used to build the scene safety analysis model based on BIM. The whole life cycle safety intelligent analysis of the construction scene is realized, and the improved particle swarm optimization algorithm is used to solve the model by adaptive differential evolution, so as to shorten the calculation time of the model. The experimental results show that the safety management performance of the proposed method is high, and the safety management evaluation grade is 285. The identification accuracy of main beam stress change is high. Under the conditions of unbalanced load, combination of unbalanced load and prestress, combination of wind load and prestress and unbalanced load, the safety analysis of upper edge stress and lower edge stress of main beam can be effectively completed, and the construction safety of prestressed concrete continuous beam bridge can be realized.