Improved Particle Swarm Algorithm Research Articles

Reentry trajectory optimization of hypersonic glide vehicle based on improved particle swarm algorithm

The reentry process of hypersonic glide vehicle presents a host of intricate constraint issues. A method for optimizing the reentry trajectory of hypersonic glide vehicle is presented to achieve the shortest possible reentry time. The approach utilizes the improved particle swarm optimization (IPSO) algorithm. Firstly, population initialization is performed using the quasi-oppositional differential evolution (QODE) strategy to promote diversity within the population. Then an adaptive adjustment algorithm is designed for the inertia weight coefficient and learning factor to achieve a balance between global and local search capabilities. Finally, utilizing the IPSO algorithm for optimizing the flight parameters of the reentry trajectory, the reentry time is reduced from 338 seconds to 335 seconds. The simulations results indicate that the IPSO algorithm outperforms both the standard PSO algorithm and genetic algorithm (GA) in terms of optimization performance index.

Assessment of salmon sashimi processing conditions for Listeria monocytogenes cross-contamination and effectiveness of CLPSO-BP neural network model constructed for predicting microbial transfer

During preparation stages of ready-to-eat (RTE) foods, they can be cross-contaminated with Listeria monocytogenes which could result to outbreaks of foodborne listeriosis. In this study, the effects of gloves, cutting boards and cleaning methods on the transmission of L. monocytogenes during the processing of salmon sashimi were investigated. Subsequently, an improved particle swarm algorithm optimized BP neural network prediction model (CLPSO-BP) was developed based on Lévy flight and chaotic mapping. After processing contaminated salmons with gloves, we found that the surfaces of the gloves contained a significant amount of L. monocytogenes (5.24 log CFU/g), and 10.69% of the bacteria were subsequently transferred to uninfected salmon after being processed with the same gloves. Contamination of the processing tools with L. monocytogenes was reduced by up to 3 log CFU/g and the transfer rate was reduced by up to 50% after cleaning with room temperature water and detergent. In contrast, no bacteria were found after wiping the surfaces of the tools 70 °C hot water and detergent. The mean squared error (MSE) for the CLPSO-BP modeling tests was 0.1003, indicating accurate predictions. Overall, choosing an appropriate cleaning method and changing gloves regularly can help to avoid cross-contamination when processing RTE foods.

Optimized Battery Capacity Allocation Method for Wind Farms with Dual Operating Conditions

In order to solve the problems of wind power output volatility and wind power participation in frequency regulation, a method for optimizing the capacity allocation of wind farm storage batteries based on the dual grouping strategy and considering the simultaneous execution of the dual conditions of energy storage in fluctuation smoothing and primary frequency regulation is proposed. Firstly, a two-layer model is established to optimize the capacity allocation under dual operating conditions, i.e., the planning layer takes into account the lifetime, cost, and benefit, and the operation layer considers the wind turbine reserve backup and storage control to participate in the primary frequency regulation in a cooperative manner. Then, the dual battery pack operation strategy is embedded with the variational modal decomposition method to determine the charging and discharging operation strategy of energy storage after considering the grid-optimized reference power. An improved particle swarm algorithm with inverse learning pre-optimization combined with variational crossover post-optimization is embedded in the GUROBI computation to obtain the optimal battery storage capacity allocation scheme. Finally, the superiority of the model proposed in this paper in terms of improving energy storage utilization, service life, and economic efficiency as well as reducing wind power load shedding is verified by comparing it with a single execution working condition scenario and traditional battery control strategy.

Adaptive Reactive Power Optimization in Offshore Wind Farms Based on an Improved Particle Swarm Algorithm

To address the reactive power optimization control problem in offshore wind farms (OWFs), this paper proposes an adaptive reactive power optimization control strategy based on an improved Particle Swarm Optimization (PSO) algorithm. Firstly, an OWF multi-objective optimization control model is established, with the total sum of voltage deviations at wind turbine (WT) terminals, active power network losses, and reactive power margin of WTs as comprehensive optimization objectives. Innovatively, adaptive weighting coefficients are introduced for the three sub-objectives, enabling the weights of each optimization objective to be adaptively adjusted based on real-time operating conditions, thus enhancing the adaptability of the reactive power optimization model to changes in operating conditions. Secondly, a Uniform Adaptive Particle Swarm Optimization (UAPSO) algorithm is proposed. On one hand, the algorithm initializes the particle swarm using a uniform initialization method; on the other hand, it improves the particle velocity update formula, allowing the inertia coefficient to adaptively adjust based on the number of iterations and the fitness ranking of particles. Simulation results demonstrate the following: (1) Under various operating conditions, the proposed adaptive multi-objective reactive power optimization strategy can ensure the stability of node voltages in offshore wind farms, reduce active power losses, and simultaneously improve reactive power margins. (2) Compared with the traditional PSO algorithm, UAPSO exhibits an approximately 10% improvement in solution speed and enhanced solution accuracy.

PECT Composite Defect Detection Algorithm Based on DualGAN

To address the problems of insufficient accuracy and slow reconstruction speed of Planar Electrical Capacitance Tomography (PECT) detection of damaged specimens, a Dual Generative Adversarial Networks (DualGAN)-based PECT image defect detection method is proposed in this paper. The improved particle swarm algorithm with adaptive particle number and L2-norm is used to optimize the sensitivity field, combined with the parallel Landweber algorithm to solve the PECT inverse problem to obtain the dielectric constant distribution map. In the DualGAN network, the Unet generator utilizes an Adam-based local attention mechanism to adjust module weights, facilitating feature extraction and the generation of high-quality transformation images of the Landweber dielectric constant distribution. A PatchGAN discriminator is employed to distinguish between transformation images and real images, using the generated transformation images as target images. Experimental results demonstrate that the sensitivity field, enhanced by the improved particle swarm algorithm and L2-norm normalization, achieves better balance. Furthermore, the addition of a network transformation using the Adam-based local attention weight mechanism on the DualGAN network reduces artifacts in the reconstructed images, resulting in more accurate PECT reconstructions. The PECT image defect detection method, integrating DualGAN, an improved particle swarm optimization algorithm, and a local attention mechanism, has made significant strides in addressing challenges related to image reconstruction accuracy and speed. This technological advancement has enhanced the precision and efficiency of defect detection in carbon fiber composite materials, thereby fostering the broader utilization of planar capacitance tomography technology in industrial damage detection and material defect analysis.

Research on optimal scheduling of microgrid based on improved quantum particle swarm optimization algorithm

INTRODUCTION: With the large-scale integration of new energy into the grid, the safety and reliability of the power grid have been severely tested. The optimized configuration of micro power systems is a key element of intelligent power systems, playing a crucial role in reducing energy consumption and environmental pollution.
 OBJECTIVES: a power grid optimization scheduling model is proposed that comprehensively considers the issues of power grid operating costs and environmental governance costs
 METHODS: Using quantum particle swarm optimization method to optimize the objective function with the lowest system operating cost and the lowest environmental governance cost. In order to improve the search ability of the algorithm and eliminate the problem of easily getting stuck in local optima, the Levy flight strategy is introduced, and the variable weight method is used to update the particle factor to improve the optimization ability of the algorithm.
 RESULTS: The simulation results show that the improved quantum particle swarm optimization algorithm has strong optimization ability, and the scheduling model proposed in this paper can achieve good scheduling results in different scheduling tasks.
 CONCLUSION: （1）The improved particle swarm algorithm, in comparison to itspredecessor, boasts a greater degree of optimization accuracy, aswifter convergence rate, and the capability to avoid the algorithm'sdescent into the local optimal solution at a later stage of the process. （2）The proposed model can effectively reduce users’ electricity costs and environmental pollution, and promote the optimized operation of microgrids.

Multi-Objective Optimal Scheduling of Microgrids Based on Improved Particle Swarm Algorithm

Microgrid optimization scheduling, as a crucial part of smart grid optimization, plays a significant role in reducing energy consumption and environmental pollution. The development goals of microgrids not only aim to meet the basic demands of electricity supply but also to enhance economic benefits and environmental protection. In this regard, a multi-objective optimization scheduling model for microgrids in grid-connected mode is proposed, which comprehensively considers the operational costs and environmental protection costs of microgrid systems. This model also incorporates improvements to the traditional particle swarm optimization (PSO) algorithm by considering inertia factors and particle adaptive mutation, and it utilizes the improved algorithm to solve the optimization model. Simulation results demonstrate that this model can effectively reduce electricity costs for users and environmental pollution, promoting the optimized operation of microgrids and verifying the superior performance of the improved PSO algorithm. After algorithmic improvements, the optimal total cost achieved was CNY 836.23, representing a decrease from the pre-improvement optimal value of CNY 850.

A method for testing partial discharge characteristics of switch cabinet based on improved particle swarm algorithm

In the current operational environment of the switch cabinet, the presence of mixed-frequency signals poses interference from white noise to partial discharge detection, resulting in suboptimal detection efficiency and accuracy. Therefore, this study explores a method for testing the partial discharge characteristics of switch cabinets based on an improved particle swarm algorithm. This novel approach involves signal processing of partial discharge in a switch cabinet, feature extraction based on the particle swarm algorithm, and feature output for partial discharge detection. Experimental comparisons demonstrate that the new detection method significantly enhances detection efficiency and ensures the accuracy of results, meeting the operational requirements of substations.

Analysis and optimization of a medium-depth ground source heat pump heating systems with heat storage and borehole heat exchangers

The medium-depth ground source heat pump (MD-GSHP) heating technology is considered a clean energy solution with the potential to mitigate air pollution issues attributed to winter heating in northern China. However, it currently suffers from problems such as a mismatch between heat supply and demand and a lack of optimal operation. To overcome these limitations, this study focuses on a 3000 m2 residential building in Xi’an and implements two configurations: coupling MD-GSHP heating systems with heat storage tank and shallow borehole heat exchanger (BHE). Additionally, an optimization strategy based on the improved particle swarm algorithm is proposed, integrating chaos optimization and dynamic parameters. Finally, noteworthy outcomes were revealed by optimizing the MD-GSHP heating systems under different objective functions. In system coupled with shallow BHE, the variation trend of flow in the medium-depth BHE was similar to that of heating load when the heating cost was taken as the objective function. After the optimization, heating cost reduced by 10.02%, while system coefficient of performance (COP) and medium-depth geothermal utilization coefficient increased by 10.43% and 2.00%, respectively. For system coupled with heat storage tank, the optimized flow exhibited a generally decreasing-increasing-decreasing trend, with the peak preceding the heating load curve. This resulted in a 12.82% reduction in heating cost, along with increases of 10.35% and 1.96% in system COP and the medium-depth geothermal utilization coefficient, respectively. Comprehensive consideration of heating cost and system COP suggested a favorable weight of 2.8 for continuous system operation. In conclusion, when prioritizing heating cost, the system coupled with heat storage tank yields greater economic benefits compared to the system coupled with shallow BHE. The findings of this study can provide a guidance for the applications of the MD-GSHP heating technology.

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.

Optimization of reactive power in distribution networks with DG based on improved particle swarm algorithm

Optimization of reactive power in distribution networks with DG based on improved particle swarm algorithm

Biomechanical analysis of the danger of the whip-leg striking maneuver in martial arts sparring bouts

Abstract This paper recognizes the human motion in martial arts movements based on the LSTM recurrent neural network algorithm, which uses signal statistics to extract the action time series features through the data collection from the relevant human motion sensors in order to effectively recognize the athletes’ movements. The parameter settings of the LSTM neural network model are optimized using an improved particle swarm algorithm. The experimental process is then created. After filming the video, it is divided into two documents, A and B, and saved. The experimenter’s actions undergo technical analysis using LSTM. Three groups of athletes were invited to carry out the actual test after the design was completed, and biomechanical analysis was performed based on the experimental data. The experiments showed that the activation of the left medial femoral muscle of the athletes was greater than that of the left lateral femoral muscle, and the root mean square of electromyography was 25.38±11 and 27.54+11, respectively. The angle of the left leg was 69±2.5, and that of the right leg was 71±9.2, which was more than 80% of the safe rotational amplitude. It is easy to cause sports injury if there is too much difference in the activation level of leg muscles during whiplash.

RPA-supported digital workforce in the field of statistical work in the electricity market

Abstract How to count a large amount of data and information more efficiently in electric power statistics has become the main problem that electric power enterprises should pay attention to. For this reason, this paper proposes a study on the application of digital employees using RPA technology in the statistical analysis of the electric power market. Optimizing power market statistics is achieved by using the improved particle swarm algorithm SN-PSO, which is based on the fitness function and particle coding strategy to address the unbalanced load phenomenon. In AI-related technology, we create digital employees and conduct case studies on electricity market statistics by combining RPA technology with image recognition technology, NLP, and knowledge graph technology. The results show that the customers of the power supply company that show negative growth in electricity are mainly concentrated in the manufacturing industry (average value of 21.44) and the mining industry (average value of 7.36). In contrast, from the perspective of regional distribution, the negative growth rate of the customers in the large customers is mainly concentrated in the region of E and the remaining region of the large customers, which indicates that these two regions need to strengthen tracking management. This study improves the statistical work mode of electrical enterprises in the information age so that electric power enterprises and China’s market economy can develop better.

Optimization of Food Processing Parameters Based on Parametric Models

Abstract In this paper, a food processing process optimization model is constructed based on the improved particle swarm algorithm. By adding the chaos mapping strategy to the basic particle swarm algorithm, the optimization efficiency of the particle swarm under the constraints is improved. Combining the multi-objective optimization capability of the NDWPSO algorithm, the process parameter optimization process is constructed to optimize the parameters of food processing processes. The proposed method for process parameter optimization is applied to the vacuum drying process parameter optimization experiment of Jujube to test the effectiveness of particle swarm process parameter optimization. The results show that the diversity value and the best-adapted value of the NDWPSO algorithm in the single-peak function only use a small number of iterations to drop to a level close to 0, indicating that the NDWPSO algorithm has a faster convergence speed. In the vacuum drying process of jujube, the theoretical values of the optimal process parameters for freeze-dried jujube slices are: jujube slices thickness 5mm, sublimation drying temperature -22℃, resolution drying temperature 20℃.

A Coupled Simulated Annealing and Particle Swarm Optimization Reliability-Based Design Optimization Strategy under Hybrid Uncertainties

As engineering systems become increasingly complex, reliability-based design optimization (RBDO) has been extensively studied in recent years and has made great progress. In order to achieve better optimization results, the mathematical model used needs to consider a large number of uncertain factors. Especially when considering mixed uncertainty factors, the contradiction between the large computational cost and the efficiency of the optimization algorithm becomes increasingly fierce. How to quickly find the optimal most probable point (MPP) will be an important research direction of RBDO. To solve this problem, this paper constructs a new RBDO method framework by combining an improved particle swarm algorithm (PSO) with excellent global optimization capabilities and a decoupling strategy using a simulated annealing algorithm (SA). This study improves the efficiency of the RBDO solution by quickly solving MPP points and decoupling optimization strategies. At the same time, the accuracy of RBDO results is ensured by enhancing global optimization capabilities. Finally, this article illustrates the superiority and feasibility of this method through three calculation examples.

RETRACTED:Optimum planning of energy hub with participation in electricity market and heat markets and application of integrated load response program with improved particle swarm algorithm

The drive for improved energy efficiency and flexibility has birthed an innovative solution: an energy hub. This hub operates in sync with natural gas and electricity grids, integrating batteries and thermal storage to optimize renewable energy use, reduce costs, and bolster network stability. In addressing these objectives, a mathematical optimization model for energy hub operation is developed in this paper. This model captures the intricate interplays between electricity and natural gas networks, accounting for the intricacies posed by uncertainties and variations in renewable energy sources and demand profiles. Also, this paper uniquely delves into the integration of load response programs, an approach geared towards amplifying the energy hub system's flexibility and reliability. Through these programs, subscribers gain the agency to calibrate their energy consumption based on real-time pricing signals and incentives dispensed by the energy hub. The simulation findings serve as a compelling testament to the efficacy of the proposed system. Operating costs witness a substantial reduction, the adoption of renewable energy sources is markedly heightened, and the stability and reliability of the upstream energy networks are tangibly ameliorated. The integration of load response programs emerges as a particularly effective strategy in mitigating peak demand and augmenting the system's overall energy efficiency. Underpinning the entirety of this innovative solution is the transformation of the challenges into an optimization problem. This catalyzes the employment of a newly developed particle community algorithm, custom-tailored to surmount the challenges of local optima. This algorithm inherently bolsters the system's search capabilities, minimizing the risk of entrapment within localized solutions. Ultimately, the proposed energy hub system encapsulates a promising trajectory toward the realization of a sustainable and adaptable energy landscape. By seamlessly integrating diverse energy sources and storage systems, augmenting energy flexibility and efficiency, and aligning with broader energy transition and decarbonization goals, this solution presents a formidable contender. The simulation outcomes underscore its potential by showcasing a substantial 4.57 % reduction in the cost of procuring energy from the upstream electrical network.

3D path planning problem for fighter aircraft with multiple constraints

AbstractPath planning is a crucial component for ensuring the safety and efficiency of flight missions, especially for fighter aircraft. To enhance the combat effectiveness of fighter aircraft, it is important to consider how to avoid danger sources an terrain obstacles, reduce fuel consumption, and utilize the aircraft's own performance to accomplish the mission objectives. In the modern battlefield environment, the shortest path is not the only criterion for planning, but also other factors such as the threat level to the aircraft, fuel consumption, mission completion time, and minimum turning radius. In this paper, the authors propose a multi‐constraint path planning method for fighter aircraft that incorporates these factors into an improved particle swarm algorithm. The authors transform the constraints of three‐dimensional terrain, threat source, fuel consumption, and mission time into an aggregated fitness function. The authors construct a limit curvature matrix to evaluate the feasibility of the generated path. The authors also introduce an adaptive adjustment strategy based on the activation function for the parameters in the particle swarm algorithm. The weights of each constraint are determined according to the actual demand. The experiment results show that the authors’ method can efficiently plan the optimal path that satisfies the requirements. Compared with other improved particle swarm algorithms, the authors’ method has higher optimal search efficiency and better convergence effect. The authors also provide optimal values for important parameters such as mission energy consumption, mission time, flight speed and others to support the overall mission planning. The authors’ method has a certain practical application value.

Scheduling optimization of IES based on improved particle swarm algorithm

The integrated energy system (IES) has been a qualified candidate for solving the problems of energy utilization and environmental pollution. In this study, an improved IES scheduling optimization model based on an improved particle swarm algorithm is established, and the daily operating cost is taken as the objective function. Constraints on the energy supply and consumption sides are fully considered. The results show that with the energy storage conversion scheduling strategy participating in the optimization, the daily cost is significantly decreased by 10.5%. Compared with the genetic algorithm with elite strategy, the particle swarm algorithm is more suitable for the IES, with the lowest daily cost of $ 6, 193. Besides, the improved annealing particle swarm algorithm has better adaptability when facing high-dimensional multimodal function problems, and performs better on global search and local optimization.

Path planning with obstacle avoidance for soft robots based on improved particle swarm optimization algorithm

Soft-bodied robots have the advantages of high flexibility and multiple degrees of freedom and have promising applications in exploring complex unstructured environments. Kinematic coupling exists for the soft robot in a problematic space environment for motion planning between the soft robot arm segments. In solving the soft robot inverse kinematics, there are only solutions or even no solutions, and soft robot obstacle avoidance control is tough to exist, as other problems. In this paper, we use the segmental constant curvature assumption to derive the positive and negative kinematic relationships and design the tip self-growth algorithm to reduce the difficulty of solving the parameters in the inverse kinematics of the soft robot to avoid kinematic coupling. Finally, by combining the improved particle swarm algorithm to optimize the paths, the convergence speed and reconciliation accuracy of the algorithm are further accelerated. The simulation results prove that the method can successfully move the soft robot in complex space with high computational efficiency and high accuracy, which verifies the effectiveness of the research.

Optimization of Algorithm for Solving Railroad Power Conditioner Compensation Power Reference Value and System Power Quality Analysis Based on Optimal Compensation Model

At present, electrified railroads with complex road conditions are facing the problems of the existence of a single power supply method, the deterioration in power quality, and the difficulty in recycling regenerative braking energy. In order to improve the above problems, this paper establishes a traction power supply system containing photovoltaic units, proposes an optimized compensation model for the RPC (railroad power conditioner), and improves the particle swarm algorithm for solving the reference value of RPC compensation power. First, the structure of the RPC-based traction photovoltaic power generation system and the establishment of the integrated energy management strategy of the system are constructed. Then, the back-to-back converter compensation model with power quality index parameter constraints is established, which establishes the optimization function with the objectives of minimizing the negative sequence current, maximizing the power factor, and minimizing the RPC compensation power, as well as establishes constraints on the active converter capacity and three-phase voltage imbalance. Then, the particle swarm algorithm for solving the RPC compensation power reference value is improved, specifically in the original PSO by introducing dynamically changing inertia weights and learning factors. This not only solves the problem of the single power supply and realizes the nearby consumption of photovoltaic units in the traction system, but also realizes the recycling of regenerative braking energy and the coordinated control of the traction photovoltaic power generation system, improves the power quality of the system, and meets the demand of the RPC for real-time control. In order to verify the effectiveness of the optimized compensation model established in this paper and improve the convergence of the particle swarm algorithm for solving the RPC compensation power reference value, a simulation model of the traction PV power generation system is established in MATLAB/Simulink, and a real-time simulation is carried out to verify it based on the preset working condition data. The results show that the RPC optimization compensation model developed in this paper can coordinate the control system energy flow and improve the system power quality (the power factor increases and the negative sequence current decreases). The improved particle swarm algorithm is more convergent.