Gaussian Particle Swarm Optimization Research Articles

Multi-swarm surrogate model assisted PSO algorithm to minimize distribution network energy losses

The computational analysis of real-world engineering problems often relies on time-consuming simulation calculations. This presents challenges in balancing computational burden and precision when applying traditional metaheuristic algorithms to large-scale complex problems. While there have been numerous surrogate-assisted metaheuristic optimization algorithms proposed, most of them are designed for solving expensive problems within 30–50 dimensions. In this paper, a novel approach called Parallel-multi-swarm Gaussian Process Regression surrogate-based particle swarm optimization (Parallel-MS-GPRS-PSO) is proposed for high-dimensional problems. This approach combines a standard particle swarm optimization (PSO) algorithm, multi-swarm cooperative PSO (MSCPSO), and a GPR surrogate-based PSO (PSO-GPR). By integrating PSO-GPR and MSCPSO, the proposed method aims to effectively explore and exploit the search space while enhancing the global and local performance of the surrogate model. To validate the effectiveness of the proposed algorithm, it is compared with several existing PSO algorithms on seven benchmark functions and an engineering problem. The results illustrate that the Parallel-MS-GPRS-PSO approach outperforms the existing algorithms. Moreover, the method shows promising performance in computationally expensive engineering optimization problems with 288 variables.

Enhanced Adsorption Capacity of Methylene Blue Dye onto Kaolin through Acid Treatment: Batch Adsorption and Machine Learning Studies

Algerian kaolinite, sourced from Djebel Debbagh nuance 3 (DD3), was used as a low-cost adsorbent to remove methylene blue (MB) dye from water. Its adsorption capacity was enhanced through sulfuric acid treatment (treated-DD3). In response to the urgent demand for clean water, various technologies have been developed to address dye removal from wastewater. This study, specifically delving into the treatment of textile wastewater, examined the efficacy of treated-DD3 through adsorption processes. The acid treatment increased the surface area and pore volume of DD3. X-ray diffraction showed crystalline phases in both, with treated-DD3 having higher crystallinity. Fourier-transform infrared spectroscopy found no significant differences post-acid treatment. Scanning electron microscopy revealed DD3 had large, stacked particles with low surface area, while treated-DD3 had increased porosity and a smoother surface. Various parameters affecting MB adsorption were studied. The Langmuir and Freundlich models were used for isotherm parameters. Treated-DD3 exhibited a higher MB adsorption capacity (64.58 mg/g according to the Langmuir model) than DD3 (44.48 mg/g). Thermodynamic analysis indicated spontaneous and endothermic MB adsorption onto both DD3-BM and treated-DD3-BM systems under different pH conditions. Treated-DD3 effectively reduced chemical oxygen demand (from 304.056 mg/L to 34.44 mg/L) and biological oxygen demand (from 80 mg/L to 20 mg/L) in real textile wastewater. The adsorbent exhibited rapid removal and decolorization, surpassing 93% within the first 7 min of the experiment. The Gaussian process regression and particle swarm optimization (GPR–PSO) predicted MB adsorption capacity effectively (R = 0.9989, R2 = 0.9978, adj-R2 = 0.9978, RMSE = 1.1390, and MAE = 0.3926).

A hybrid-model optimization algorithm based on the Gaussian process and particle swarm optimization for mixed-variable CNN hyperparameter automatic search

A hybrid-model optimization algorithm based on the Gaussian process and particle swarm optimization for mixed-variable CNN hyperparameter automatic search

Short-term performance degradation prediction of proton exchange membrane fuel cell based on discrete wavelet transform and gaussian process regression

Proton exchange membrane fuel cell (PEMFC) is regarded as one of the most promising energy conversion devices, but cost and durability are two challenges that hinder its large-scale application. Short-term performance degradation prediction is of great importance for the optimization of operating conditions and maintenance strategies to improve the durability and reduce costs of the PEMFCs. This paper proposes a novel data-driven short-term prediction method combining discrete wavelet transform (DWT), gaussian process regression (GPR) and particle swarm optimization (PSO) algorithm. The DWT decomposes the voltage time series waveform into numerous sub-waveforms with different characteristics. The GPR is utilized to construct individual prediction models for distinct sub-waveforms and the final prediction outcomes are obtained by adding the results of each model. The PSO achieves the automatic hyper-parameters optimization in order to improve the accuracy and robustness of the GPR model. In addition, the effectiveness of the proposed method is fully validated by the degradation datasets of PEMFCs in constant and quasi-dynamic load current conditions as well as real road working conditions. Finally, compared with widely used data-driven models such as artificial neural networks (ANN), support vector machine (SVM), long short-term memory (LSTM) and newly proposed methods in relevant studies, the proposed method exhibits superior accuracy and stability for degradation prediction.

Reliability Optimization Method for Gas–Electric Integrated Energy Systems Considering Cyber–Physical Interactions

With the development in the field of energy and the growing demand for sustainable energy, gas–electric integrated energy systems are attracting attention as an emerging energy supply method. At the same time, with the deep application of information technology, the cyber–physical interactions of gas–electric integrated energy systems are increasingly enhanced. To this end, first, the reliability assessment indices of a gas–electric integrated energy system, which comprehensively considers the interactions between cyber–physical and different energy sources, are established in this paper to quantitatively assess the reliability level of the system under different fault and failure conditions. Second, to solve the reliability optimization problem, a comprehensive reliability enhancement optimization model is constructed in this paper, which targets the sum of the total penalties of the failure rate and average repair time modification. The impact of the cyber systems on the gas–electric integrated energy systems is transformed into a modification of the failure rate and the average repair time, and the model is solved by an adaptive Gaussian particle swarm optimization algorithm. Finally, the applicability and superiority of the adaptive Gaussian particle swarm optimization algorithm to the reliability optimization of the gas–electricity integrated energy system are verified by conducting simulation tests on the gas–electricity integrated energy system coupled with an 8-node distribution system and the 11-node natural gas system in Belgium. Furthermore, the effects of cyber systems and cyber-attacks on system reliability optimization are also analyzed to verify the effectiveness of the proposed method and the rationality of the newly defined reliability indices.

Structural optics design of the zoom system based on particle swarm optimization

Selection of initial structural parameters is an essential step for the zoom system design. In this paper, a new method based on Gaussian optics and Particle Swarm Optimization (PSO) algorithm is proposed to design initial structural parameters of the zoom system, and an optical automatic optimization design is carried out based on the initial structural parameters. Firstly, displacement parameters of the zoom group and the compensation group of the zoom system are derived based on Gaussian optics. Secondly, constraints of the zoom system are established using the PSO algorithm, and a fitness function is designed. Then, the initial structure parameters of the zoom system are optimized. Finally, uniform and monotonic movement of the cam curve is achieved in an example of a 6 × zoom system. The results indicate that our proposed methods improve the efficiency and accuracy of the zoom system design, which has guiding significance for the design of multi-group moving zoom system with high zoom ratio and other engineering applications.

Design of Model Predictive Control Weighting Factors for PMSM Using Gaussian Distribution-Based Particle Swarm Optimization

An improved particle swarm optimization (PSO) algorithm based on Gaussian distribution model is proposed to realize the autotuning of weighting factors for the cost function design in the model predictive control method. First, the design principle of the weighting factors in model predictive torque control for permanent magnet synchronous motor system is analyzed. Then, using the root mean square of the current error in the two-phase rotating coordinate system and the system switching frequency as references, the objective function of the particles in the PSO is designed by considering the main control goals of reducing the torque ripple, the current total harmonic distortion, and the switching frequency. The Gaussian individual optimal distribution model is used to update the particle position on the structure of the conventional PSO algorithm. The experimental results show that the proposed method can solve the problem of weighting factors design as it reduces the switching frequency of the system while achieving excellent steady-state performance.

Plate shape recognition based on Gaussian function and particle swarm optimization for roller quenching process

Plate shape recognition in the roller quenching process provides effective feedback signals for plate shape control. Its accuracy directly affects the control effect. Most studies have obtained the high recognition accuracy of the plate shape defects by neural networks or parameter identification methods, but cannot provide a sufficiently detailed feedback signal with controllable accuracy for plate shape control, limiting the further improvement of the control effect. For these problems, a plate shape recognition method for roller quenching process is proposed. Firstly, the roller quenching process and the representation of the plate shape are introduced, and the structure of the plate shape recognition method is designed. Secondly, the Gaussian function is used to describe the plate shape defects, and the physical significance of each parameter in the Gaussian function is analyzed. Based on the Gaussian function, a plate shape fitting method with iterative structure and controllable fitting accuracy is designed to extract the detailed information about each defect. Thirdly, according to the actual production experience, the expert rules are designed for plate shape classification and to evaluate the quality. Finally, the method is verified by industrial production data. The results verify the correctness and effectiveness of our method, indicating that the method can provide important guidance for the improvement of plate shape quality and lays an important foundation for plate shape control.

Comparison of the Performance of a Surrogate Based Gaussian Process, NSGA2 and PSO Multi-objective Optimization of the Operation and Fuzzy Structural Reliability of Water Distribution System: Case Study for the City of Asmara, Eritrea

Comparison of the Performance of a Surrogate Based Gaussian Process, NSGA2 and PSO Multi-objective Optimization of the Operation and Fuzzy Structural Reliability of Water Distribution System: Case Study for the City of Asmara, Eritrea

Thermal-Economic Optimization of Plate–Fin Heat Exchanger Using Improved Gaussian Quantum-Behaved Particle Swarm Algorithm

Heat exchangers are usually designed using a sophisticated process of trial-and-error to find proper values of unknown parameters which satisfy given requirements. Recently, the design of heat exchangers using evolutionary optimization algorithms has received attention. The major aim of the present study is to propose an improved Gaussian quantum-behaved particle swarm optimization (GQPSO) algorithm for enhanced optimization performance and its verification through application to a multivariable thermal-economic optimization problem of a crossflow plate–fin heat exchanger (PFHE). Three single objective functions: the number of entropy generation units (NEGUs), total annual cost (TAC), and heat exchanger surface area (A), were minimized separately by evaluating optimal values of seven unknown variables using four different PSO-based methods. By comparing the obtained best fitness values, the improved GQPSO approach could search quickly for better global optimal solutions by preventing particles from falling to the local minimum due to its modified local attractor scheme based on the Gaussian distributed random numbers. For example, the proposed GQPSO could predict further improved best fitness values of 40% for NEGUs, 17% for TAC, and 4.5% for A, respectively. Consequently, the present study suggests that the improved GQPSO approach with the modified local attractor scheme can be efficient in rapidly finding more suitable solutions for optimizing the thermal-economic problem of the crossflow PFHE.

A Study for Development Suitability of Biomass Power Generation Technology Based on GHG Emission Reduction Benefits and Growth Potential.

Biomass energy can alleviate global warming and solve energy depletion, which is increasingly concerned by the world. Due to the different emission reduction benefits and growth potential of different regions and biomass power generation technologies, analyzing the suitability of these technologies combined with regional conditions can more accurately guide the long- and short-term development of biomass power industry. However, there is no comprehensive evaluation of the benefits and potential of several biomass power generation technologies in different regions. Therefore, this paper introduces development suitability indexes and constructs life-cycle environmental impact and time-value economic impact assessment models, and a growth potential dynamic assessment model based on Gaussian process and particle swarm optimization to illustrate the greenhouse gas emission reduction benefits and growth potential of biomass power generation technologies. The empirical research of China shows that biomass gasification and direct combustion power generation can bring the best environmental benefits, and biogas power generation can bring the best economic benefits. For regions with abundant biomass resources and bad air condition, it is more suitable to develop gasification and direct combustion power generation technology, while mixed-combustion and biogas power generation technology are more suitable in regions with high electricity consumption.

Study on Minimum Miscibility Pressure of CO2–Oil System Based on Gaussian Process Regression and Particle Swarm Optimization Model

Abstract Injecting CO2 into the reservoir can not only improve crude oil recovery but also achieve the goal of CO2 geological storage. It can not only reduce the greenhouse effect but also obtain additional economic benefits. From the perspective of minimum miscible pressure, carbon dioxide flooding can be divided into miscible flooding and immiscible flooding, and miscible flooding is widely used in the oil field. The most important condition for miscible flooding is to achieve the minimum miscible pressure (MMP). In the study, combining the particle swarm optimization (PSO) with Gaussian process regression (GPR), a novel intelligent GPR and particle swarm optimization (GPR-PSO) method was proposed to establish the model of predicting the MMP of the CO2 and oil system. The model uses the database with more data than in the previous literature, with 365 data points, and the value range of the data is also wider. Moreover, the accuracy of GPR-PSO model was evaluated by statistical error and graph error and compared with the prediction results of existing models. The results show that compared with other models, the GPR-PSO model has higher accuracy and wider application range, the mean absolute relative error is only 1.66%. Meanwhile, the reliability of the model is verified by the sensitivity analysis of parameters. The results show that the most influential parameter on the prediction results is the reservoir temperature, and the least influential parameter is the critical temperature of injected gas. The GPR-PSO model can be used not only to predict the MMP of CO2 and oil system but also to predict the MMP of other gases and crude oil system.

Gaussian quantum particle swarm optimization-based wide-area power system stabilizer for damping inter-area oscillations

PurposeThe extensive increase in power demand has challenged the ability of power systems to deal with small-signal oscillations such as inter-area oscillations, which occur under unseen operating conditions. A wide-area measurement system with a phasor measurement unit (PMU) in the power network enhances the observability of the power grid under a wide range of operating conditions. This paper aims to propose a wide-area power system stabilizer (WAPSS) based on Gaussian quantum particle swarm optimization (GQPSO) using the wide-area signals from a PMU to handle the inter-area oscillations in the system with a higher degree of controllability.Design/methodology/approachIn the design of the wide-area stabilizer, a dead band is introduced to mitigate the influence of ambient signal frequency fluctuations. The location and the input signal of the wide-area stabilizer are selected using the participation factor and controllability index calculations. An improved particle swarm optimization (PSO) technique, namely, GQPSO, is used to optimize the variables of the WAPSS to move the unstable inter-area modes to a stable region in the s-plane, thereby improving the overall system stability.FindingsThe proposed GQPSO-based WAPSS is compared with the PSO-based WAPSS, genetic algorithm-based WAPSS and power system stabilizer. Eigenvalue analysis, time-domain simulation responses and performance index analysis are used to assess performance. The various evaluation techniques show that GQPSO WAPSS has a consistently good performance, with a higher damping ratio, faster convergence with fewer oscillations and a minimum error in the performance index analysis, indicating a more stable system with effective oscillation damping.Originality/valueThis paper proposes an optimally tuned design for the WAPSS with a wide-area input along with a dead-band structure for damping the inter-area oscillations. Tie line power is used as the input to the WAPSS and optimal tuning of the WAPSS is performed using an improved PSO algorithm, known as Gaussian quantum PSO.

Hybrid algorithm for multi-objective optimization design of parallel manipulators

In this paper, a hybrid algorithm was proposed for multi-objective optimization design with high efficiency and low computational cost based on the Gaussian process regression and particle swarm optimization algorithm. For the proposed method, the global performance indices, including regular workspace volume, global transmission index, global stiffness index, and global dynamic index were considered as objective functions. First, the multi-objective optimization problem considering the boundary conditions, objective, and constraint functions was constructed. Second, the Latin hypercube design was regarded as the design of experiment to obtain the computer sample points. Besides, the high-precision objective-function values were obtained by increasing the node density in the workspace at these sample points to provide sufficient information for the mapping model. Third, the Gaussian process regression was proposed to build the mapping model between the objective functions and the design parameters, thus reducing the computational cost of global performance indices. Cross-validation and external validation were adopted to verify the mapping model. Finally, the hybrid algorithm combined with the Gaussian process regression and particle swarm optimization algorithm was proposed for multi-objective optimization design. The 2PRU-UPR parallel manipulator was taken as a case to implement the proposed method (where P was a prismatic joint; R a revolute joint; U a universal joint). The comparison from the back propagation neural network, multivariate regression, and Gaussian process regression mapping models showed that the Gaussian process regression model had higher accuracy and robustness. The proposed hybrid algorithm saved 99.84% of computational cost compared to using the particle swarm optimization algorithm. The Pareto frontier of the multi-objective optimization problem of the 2PRU-UPR parallel manipulator was also obtained. After optimization, the performance indices were significantly improved.

Zoom lens with high zoom ratio design based on Gaussian bracket and particle swarm optimization.

In this paper, we use Gaussian brackets and a particle swarm optimization (PSO) algorithm to design a 20× four-group zoom lens with a focus tunable lens and two moving groups. This method uses Gaussian brackets to derive the paraxial design equations of the zoom lens and determine the lens parameters. In the optimization stage, we define an objective function as a performance indicator to optimize its first-order design and implement the PSO algorithm in MATLAB to find its global optimal first-order design. The optimized 20× zoom lens has a focal length of 4.5-90 mm and a total length of 145 mm. This method solves the difficulty of solving the initial structure of the zoom. Compared with traditional trial and error, the calculation speed is faster, the accuracy is higher, and it does not rely on the initial value. The results show that this method is suitable for the first-order design of complex optical systems.

Interactive teaching learning based optimization technique for multiple object tracking

In this paper, an Interactive Teaching Learning Based Optimization (In-TLBO) algorithm is proposed for tracking multiple objects with several challenges. The performance of the four other competitive approaches, such as the Mean Shift (MS), Particle Swarm Optimization (PSO), Sequential PSO (SPSO), and Adaptive Gaussian Particle Swarm Optimization (AGPSO) are investigated for comparison. The quantitative and qualitative analyses of these approaches have been performed to demonstrate their efficacy. The comparison of various performance measures includes the convergence rate, tracking accuracy, Mean Square Error (MSE) and coverage test. To assess the dominance of the proposed In-TLBO approach, Sign and Wilcoxon test are also performed. These two non-parametric tests reveal considerable advancement of the proposed Interactive TLBO (In-TLBO) over other four competitive approaches. In-TLBO shows significant improvement over the MS and PSO algorithms with a level of significance α = 0.05, and over SPSO, with a level of significance α = 0.1 by considering detection rate as winning parameter. The analyses of comparative results demonstrate that the proposed approach effectively tracks similar objects in the presence of many real time challenges.

Measurement of oil fraction in oil-water dispersed flow with swept-frequency ultrasound attenuation method

It is very meaningful to measure the oil fraction of oil-water dispersed flow in pipelines. Traditional single-frequency ultrasonic methods measure the oil fraction by treating the dispersed flow with an average droplet size, which ignores the influence of the droplet size distribution and limits the measurement accuracy. Therefore, a swept-frequency ultrasound attenuation method based on droplet size distribution is proposed for the measurement of oil fraction. According to the characteristics of similar physical parameters and large droplet size of oil-water two phases, the Bouguer-Lambert-Beer's Law and McClements model are combined to predict ultrasonic attenuation in the oil-water dispersed flow, considering the attenuation effects of absorption and scattering. Due to the time-varying characteristic of the oil-water two-phase flow state, the chirp-based detection method is proposed to quickly measure the ultrasonic attenuation of multiple frequencies. For the nonlinear problem of ultrasonic attenuation response, trust region algorithm combined with Gaussian quantum particle swarm optimization is proposed for inversion calculation of oil fraction. Finally, the effectiveness of the ultrasonic attenuation mechanism model and inversion algorithm is verified by simulation and numerical calculation, and the experiments are carried out in the test device for liquid-liquid two-phase flow. The experimental results show that the proposed method can measure the oil fraction of oil-water dispersed flow with an average error less than 1.67% and a maximum error less than 3.57%.

Advanced Prediction of Roadway Broken Rock Zone Based on a Novel Hybrid Soft Computing Model Using Gaussian Process and Particle Swarm Optimization

A simple and accurate evaluation method of broken rock zone thickness (BRZT), which is usually used to describe the broken rock zone (BRZ), is meaningful, due to its ability to provide a reference for the roadway stability evaluation and support design. To create a relationship between various geological variables and the broken rock zone thickness (BRZT), the multiple linear regression (MLR), artificial neural network (ANN), Gaussian process (GP) and particle swarm optimization algorithm (PSO)-GP method were utilized, and the corresponding intelligence models were developed based on the database collected from various mines in China. Four variables including embedding depth (ED), drift span (DS), surrounding rock mass strength (RMS) and joint index (JI) were selected to train the intelligence model, while broken rock zone thickness (BRZT) is chosen as the output variable, and the k-fold cross-validation method was applied in the training process. After training, three validation metrics including variance account for (VAF), determination coefficient (R2) and root mean squared error (RMSE) were applied to describe the predictive performance of these developed models. After comparing performance based on a ranking method, the obtained results show that the PSO-GP model provides the best predictive performance in estimating broken rock zone thickness (BRZT). In addition, the sensitive effect of collected variables on broken rock zone thickness (BRZT) can be listed as JI, ED, DS and RMS, and JI was found to be the most sensitive factor.

PV MPPT Control under Partial Shading Conditions with a Particle Replacement Gaussian Particle Swarm Optimization Method

PV MPPT Control under Partial Shading Conditions with a Particle Replacement Gaussian Particle Swarm Optimization Method

Employing a Gaussian Particle Swarm Optimization method for tuning Multi Input Multi Output‐fuzzy system as an integrated controller of a micro‐grid with stability analysis

AbstractThere are mainly two most essential problems in power networks, load frequency control and power flow management, which are grown recently because of growth in dimension/complication of grids. Present work suggests a controller based on fuzzy systems in which controller design is performed in a supervisory manner over a multiagent system aiming to control the frequency variation as well as generation cost minimization in the entire grid. The designing processes for low‐frequency controller (LFC) and management are mostly performed separately, which results in the disruption of both outputs. This challenge is tackled in this paper by the integration of them in the designing process. Additionally, stability guarantee is in high importance in the power systems, which is neglected in most of the related works. The Gaussian particle swarm optimization (GPSO) algorithm is applied for determining the optimal values of the decision variables, which can also guarantee the stability of the system by adopting a chaotic map by Gaussian function to balance the seeking abilities of particles that promotes the computation effectiveness without affecting the efficiency of the fuzzy controller. Then, the stability situationof the fuzzy + GPSO method is derived that guarantees a suitable global exploration and rapid convergence, with no require to gradients.