Adaptive Particle Swarm Algorithm Research Articles

Research on Path Planning Method of Solid Backfilling and Pushing Mechanism Based on Adaptive Genetic Particle Swarm Optimization

This paper investigates the path planning problem of the coal mine solid-filling and pushing mechanism and proposes a hybrid improved adaptive genetic particle swarm algorithm (AGAPSO). To enhance the efficiency and accuracy of path planning, the algorithm combines a particle swarm optimization algorithm (PSO) and a genetic algorithm (GA), introducing the sharing mechanism and local search capability of the particle swarm optimization algorithm. The path planning of the pushing mechanism for the solid-filling scenario is optimized by dynamically adjusting the algorithm parameters to accommodate different search environments. Subsequently, the proposed algorithm’s effectiveness in the filling equipment path planning problem is experimentally verified using a simulation model of the established filling equipment path planning scenario. The experimental findings indicate that the improved hybrid algorithm converges three times faster than the original algorithm. Furthermore, it demonstrates approximately 92% and 94% better stability and average performance, respectively, than the original algorithm. Additionally, AGAPSO achieves a 27.59% and 19.16% improvement in path length and material usage optimization compared to the GA and GAPSO algorithms, showcasing superior efficiency and adaptability. Therefore, the AGAPSO method offers significant advantages in the path planning of the coal mine solid-filling and pushing mechanism, which is crucial for enhancing the filling effect and efficiency.

Application of Adaptive Algorithms in Smart Building Design Optimisation

Abstract How to realize the unity of safety, comfort, and economy of building structures has been a hot spot of concern in the field of construction engineering. This paper searches for optimal particles using a hybrid optimization strategy and optimizes the weights of each index in the fitness function into the same interval. Then dynamic inertia weights are used to improve the performance of the algorithm, and an enhanced adaptive particle swarm algorithm is obtained. After selecting the optimization variables for the building design, the objective function and constraints are designed, and the improved particle swarm algorithm is used to solve the optimal design of the intelligent building. The total weight of the target building structure was reduced after optimization, and 47.04% of the building materials were saved. The outer diameter of the steel pipe concrete at the lowest level of the building increases from 1.73m to 2.06m after optimization, which fulfills the law of column change in building design. It has also been found that the wind resistance of the optimized building structure has improved. This paper provides a reliable basis for the application of adaptive algorithms in building design optimization, and the proposed method also provides an effective reference for the field of construction engineering.

Cooperative Path Planning of Ground-air Robots for Distributed Photovoltaic Inspection

Facing the Distributed PV generation inspection scenario, to overcome the low efficiency of traditional manual inspection and the insufficient endurance of existing UAV inspection, UAV aerial inspection and UGVs to provide energy supply in the road network are adopted, and a path planning method that prioritizes the meeting points is proposed. Firstly, the improved genetic algorithm is used to plan the inspection path of each UAV, then the UAV path is clustered to determine the optimal number of meeting points, then the adaptive particle swarm algorithm is used to find the best location of the meeting points in the road network, and finally, the patrol path with the meeting points is optimized twice. Through the simulation test of the standard data set and real data, the effectiveness of the proposed method in solving the path planning problem of distributed photovoltaic coordinated inspection by ground-to-air robots is verified.

Modeling and Optimization of Mechanical Cutting of Downhole Tubing

Summary Mechanical cutting of tubing plays a vital role in solving the problem of pipe string jams in workover operations of oil wells. To improve the efficiency of downhole cutting operations and save operation costs, it is necessary to optimize the parameters of downhole-cutting operations. However, previous research did not involve related engineering problems. Therefore, in this paper, the equivalent simulation experiment of downhole cutting is conducted based on actual field data. Cutting speed, feed rate, and cutting thickness are used as parameters while cutting power (P), material removal rate (MRR), and tool chip temperature (T) are used as optimization objectives with the trade-offs between the three objectives considered. The full factorial design is used to carry out the experiments and the combination of grey relational analysis (GRA) method and entropy weight method is used to determine the weight of the three objectives. The influence of cutting parameters on the optimization objectives is analyzed, the mathematical model between cutting parameters and a single objective is established, and the adaptive weight particle swarm algorithm is used to optimize the coefficients of this model. The relationship between the multiobjective model and cutting parameters is established using a multiple nonlinear regression model, and the selection of interaction terms is completed using a stepwise regression method. The reliability of the model is also verified. This paper provides a reference for future research on downhole-cutting problems.

A Data-Driven Rutting Depth Short-Time Prediction Model with Metaheuristic Optimization for Asphalt Pavements Based on RIOHTrack

Rutting of asphalt pavements is a crucial design criterion in various pavement design guides. A good road transportation base can provide security for the transportation of oil and gas in road transportation. This study attempts to develop a robust artificial intelligence model to estimate different asphalt pavements' rutting depth clips, temperature, and load axes as primary characteristics. The experiment data were obtained from 19 asphalt pavements with different crude oil sources on a 2.038 km long full-scale field accelerated pavement test track (RIOHTrack, Road Track Institute) in Tongzhou, Beijing. In addition, this paper also proposes to build complex networks with different pavement rutting depths through complex network methods and the Louvain algorithm for community detection. The most critical structural elements can be selected from different asphalt pavement rutting data, and similar structural elements can be found. An extreme learning machine algorithm with residual correction (RELM) is designed and optimized using an independent adaptive particle swarm algorithm. The experimental results of the proposed method are compared with several classical machine learning algorithms, with predictions of Average Root Mean Squared Error, Average Mean Absolute Error, and Average Mean Absolute Percentage Error for 19 asphalt pavements reaching 1.742, 1.363, and 1.94\% respectively. The experiments demonstrate that the RELM algorithm has an advantage over classical machine learning methods in dealing with non-linear problems in road engineering. Notably, the method ensures the adaptation of the simulated environment to different levels of abstraction through the cognitive analysis of the production environment parameters.

The influence of social network on innovation and entrepreneurship of economic and management college students based on adaptive particle swarm algorithm

Abstract Exploring the influence of social networks on innovation and entrepreneurship of economics and management college students to help them better enhance their innovation consciousness to face the future social workplace. In this paper, the particle swarm algorithm is optimized according to the strategy and parameter adaptive mechanism, and the SPS-PSO algorithm is used to initialize and update the population. The social network and innovation and entrepreneurship ability of university students in economics and management are introduced, and feature selection based on strategy and parameter adaptive particle swarm algorithm is utilized. The index mining of social network and innovation and entrepreneurship ability of college students is conducted, and the data analysis of innovation and entrepreneurship ability of college students in economics and management is carried out with the example of the University of S. From the social network, the percentages of those who agree with the validity of social network centrality, heterogeneity, dynamics and linkage strength are 71.56, 71.69%, 72.55% and 71.16%, respectively. From the perspective of innovation and entrepreneurship, the overall percentage of college students in economics and management who agreed was 69.27%. This indicates that the higher the integrity of the social network structure of economics and management college students, the more it can enhance innovation and entrepreneurship abilities and promote the cultivation of observation and judgment ability and divergent thinking abilities of economics and management college students.

Autonomous motion and control of lower limb exoskeleton rehabilitation robot.

Introduction: The lower limb exoskeleton rehabilitation robot should perform gait planning based on the patient's motor intention and training status and provide multimodal and robust control schemes in the control strategy to enhance patient participation. Methods: This paper proposes an adaptive particle swarm optimization admittance control algorithm (APSOAC), which adaptively optimizes the weights and learning factors of the PSO algorithm to avoid the problem of particle swarm falling into local optimal points. The proposed improved adaptive particle swarm algorithm adjusts the stiffness and damping parameters of the admittance control online to reduce the interaction force between the patient and the robot and adaptively plans the patient's desired gait profile. In addition, this study proposes a dual RBF neural network adaptive sliding mode controller (DRNNASMC) to track the gait profile, compensate for frictional forces and external perturbations generated in the human-robot interaction using the RBF network, calculate the required moments for each joint motor based on the lower limb exoskeleton dynamics model, and perform stability analysis based on the Lyapunov theory. Results and discussion: Finally, the efficiency of the APSOAC and DRNNASMC algorithms is demonstrated by active and passive walking experiments with three healthy subjects, respectively.

An improved method of state of health prediction for lithium batteries considering different temperature

Due to the inaccurate extraction of battery aging characteristics, low accuracy of available capacity, and a large amount of training data, a data-driven algorithm is used to predict the functional capacity of Li-ion batteries. We propose a method to predict the health status of Li-ion batteries under different temperature conditions using health factor (HF) and weighted particle swarm optimization Gaussian process regression (WPSO-GPR) models. The health factor (HF) is extracted adaptively in the voltage and time profiles during the discharge stages for the HF extraction problem in different temperature predictions. A Gaussian process regression (GPR) model for SOH prediction is created for the capacity regeneration problem, with rational quadratic coefficients as the kernel function. An adaptive weight particle swarm algorithm is used to optimize the GPR model. The proposed framework for designing single-cell experiments on the cell data set is validated using two evaluation metrics: root mean square error (RMSE) and means absolute percentage error (MAPE). The results demonstrate that the proposed method has a short sample size, high prediction accuracy, and broad applicability.

Optimization Model and Solution Algorithm for Rural Customized Bus Route Operation under Multiple Constraints

In order to improve the operational efficiency of public transportation systems in rural areas, we investigated the demand-responsive rural customized bus vehicle route optimization problem. First, a two-stage planning model describing the problem in the reservation phase and real-time phase was constructed with the objectives of minimizing the operating cost of the operator and the travel time cost of the passenger, and the passenger time window, vehicle characteristics, rated passenger capacity and the running time of the route were considered in the constraints. Second, a hybrid algorithm solution model combining bat algorithm and adaptive particle swarm algorithm was designed to obtain a more optimal solution. Finally, the effectiveness of the hybrid algorithm on the optimization model was verified by using the actual road network in some townships of Jing’an County, Jiangxi Province, China, and the obtained objective function value was reduced by 5.5%. The results show that the optimization model and hybrid algorithm designed in this paper can be used to provide theoretical references for opening demand-responsive customized bus route operation schemes in rural areas.

Scale-up prediction of supercritical CO2 circulating fluidized bed boiler based on adaptive PSO-SVM

Supercritical CO2 (S-CO2) circulating fluidized bed (CFB) boiler has broad prospects in the field of coal-fired power generation because of its high combustion efficiency, compact structure, and low pollution emission. Scale-up regularity research based on combustion characteristics of S-CO2 CFB boiler is the key to make it a wide range of industrial applications. In order to simplify the complicated workload and save the huge time cost of numerical simulations, it is of great significance to accurately make the scale-up prediction according to the operating performance of the S-CO2 CFB boiler. This study proposed a scale-up prediction model corresponding to the combustion characteristics of S-CO2 CFB boiler based on adaptive particle swarm optimization support vector machine (APSO-SVM). The parameters of the particle swarm algorithm were processed adaptively firstly combined with the boiler characteristics, and then the adaptive particle swarm algorithm was integrated with the support vector machine to solve the precocious convergence problem of particles being easy to gather in a certain position in the process of pattern recognition. The novel method effectively predicts the boiler in the scaling process from the aspect of boiler capacity, optimizes the scale-up regularity expression by numerical simulations, greatly saves time cost and applicability of enlarge design by altering complex numerical simulations, and lays the application foundation of S-CO2 CFB boiler in the industrial field with acceptable operation accuracy.

Automatic feature extraction for online chatter monitoring under variable milling conditions

Chatter is an unpredictable self-excited vibration phenomenon in the milling process, which can seriously affect machining efficiency and quality. In the aerospace industry, the cutting process lasts for an extended period, and the cutting parameters continuously change. This paper presents an automated method for monitoring chatter in this field. Recurrence plot (RP) can accurately reflect dynamic changes in the cutting system, but its hyperparameters must be set in advance. This paper initially proposes a novel adaptive particle swarm algorithm (APSO) for calculating hyperparameters so that RP can be obtained automatically. Then, as the global and local features of RP show clear changes in different cutting states, a deep neural network architecture that can extract features from multiple scales is developed. Three categories of experiments are conducted to test the proposed method. Experimental results show that the proposed method can achieve accurate online chatter monitoring under different cutting conditions.

Day-Ahead Operation Analysis of Wind and Solar Power Generation Coupled with Hydrogen Energy Storage System Based on Adaptive Simulated Annealing Particle Swarm Algorithm

As the low-carbon economy continues to evolve, the energy structure adjustment of using renewable energies to replace fossil fuel energies has become an inevitable trend. To increase the ratio of renewable energies in the electric power system and improve the economic efficiency of power generation systems based on renewables with hydrogen production, in this paper, an operation optimization model of a wind–solar hybrid hydrogen energy storage system is established based on electrochemical energy storage and hydrogen energy storage technology. The adaptive simulated annealing particle swarm algorithm is used to obtain the solution, and the results are compared with the standard particle swarm algorithm. The results show that the day-ahead operation scheme solved by the improved algorithm can save about 28% of the system operating cost throughout the day. The analytical results of the calculation example revealed that the established model had fully considered the actual operational features of devices in the system and could reduce the waste of wind and solar energy by adjusting the electricity purchased from the power grid and the charge and discharge powers of the storage batteries under the mechanism of time-of-use electricity price. The optimization of the day-ahead scheduling of the system achieved the minimization of daily system operation costs while ensuring that the hydrogen-producing power could meet the hydrogen demand.

Inversion of thermal properties of lunar soil from penetration heat of projectile using a 2D axisymmetric model and optimized PSO algorithm

The thermophysical parameters of lunar soil can be inferred from the temperature field during the invasion process of reconnaissance projectile. This paper adopts a two-dimensional axisymmetric model to reconstruct the projectile invasion process. An optimized particle swarm optimization method is then used to retrieve the thermophysical parameters of lunar soil. When the reconnaissance projectile penetrates the lunar interior, it rubs against the lunar soil and generates heat, which diffuses between the projectile body and the lunar soil. The sensors inside the reconnaissance projectile measure the temperature variation of the projectile body to inverse the thermophysical parameters. This paper carried out physical modeling of the penetration process of reconnaissance projectile. A two-dimensional axisymmetric simulation model is constructed for the physical process, and the adaptive inertial weight particle swarm algorithm is adopted. The inversion experiment of lunar soil thermophysical parameters based on the simulation model shows that the inversion error is less than 10%, which verifies the feasibility of this method.

Application of PSO Improved Algorithm in Motor Fault Diagnosis Simulation

Aiming at the shortcomings of the traditional fault diagnosis methods of electric motors, the author proposes a method based on the application of improved PSO algorithm in the simulation of motor fault diagnosis. The method optimizes the fault diagnosis method of BP neural network by adopting the adaptive mutation particle swarm algorithm. Firstly, the fault features are extracted from the response signals of the measurable points of the motor to be tested, and wavelet packet decomposition and normalization are performed to construct a sample set; then, the particle swarm improvement algorithm is used to optimize the weights and thresholds of the BP neural network, so as to realize the training and testing of the motor to be tested. In the fault diagnosis of a certain motor, it is found that the fault diagnosis time and diagnosis rate of this method are significantly improved compared with the previous ones, and the diagnosis rate reaches 99% when the center deviation range is 0.3, the efficiency of the fault diagnosis model for parameter optimization through the improved PSO algorithm is higher, and the accuracy of the diagnosis results is further improved.

Vehicle Positioning and Navigation in Asynchronous Navigation System

A Pseudo-satellite system that transmits signals similar to GNSS can provide positioning services in places where GNSS signals are not captured and have enormous potential for indoor machine system and airports. Different paths of the device have different carrier phase initial solution positioning accuracy. Existing methods rely on measuring instruments or use many coordinate points for solving ambiguity resolution (AR), which creates inconvenience for real-time ground positioning. This study aims to find a new on-the-fly (OTF) method to achieve high accuracy and convenient positioning. A new method is proposed based on a two-difference observation model for ground-based high-precision point positioning. We used an adaptive particle swarm algorithm to solve the initial solution, followed by a nonlinear least-squares method to optimize the localization solution. It is free of priori information or measuring instruments. We designed several different paths, such as circular trajectory and square trajectory, to study the positioning accuracy of the solution. Simulation experiments with different trajectories showed that geometric changes significantly impact solutions. In addition, it does not require precise time synchronization of the base stations, making the whole system much easier to deploy. We built a real-world pseudo-satellite system and used a multi-sensor crewless vehicle as a receiver. Real-world experiments showed that our approach could achieve centimeter-level positioning accuracy in applications.

Analysis of the Distributed Immune Inspection Equipment Sensor Scheduling Model Based on Adaptive Dynamic Probabilistic Particle Swarm Optimization

Based on the analysis of bacterial parasitic behavior and biological immune mechanism, this paper puts forward the basic idea and implementation method of an embedding adaptive dynamic probabilistic parasitic immune mechanism into a particle swarm optimization algorithm and constructs particle swarm optimization based on an adaptive dynamic probabilistic parasitic immune mechanism algorithm. The specific idea is to use the elite learning mechanism for the parasitic group with a strong parasitic ability to improve the ability of the algorithm to jump out of the local extreme value, and the host will generate acquired immunity against the parasitic behavior of the parasitic group to enhance the diversity of the host population’s particles. Parasitic behavior occurs when the number of times reaches a predetermined algebra. In this paper, an example simulation is carried out for the prescheduling and dynamic scheduling of immune inspection. The effectiveness of prescheduling for immune inspection is verified, and the rules constructed by the adaptive dynamic probability particle swarm algorithm and seven commonly used scheduling rules are tested on two common dynamic events of emergency task insertion and subdistributed immune inspection equipment failure. In contrast, the experimental data was analyzed. From the analysis of experimental results, under the indicator of minimum completion time, the overall performance of the adaptive dynamic probability particle swarm optimization algorithm in 20 emergency task insertion instances and 20 subdistributed immune inspection equipment failure instances is better than that of seven scheduling rules. Therefore, in the two dynamic events of emergency task insertion and subdistributed immune inspection equipment failure, the adaptive dynamic probabilistic particle swarm algorithm proposed in this paper can construct effective scheduling rules for the rescheduling of the system when dynamic events occur and the constructed scheduling. The performance of the rules is better than that of the commonly used scheduling rules. Among the commonly used scheduling rules, the performance of the FIFO scheduling rules is also better. In general, the immune inspection scheduling multiagent system in this paper can complete the prescheduling of immune inspection and process dynamic events of the inspection process and realize the prereactive scheduling of the immune inspection process.

A Compact Adaptive Particle Swarm Optimization Algorithm in the Application of the Mobile Sensor Localization

The mobile sensor network can sense and collect the data information of the monitored object in real time in the monitoring area. However, the collected information is meaningful only if the location of the node is known. This paper mainly optimizes the Monte Carlo Localization (MCL) in mobile sensor positioning technology. In recent years, the rapid development of heuristic algorithms has provided solutions to many complex problems. This paper combines the compact strategy into the adaptive particle swarm algorithm and proposes a compact adaptive particle swarm algorithm (cAPSO). The compact strategy replaces the specific position of each particle by the distribution probability of the particle swarm, which greatly reduces the memory usage. The performance of cAPSO is tested on 28 test functions of CEC2013, and compared with some existing heuristic algorithms, it proves that cAPSO has a better performance. At the same time, cAPSO is applied to MCL technology to improve the accuracy of node localization, and compared with other heuristic algorithms in the accuracy of MCL, the results show that cAPSO has a better performance.

Integral Layout Optimization of Subsea Production Control System Considering Three-Dimensional Space Constraint

The subsea production control system, characterized by a complex and diverse structure and high cost, is one of the essential parts of a subsea production system. The rational layout of the subsea production control system is essential to reduce development costs and ensure safe production in offshore fields. Most previous studies on layout design in offshore fields have focused on the oil- and gas-gathering system. However, the layout of the subsea production control system has not thoroughly been researched to date and the seabed terrain and integral optimization have rarely been discussed. This paper focuses on the multi-layer star structure and multi-layer star-tree structure, two common layout structures of subsea production control systems, and establishes the corresponding model with obstacle and seabed terrain conditions. Obtaining the lowest possible total cost was the aim of the model. A hybrid algorithm combining the adaptive mutation particle swarm algorithm and the A-star algorithm was applied to integrally optimize the subsea distribution unit and umbilical touch down point positions, the pipe connection topology and pipe routes. The practicality of this approach is demonstrated by designing a layout with one FPSO and 22 subsea control modules. The results indicate that the multi-layer star-tree layout structure has a lower total cost compared to that of the multi-layer star layout structure. In addition, the results were compared with a case that ignores the seabed terrain, indicating differences in the total construction cost. This method provides engineers with quantitative references and reliable cost estimates to make decisions regarding the layout of the subsea production control system.

Wavelet Packet Transform-Assisted Least Squares Support Vector Machine for Gear Wear Degree Diagnosis

Wear degree detection of gears is an effective way to prevent faults. However, due to the interference of high-speed meshing vibration and environmental noise, the weak vibration signal generated by the gear is easily covered by the noise, which makes it difficult to detect the degree of wear. To address this issue, this paper proposes a novel gear wear degree diagnosis method based on local weighted scatter smoothing method (LOWESS), wavelet packet transform (WPT), and least square support vector machine (APSO-LSSVM) optimized by adaptive particle swarm algorithm. According to the low signal-to-noise ratio characteristic of gear vibration signal, LOWESS is first used to preprocess the signal spectrum. Then, the characteristic parameters used to characterize gear wear are extracted from different decomposition depths by WPT and, finally, combined with APSO-SVM to diagnose the degree of gear wear. Compared with the basic least squares support vector machine, the improved method has better performance in sample classification. The experimental results show that the method in this paper can effectively reduce the diagnosis error caused by background noise, and the diagnosis accuracy reaches 98.33%, which can provide a solution for the health status monitoring of gears.

An Improved Particle Swarm Optimization-Powered Adaptive Classification and Migration Visualization for Music Style

Based on the adaptive particle swarm algorithm and error backpropagation neural network, this paper proposes methods for different styles of music classification and migration visualization. This method has the advantages of simple structure, mature algorithm, and accurate optimization. It can find better network weights and thresholds so that particles can jump out of the local optimal solutions previously searched and search in a larger space. The global search uses the gradient method to accelerate the optimization and control the real-time generation effect of the music style transfer, thereby improving the learning performance and convergence performance of the entire network, ultimately improving the recognition rate of the entire system, and visualizing the musical perception. This kind of real-time information visualization is an artistic expression form, in which artificial intelligence imitates human synesthesia, and it is also a kind of performance art. Combining traditional music visualization and image style transfer adds specific content expression to music visualization and time sequence expression to image style transfer. This visual effect can help users generate unique and personalized portraits with music; it can also be widely used by artists to express the relationship between music and vision. The simulation results show that the method has better classification performance and has certain practical significance and reference value.