Genetic Particle Swarm Optimization Research Articles

MURE: Multi-layer real-time livestock management architecture with unmanned aerial vehicles using deep reinforcement learning

In recent years, the combination of unmanned aerial vehicles (UAVs) and wireless sensor networks (WSNs) has gained popularity in livestock management (LM) due to energy constraints and network instability. Limited energy storage of sensor nodes (SNs) and the possibility of packet loss contribute to fast energy consumption and unstable networks, respectively. UAVs serve as relay nodes and data sinks, addressing these issues by temporarily storing data to reduce SN workload and establishing mobile nodes for network stability. We propose two innovations based on previous work: 1) We introduce a multi-layer wireless network architecture, categorizing UAVs into two layers based on their functions including data collection and data processing. This enhances task parallelization, bridging performance gaps among multiple UAVs; 2) We overcome the mobility limitation of SNs, considering their real-time movement in the network. Through deep reinforcement learning, UAVs learn to cooperatively locate moving SNs. This accounts for the inevitable mobility of livestock in the industry. Additionally, we simulate the environment and compare our approach to traditional methods, evaluating metrics such as collected data per timestep (DCPS), energy consumed per timestep (ECPS), and network stability (NS). Experimental results demonstrate that our method outperforms traditional approaches, achieving a data collecting gain of 4.84% and 8.20% compared to the methods without considering SN mobility or the multi-layer characteristics of WSNs, respectively. Under energy consumption limits, our method yields energy savings of 3.00% and 1.35% respectively. Furthermore, we extensively study and validate our method against other path planning algorithms, including genetic particle swarm optimization (GPSO), modified central force optimization (MCFO), and rapidly-exploring random trees (RRT). Our approach surpasses these methods in terms of data collecting efficiency and network stability.

Optimization of Sewing Equipment Based on Improved Genetic-ant Colony Hybrid Algorithm

The optimization of the cutting path of the sample can effectively reduce the cutting time, thereby improving the production efficiency of numerical control processing. This paper comprehensively considers the impact of the cutting order and the position of the knife entry point on the cutting path, converts the cutting path problem into a type of traveling salesman problem (TSP), and proposes an improved genetic-particle swarm optimization algorithm. The selection mechanism of the algorithm combines the elitist retention strategy and roulette wheel selection method to accelerate the search for the optimal solution; the mutation strategy designs a linear decreasing mutation rate, which enhances the global search ability; at the same time, introduces the ant colony optimization algorithm to process the fitness function, adjusts the population evolution difference, and speeds up the optimization process. Through this hybrid algorithm, the cutting order of the sample can be quickly optimized, and the nearest neighbor algorithm is used to determine the position of the knife entry point. Tests are conducted on clothing patterning charts and standard examples. Compared with several commonly used algorithms, experimental results verify the feasibility and effectiveness of this algorithm

Identifying the plane position of the receiver coil in a multi‐transmitter IPT system through the identification of parameters

AbstractThe study introduces a method for identifying the location of receiver coils within a multi‐transmitter IPT system that utilizes shared transmitter coils. In contrast to the conventional receiver coil position recognition methods, this approach eliminates the need for additional detection coils and position sensors. Identifying the location of the receiver coil in a multi‐transmitter IPT system is achieved through the concurrent optimization of the transmitter coil and its electrical characteristics. The study presents the design of coil grouping and control mechanisms for a 3×3 multi‐transmitter. A mathematical model has been developed for the mutual inductance between receiver and transmitter coils on the X–Y plane. Adaptive cooperative particle swarm optimizer (ACPSO) facilitates the identification of mutual inductance and load parameters in a multi‐transmitter IPT system, positioning the receiver coil in a 2D plane using genetic particle swarm optimization (GAPSO) and a mutual inductance mathematical model. The discrepancy in accuracy between the receiver coil's test coordinates and the absolute coordinates remains below 3.5%.

Optimal Allocation of Water Resources in Canal Systems Based on the Improved Grey Wolf Algorithm

Xinjiang is located in the arid region of northwestern China, and agriculture accounts for an absolute share of total water use. Resource-based, engineering, structural, and managed water shortages coexist. Therefore, it is of great significance to vigorously develop water conservation technology and improve the efficiency of water transmission and distribution in canal systems. This research aims at addressing the problems of difficult manual regulation and the overall optimization of the final canal system, low-water-resource utilization efficiency, and management efficiency. Taking the branch-double two-stage canal system of Dongfeng branch canal in Mangxiang, Jinghe irrigation district, as a case study, and the rotation irrigation group and irrigation duration as decision variables, canal distribution is modeled with the goal of minimizing seepage losses. The improved grey wolf algorithm combined with particle swarm optimization is used for the first time and compared with the traditional grey wolf algorithm, genetic particle swarm optimization fusion algorithm, and northern goshawk algorithm. The results show that (1) on the basis of meeting the water discharge capacity and water demand requirements of the canal system, the diversion time of the water distribution scheme obtained by using the improved grey wolf algorithm is shortened from 11 d to 8.91 d compared with the traditional empirical water distribution scheme. (2) The improved grey wolf algorithm converges to the optimal value within 10 generations compared to the remaining methods, and the total water leakage is reduced from 16.15 × 104 m3 to 11.75 × 104 m3. (3) The number of gate adjustments is reduced, and the canal gates are opened and closed at the same time within each rotational irrigation group. The grey wolf algorithm improved by its combination with particle swarm has stronger optimization ability and convergence, which can better meet the requirements of efficient water resource allocation in irrigation canal systems, as well as a high application value.

Short-term prediction of photovoltaic power generation based on ICEEMDAN-SE-GAPSO-LSTM

This study proposes an improved adaptive noise-complete ensemble empirical mode decomposition (ICEEMDAN), sample entropy (SE), and genetic particle swarm optimization (GAPSO) algorithm-based short-term solar power forecast model. The photovoltaic power data is first decomposed using the ICEEMDAN algorithm to produce a number of eigenmode components with various properties. The sample entropy of each component is then calculated, and the components with similar sample entropy values are combined to create new modal components. These new modal components are then input into the GAPSO-optimized LSTM model for prediction, and the prediction results of each component are summed and reconstructed to produce the final prediction. The results completely validate the efficacy and dependability of the suggested method, using the measured data from a photovoltaic power station in Xinjiang, China as an example.

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.

Fuzzy Genetic Particle Swarm Optimization Convolution Neural Network Based On Oral Cancer Identification System

Oral cancer is the eighth most common type of cancer in the world. Every year, 130,000 people in India die from mouth cancer. Getting a diagnosis from a clinical exam by skilled doctors and a biopsy takes time. When a problem is found early, it is always easier to treat. The primary goal of this work is to recognise disease-affected oral regions in a given oral image and classify the oral cancer disorder. This study employs unique Deep Learning algorithms to detect the location of disease-affected oral areas. This work employs the most effective feature extraction techniques, including appearance and patter-based features. Following feature extraction, the Bee Pulse Couple Neural Network (BeePCNN) algorithm is used to choose the best feature. Finally, Deep Learning is used to classify these attributes. An innovative FGPSOCNN reduces the computational complexity of CNN. On an additional real-time data set from Arthi Scan Hospital, a secondary evaluation is conducted. The experimental results indicate that the innovative FGPSOCNN performs better than existing methods.

Single-Frequency GNSS Integer Ambiguity Solving Based on Adaptive Genetic Particle Swarm Optimization Algorithm

Single-Frequency GNSS Integer Ambiguity Solving Based on Adaptive Genetic Particle Swarm Optimization Algorithm

Enhancing daily rainfall prediction in urban areas: a comparative study of hybrid artificial intelligence models with optimization algorithms

Forecasting precipitation is a crucial input to hydrological models and hydrological event management. Accurate forecasts minimize the impact of extreme events on communities and infrastructure by providing timely and reliable information. In this study, six artificial intelligent hybrid models are developed to predict daily rainfall in urban areas by combining the firefly optimization algorithm (FA), invasive weed optimization algorithm (IWO), genetic particle swarm optimization algorithm (GAPSO), neural network (ANN), group method of data handling (GMDH), and wavelet transformation. Optimization algorithms increase forecasting accuracy by controlling all stages. A variety of criteria are used for validating the models, including correlation coefficient (R), root-mean-square error (RMSE), mean absolute error (MAE), critical success index (CSI), probability of detection (POD), and false alarm ratio (FAR). The proposed models are also evaluated in an urban area in Ahvaz, Iran. The GAPSO-Wavelet-ANN model is superior to other models for predicting daily rainfall, with an RMSE of 1.42 mm and an R of 0.9715.

A sliding mode control of the bearingless permanent magnet slice motor for the blood pump based on the GAPSO

In this paper, we proposed a sliding mode control method for the bearingless permanent magnet slice motor for the blood pump based on the genetic particle swarm algorithm, which aims to solve the problems of strong coupling, strong interference, nonlinearity and uncertainty. Firstly, the mathematical model of rotor torque and suspension force of the bearingless permanent magnet slice motor is established. Secondly, the structure of sliding mode observer is deduced by designing sliding mode surface and control law. And, the performance parameters of sliding mode observer are optimized by the genetic particle swarm optimization algorithm. Thirdly, electromagnetic torque and suspension force control under this control method is studied by Simulink. Finally, the control method is applied to the control of the blood flow of the blood pump, and the rotation speed can effectively control the blood flow. The results indicate that compared with PID control and traditional sliding mode control methods, the sliding mode control method optimized by the genetic particle swarm optimization algorithm greatly improves the control performance of bearingless permanent magnet slice motor. The results show that the blood flow can meet expectations with a small error, which fully meets the blood perfusion requirements of the blood pump.

Active Distribution Network Reactive Power Optimization Based on an Improved Genetic Particle Swarm Optimization Algorithm

As the distributed generation is gradually connected to the distribution network, it will lead to an increase in node voltage deviation and network loss. The active distribution network reactive power optimization approach based on an improved genetic particle swarm optimization algorithm is proposed in this paper to increase voltage quality and system efficiency. First, a reactive power optimization model for active distribution networks that has the least possible active power loss and voltage variation is developed. Then, the genetic idea is applied to the particle swarm optimization algorithm to improve it and address the issues of the long iteration time of the genetic algorithm and the tendency of the particle swarm optimization algorithm to settle into local optimal solutions. Finally, the enhanced genetic particle swarm optimization algorithm described in this paper is validated using the enhanced IEEE-33 node system. The findings indicate that this strategy can significantly shorten computation times while also increasing calculation precision.

Chromatography Denoising with Improved Wavelet Thresholding Based on Modified Genetic Particle Swarm Optimization

The wavelet threshold functions are widely used in oil chromatography denoising because high-quality signals are the basis for Dissolved Gas Analysis (DGA), which determines the accuracy of transformer fault monitoring. However, there are certain limitations of the wavelet threshold functions, such as the Pseudo-Gibbs phenomenon and improper threshold selection. To this purpose, a modified genetic particle swarm optimization-based improved threshold function denoising method (MGPSO-ITF) is proposed. Specifically, the method constructs a new parametric threshold function that possesses high-order derivability and a small constant deviation. To obtain optimal values for the tunable parameters, MGPSO is employed, which outperforms other methods in identifying the optimum and achieving fast convergence. The simulation results demonstrate that the enhanced thresholding function yields a higher Signal-to-Noise Ratio (SNR), higher Noise Suppression Ratio (NSR), and smaller Root Mean Square Error (RMSE) compared to prior methods. Specifically, for the originally relatively smooth signal, MGPSO-ITF does not over-correct it to cause distortion. Furthermore, experiments on measured signals illustrate that the MGPSO-ITF is highly effective at denoising and preserving the original signal properties. Particularly in cases where peak deformation is prominent, the algorithm outperforms both hard and soft thresholding methods, achieving a reduction of 2.934% and 1.029% in peak area error, respectively.

Adaptive fractional-order genetic-particle swarm optimization Otsu algorithm for image segmentation

Adaptive fractional-order genetic-particle swarm optimization Otsu algorithm for image segmentation

Research on Path Tracking and Yaw Stability Coordination Control Strategy for Four-Wheel Independent Drive Electric Trucks

Achieving accurate path tracking and vehicle stability control for four-wheel independent drive electric trucks under complex driving conditions, such as high speed and low adhesion, remains a major challenge in current research. Poor coordination control may cause the vehicle to deviate from its intended path and become unstable. To address this issue, this article proposes a coordinated control strategy consisting of a three-layer control framework. In the upper layer controller design, establish a linear quadratic regulator (LQR) path tracking controller to ensure precise steering control by eliminating steady-state errors through feedforward control. The middle layer controller utilizes the fractional order sliding mode control (FOSMC) yaw moment controller to calculate the additional yaw moment based on the steering angle of the upper input, utilizing the error of yaw rate and sideslip angle as the state variable. To collectively optimize the control system, establish a coordinated optimization objective function and utilize the hybrid genetic-particle swarm optimization algorithm (GA-PSO) to optimize the weight coefficient of LQR and sliding mode parameters of FOSMC, effectively improving the performance of the controller. In the lower layer torque distribution controller, use the quadratic programming method to achieve real-time optimal torque distribution based on tire utilization, which improves vehicle stability control. Through simulations conducted under four different working conditions, the proposed control scheme demonstrates a 15.54% to 23.17% improvement in tracking performance and a 10.83% to 23.88% optimization in vehicle driving stability compared to other control methods. This scheme provides a theoretical reference for path tracking and stability control in four-wheel independent drive electric trucks.

Wing geometry and kinematic parameters optimization of bat-like robot fixed-altitude flight for minimum energy

In this paper, the geometry parameters (WGP) and kinematic parameters (WKP) of bat wings are optimized by using the revised quasi-steady aerodynamic model and genetic particle swarm optimization algorithm (GPSO) according to the structural characteristics and kinematic law of bat, to meet the requirement of minimum flapping energy and improve the endurance of bat-like robot. For the first time, the dynamic model of bat flapping motion is improved by considering the effects of active deformation, passive flexible deformation, gravity moment, and inertia moment of the wing structure. Based on the power density model, the optimization objective function is established, and the lift-weight ratio (LtoW), Reynolds number (Re), aspect ratio (AR), Strouhal number (St), and boundary constraints of each parameter are added to complete the optimization of WGP and WKP. The optimized aerodynamic forces and power densities are compared with the estimates for actual bats flying at a fixed-altitude, and the results showed that the optimized parameters resulted in more stable aerodynamic forces and reduced energy consumption by half. Finally, the sensitivity analysis of the influence of each parameter on the LtoW and power density is carried out to better understand the effect of each parameter on keeping fixed-altitude flight and reducing energy consumption. This study can provide the theoretical basis for reasonable parameter design of bat-like robot.

An efficient wind measurement method with chaotic-sequence improved genetic-particle swarm optimization algorithm

Wind power is widely used in industry, meteorology, shipping and so on. Accurate measurement of wind parameters is the key to improve the efficiency of wind power application. But at present, wind parameters are largely measured by different devices based on time difference method, which is easily influnced by enviromental noise. Beam-forming algorithm can improve the ability to resist environmental noise and the accuracy of hardware itself. Therefore, the beam-forming algorithm can be used to measure wind parameters in the high noise environment. However, the efficiency of the algorithm depends on how to search for spectral peak. In this paper, a three-dimensional wind measurement method with chaotic-sequence improved genetic-particle swarm optimization algorithm is proposed to improve the waveform searching efficiency of beamforming algorithm. It first searches for rough target wind parameters globally, and then searches for precise target wind parameters locally. Through simulation verification, the proposed algorithm can measure the wind parameters after 0.087s under the condition of system error of 50dB and environmental noise of 20dB, the accuracy of wind speed is 0.5%, the accuracy of wind direction is 1%, and the accuracy of pitch angle is 0.5%. Compared with the wind measurement by traversal method, the proposed algorithm can improve the wind measurement efficiency by about 20 times, and has similar or even better measurement results.. And by comparing with other algorithms, the advantages of this algorithm are verified.

Enforcing Intelligent Learning-Based Security in Internet of Everything

The exponential growth of the Internet of Everything (IoE), in recent times, has revealed many underlying security vulnerabilities of the nodes forming IoE networks. The extension of conventional security protocol to these devices has been greatly complicated by the prevalence of restricted computational hardware and limited battery life. Modern learning-based algorithms have shown the potential to secure the IoE networks without undue duress on the nodes’ limited capabilities. In this article, a machine learning-based architecture has been proposed to identify malicious and benign nodes in an IoE network operating with big data. A novel approach for the cooperation of XGBoost and deep learning models along with a genetic particle swarm optimization (GPSO) algorithm to discover the optimal architectures of individual machine learning models has been proposed. Through simulations, it is shown that GPSO-based learning algorithms provide reliable, robust, and scalable solutions. The proposed model significantly outperforms other security protocols in the classification of malicious and benign nodes forming an IoE network.

Optimization design and performance analysis of ammonia heat pipe air heater under low ambient temperature conditions

In coal-fired power plants, steam-based air heaters consume high-quality steam and are easily damaged due to water freezing under low ambient temperature conditions. In this paper, we propose an ammonia heat pipe air heater design with hot water in power plants as the heat source, which can eliminate the risk of freezing and save high-quality steam. Based on distributed parameter model and hybrid genetic particle swarm optimization algorithm, an ammonia heat pipe air heater with one-pipe diameter is designed and optimized. The results show that the optimal one-pipe diameter design consumes much less power and owns less weight than the normal empirical design. A new and optimal two-pipe diameter design (i.e., multi-scale design) is further proposed, which drops the power consumption by 10.1% and the weight by 9.4% compared to the optimal one-pipe diameter design. The two optimal designs require different geometrical parameters, especially the pipe diameter(s), the pipe length, the number of heat pipes, and the air side longitudinal pitch. In addition, the two optimal designs provide slightly higher outlet air temperature uniformity than the non-optimal design. Of the three working fluids, i.e., ammonia, methanol, and ethanol, the ammonia heat pipe air heater performs best.

Research on preventive maintenance strategy of Coating Machine based on dynamic failure rate

In this paper, a dynamic preventive maintenance strategy is proposed for the problem of high maintenance cost rate due to excessive maintenance caused by unreasonable maintenance threshold setting when complex electromechanical equipment maintenance strategy is formulated. Increasing failure rate factor and decreasing service age factor are introduced to describe the evolution rules of failure rate during the maintenance of the coating machine, and the BP-LSTM (BP-Long Short Term Memory Network, BP-LSTM) model is combined to predict the failure rate of the coating machine. A Dynamic preventive maintenance Model (DM) that relies on dynamic failure rate thresholds to classify the three preventive maintenance modes of minor, medium and major repairs is constructed. A dynamic preventive maintenance strategy optimization process based on Genetic-Particle Swarm Optimization (GPSO) algorithm with the lowest cost rate per unit time in service phase is built to solve the difficult problem of dynamic failure rate threshold finding. Based on the historical operating data of the coating machine, a case study of the dynamic preventive maintenance strategy of the coating machine was conducted to verify the effectiveness of the model and the developed maintenance strategy proposed in this paper. The results show that the maintenance strategy developed in this paper can ensure better economy and applicability.

Optimization of process parameter in AI7075 turning using grey relational, desirability function and metaheuristics

ABSTRACT Even though metal may be effectively shaped by a variety of other manufacturing techniques, machining continues to play a significant role in industries. Turning is a conventional chip-forming operation that removes undesirable or surplus material from a cylindrical workpiece. The major objective of process optimization in turning operation research is focusing on the development of statistical modeling and optimization techniques for boosting production rate, lowering costs, and reducing product rejection. The current work aims to improve the CNC turning process of Al 7075 using coated carbide inserts. This investigation uses grey relational analysis, desirability function analysis, Multi-objective Genetic Algorithm (MOGA) and Multi-objective Genetic Particle Swarm Optimization (MOPSO) to optimize factors to minimize responses like surface roughness and cutting force. Comparative evaluation of results of optimum input parameter sets results in close agreement, both in traditional optimization and metaheuristic-based optimization. In particular, MOGA is found to be more efficient to solve this stated multi-criterion decision-making problem as compared to other methods.