Chaos Particle Swarm Optimization Research Articles

Integrated optimization of a turbine stage at a low Reynolds number via NURBS surface and machine learning

As the turbine load increases, both the blade profile losses and secondary flow losses in the endwall region cannot be ignored for turbines operating at low Reynolds numbers. To fully explore the flow control effects of the blade and endwall shapes, a viable integrated parameterization method and optimization method are formulated for turbine stages. NURBS surfaces are utilized for parameterizing the endwall and blade geometries. Combined with machine learning methods and improved chaos particle swarm optimization (ICPSO), an efficient surrogate model optimization strategy for high-dimensional small-sample problems is discussed. The above methods are applied to the integrated optimization of the endwalls and suction surfaces of a low-speed turbine stage, which is operated at a low Reynolds number. Under the constraint of nearly unchanged output power, the isentropic efficiency of the turbine stage is improved by 1.33%. The flow control effects of blade reshaping and endwall contouring are separately compared. The results show that the flow structures near the suction surfaces of the blades can be adjusted by blade reshaping. Moreover, the flow near the endwalls can be reorganized by using contoured endwalls and optimized blades, and the secondary flow in the endwall region can thus be significantly weakened.

Estimation of Site Effects and Equivalent Source Parameters of Wenchuan Earthquake Based on Generalized Chaotic Particle Inversion Technique

Abstract Based on the advantages of the chaos particle swarm optimization algorithm and the generalized inversion technology, this article estimates the source parameters and site effects of the Wenchuan earthquake. We used 440 sets of strong-motion records obtained from 43 aftershocks, and the area covered by the records was divided into subregions A and B. Initial separation of source, path, and site from the seismic spectra of subregions A and B using generalized inversion technique and then the source-site optimization model is established using chaotic particle swarm technology. From path-corrected records, we obtained absolute site effects for 33 stations and equivalent source parameters for 43 earthquakes. We made the following conclusions: (1) The moment magnitude Mw was lower than the local magnitude MLdetermined by China Earthquake Network Center. The self-similarity of the Wenchuan earthquake was confirmed. The stress drop averaged 2.31 MPa, and it was independent of the magnitude size and focal depth. (2) In the frequency 1–10 Hz, the quality factor values in subregions A and B are 110.9f0.6 and 116.1f1.2. The decay rate of the crustal medium in the western region of the west Sichuan plateau is significant compared to the eastern part. (3) Bedrock stations 51MXT and L2007 have site effects within a certain frequency. The effect of slope topography on site predominant frequency is not apparent, and the site effects increase with the increase in elevation. The shape of the site amplification curve is more similar in the middle- and low-frequency bands, and different attenuation phenomena will appear in the high-frequency band.

Intelligent recommendation method for offline course resources tax law based on chaos particle swarm optimization algorithm

In view of the individual differences in learners’ abilities, learning objectives, and learning time, an intelligent recommendation method for offline course resources of tax law based on the chaos particle swarm optimization algorithm is proposed to provide personalized digital courses for each learner. The concept map and knowledge structure theory are comprehended to create the network structure map of understanding points of tax law offline courses and determine the learning objectives of learners; the project response theory is used to analyze the ability of different learners; According to the learners’ learning objectives and ability level, the intelligent recommendation model of offline course resources of tax law is established with the minimum concept difference, minimum ability difference, minimum time difference, and minimum learning concept imbalance as the objective functions; Through the cultural framework, the chaotic particle swarm optimization algorithm based on the cultural framework is obtained by combining the particle swarm optimization algorithm and the chaotic mapping algorithm; The algorithm is used to solve the intelligent recommendation model, and the intelligent recommendation results of offline course resources in tax law are obtained. The experiential outcomes indicate that the process has a smaller inverse generation distance, larger super-volume, and smaller distribution performance index when solving the model; that is, the convergence performance and distribution performance of the model is better; This method can effectively recommend offline course resources of tax law for learners intelligently, and the minimum normalized cumulative loss gain is about 0.75, which is significantly higher than other methods, that is, the effect of intelligent recommendation is better.

Hybrid Chaos Particle Swarm Optimization algorithm for smart Cloud Service System based on optimization resource scheduling and allocation

<p>To enhance the smart Cloud Service System for diverse user requirements in 5G and other service networks, this study leverages resource utilization and multi-tenancy network slicing operation costs. Specifically, we propose a multi-tenancy network resource allocation strategy based on the Chaos Particle Swarm Optimization (CPSO) algorithm. In a multi-tenancy network (MTN), we lease the wireless spectrum resources of the infrastructure provider’s base station, construct access service slices as network slice services, and offer network access services to users. Introduce detailed formulation of the relationship between MTN and users, represented as a multi-master and multi-slave construct that defines the strategy space and profit function after MTN decision-making. Reverse induction is used to analyze the proposed model, and a distributed iterative algorithm is proposed to obtain the optimal throughput demand of users and the optimal slice cost of MTN. Simulation results demonstrate that the proposed strategy can effectively enhance resource utilization and user satisfaction while reducing energy consumption.</p>

Design of a low carbon economy model by carbon cycle optimization in supply chain

IntroductionConcerning economic globalization, enterprises must work with the cooperative partner to obtain more profits and overall planning of the supply chain has become a new focus for enterprise development. This paper studies the joint emission reduction of the supply chain in green low-carbon economy development and achieve joint emission and economic cost reduction through the optimization of carbon emission and economic dispatch.MethodsThe paper firstly uses the multi-agent model to complete the fullcycle modeling of carbon emission and economic cost; Secondly, the simulated annealing-adaptive chaos-particle swarm optimization (SAACPSO) method is used to optimize various parameters in the model to achieve emission and cost reductionResultsThe results show that after the optimization, the economic cost is reduced by 0.07 and the carbon emission is also reduced by 0.16; Finally, the practical test of the model is conducted with the collected data from the local company. The results show that the multi-objective optimization model of a joint enterprise supply chain is significantly better than single optimization in terms of emission reduction.DiscussionIt provides new ideas for a green economy and technical support for the global planning of supply chain integration.

Digital-Twin-Based Coordinated Optimal Control for Steel Continuous Casting Process

A digital-twin-model-based optimal control system is presented for the steel continuous casting process. The system is designed for the coordinated optimization and dynamic control of secondary cooling and final electromagnetic stirring (FEMS), and involves three related parts. Firstly, a three dimensional real-time heat transfer model is established as the digital twin of the heat transfer process of continuous casting; for high accuracy, it is calibrated offline and calibrated online using measurements of the surface temperatures and shell thicknesses (only offline). Secondly, according to metallurgical rules, cooling and stirring are optimized coordinatively, based on the established digital-twin model and chaos particle swarm optimization algorithm. Thirdly, cooling and stirring are further dynamically controlled for quality stability. Finally, the system is applied in a bloom caster with model errors ≤ ±10 °C and control errors ≤ ±4 °C, which reduces the macro-segregation over grade 1.5 from 11% to 3.3%.

Nonuniform height endwall fence optimization of a low-pressure turbine cascade

As the lift of turbine increases, the secondary flow in the endwall region seriously restricts improvement of the aerodynamic performance of low-pressure turbine. The particle swarm optimization is improved to pursue stronger optimization ability. An optimization platform is built that combines Bayesian optimization (BO), the eXtreme Gradient Boosting (XGBoost) algorithm and improved chaos particle swarm optimization. The test results show that the surrogate model optimization method proposed in this paper has more prominent application potential in the field of aerodynamic optimization. The above optimization method is used for the optimization design of the nonuniform height endwall fence (NHEF) of a high-lift low-pressure turbine cascade to achieve an efficient secondary flow control effect. The NURBS curve is used to realize the parameterization of the NHEF. The secondary flow control mechanism of traditional fences and NHEF has been compared and analysed in detail. The numerical results show that the NHEF can effectively prevent the cross migration of the boundary layer, and the fence vortex induced by the fence can suppress the development of the passage vortex. The total pressure loss at the outlet of the cascade installed NHEF is reduced by 14.82%. The secondary flow control effect of the NHEF is better than that of a traditional fence. The feature importance analysis results of input variables show that the pitchwise position of the fence has the most obvious influence on the loss of the cascade. In addition, based on the results of feature importance, a 53% length reduction fence is designed to reduce the extra weight brought by the fence. The design achieves the effect of comprehensively considering the weight and aerodynamic performance.

Fractional Order PID Optimal Control Method of Regional Load Frequency Containing Pumped Storage Plants

The pumped storage unit has good adjustment characteristics of a fast power response and convenient start and stop, which provides support for the safe and stable operation of the power system. To this end, this paper proposes a fractional order PID (FOPID) optimization control method for the regional load frequency of pumped-storage power plants. Specifically, based on IEEE standards, this paper established a single-region model of pumped storage. Then, a fractional order PID (FOPID) controller was designed, and the parameters of the controller were optimized via using the chaos particle swarm optimization (CPSO) algorithm. The effectiveness of the proposed method is verified by example simulation in the two-zone model of the pumped storage based on IEEE standards. The results of the example show that the proposed method exhibits stronger robustness and stability in the regional load frequency control.

Smartphone sensors‐based human activity recognition using feature selection and deep decision fusion

AbstractHuman activity recognition (HAR) with smartphone sensors is a significant research direction in human‐cyber‐physical systems. Aiming at the problem of feature redundancy and low recognition accuracy of HAR, this paper presents a novel system architecture comprising three parts: feature selection based on an oppositional and chaos particle swarm optimization (OCPSO) algorithm, multi‐input one‐dimensional convolutional neural network (MI‐1D‐CNN) utilizing time‐domain and frequency‐domain signals, and deep decision fusion (DDF) combining D‐S evidence theory and entropy. The proposed architecture is evaluated on the UCI HAR and WIDSM datasets. The results highlight that OCPSO performs better than particle swarm optimization (PSO) in feature selection, convergence speed, and recognition accuracy. Moreover, it is shown that for the MI‐1D‐CNN classifier, the frequency‐domain signals (95.96%) perform better than time‐domain signals (95.66%). In addition, this paper investigates the impact of the convolution layers, feature maps, filter sizes, and decision fusion methods on recognition accuracy. The results demonstrate that the DDF method (97.81%) outperforms single‐layer decision fusion in improving the recognition accuracy on the UCI HAR dataset.

Multi-project management of key chain of carbon fibre and composite materials based on chaos particle swarm optimisation

Multi-project management of key chain of carbon fibre and composite materials based on chaos particle swarm optimisation

Multi-project management of key chain of carbon fibre and composite materials based on chaos particle swarm optimisation

Multi-project management of key chain of carbon fibre and composite materials based on chaos particle swarm optimisation

A Gaussian Type-2 Fuzzy Programming Approach for Multicrowd Congestion-Relieved Evacuation Planning

Large-scale emergencies occur frequently around the world, causing serious casualties. Emergency evacuation is one of the top priorities after a disaster. Due to the uncertain and complex evacuation processes, evacuation plans obtained in a deterministic context may not meet the requirements of practical engineering applications. This research considers a multi-crowd congestion-relieved evacuation problem in Gaussian Type-2 fuzzy environments. A path-based one destination network flow (P-ODNF) model is developed for the problem formulation. To relieve the traffic congestion, a multi-point diversion evacuation strategy is employed in the model’s construction. The uncertainties associated with the evacuation process are expressed as Gaussian Type-2 fuzzy variables. A critical value-based defuzzification technique is adopted to handle the Type-2 fuzziness. Based on the credibility measure, the uncertain P-ODNF model is transformed into its deterministic counterpart. An efficient adaptive chaos particle swarm optimization algorithm (A-CPSO) is designed for model solving. Several numerical experiments are performed to demonstrate the proposed methodology. A sensitivity analysis is performed to illustrate the logical correctness of the proposed defuzzification process. The computational results show that A-CPSO is competitive in both its effectiveness and efficiency. The proposed methodology can coordinate multiple simultaneous evacuation processes to relieve congestion and improves the overall evacuation efficiency.

An Analog Circuit Fault Diagnosis Approach Based on Wavelet-based Fractal Analysis and Multiple Kernel SVM

Objective: In order to diagnose the analog circuit fault correctly, an analog circuit fault diagnosis approach on basis of wavelet-based fractal analysis and multiple kernel support vector machine (MKSVM) is presented in the paper. Methods: Time responses of the circuit under different faults are measured, and then wavelet-based fractal analysis is used to process the collected time responses for the purpose of generating features for the signals. Kernel principal component analysis (KPCA) is applied to reduce the features’ dimensionality. Afterwards, features are divided into training data and testing data. MKSVM with its multiple parameters optimized by chaos particle swarm optimization (CPSO) algorithm is utilized to construct an analog circuit fault diagnosis model based on the testing data. Results: The proposed analog diagnosis approach is revealed by a four opamp biquad high-pass filter fault diagnosis simulation. Conclusion: The approach outperforms other commonly used methods in the comparisons.

Frequency control of the islanded microgrid based on optimised model predictive control by PSO

In this paper, the amount of microgrid frequency deviation in the dynamic state can be reduced by improving the frequency controller and implementing a new method. The proposed controller is designed for a microgrid including renewable resources, and the proposed control strategy is such that the controller coefficients are adjusted and optimised at all times by the model predictive control (MPC). The weight parameters of the MPC controller have been optimised by the particle swarm optimisation (PSO) algorithm. The proposed controller is located in the secondary frequency control loop, and by applying a control signal to the sources, the frequency perturbations following the power changes in the microgrid are reduced. The simulation results show that the proposed controller performs better than the Ziegler–Nichols PI controller (PI-ZN) method, PI-based controllers that rely on fuzzy logic (PI-Fuzzy), the fractional-order proportional-integral-derivative (FOPID) controller that is based on chaos particle swarm optimisation (FOPID-CPSO) algorithm and the PID controllers based on CPSO algorithm (PID-CPSO). It has been able to effectively reduce the frequency fluctuations in terms of amplitude and number of oscillations is also more resistant to the uncertainty of microgrid parameters and shows better performance when changing parameters than other methods.

Research on dynamic stiffness with the high-order fractional derivative model for rubber bushing

A hybrid strategy for predicting the dynamic stiffness of rubber bushing is proposed in this study to analyze the axial amplitude-frequency dependence with the high-order fractional derivative model. Firstly, the finite element model (FEM) is constructed to calculate the force-displacement hysteresis curves under certain conditions. Secondly, a parametric model composed of the elastic element, the friction element, and the high-order fractional derivative viscoelastic element in parallel is proposed to describe the axial amplitude-frequency dependent behavior of rubber bushing. Then, based on the hysteresis curve obtained by the finite element analysis, the parameters of the high-order fractional derivative viscoelastic model are identified by using an adaptive chaos particle swarm optimization (ACPSO). Finally, the effectiveness of the proposed strategy is evaluated through simulations under sinusoidal excitation in the amplitude range of 0.1–2.0 mm and the frequency range of 0.1–25.0 Hz. The simulation results show that the parametric model can accurately estimate the dynamic stiffness under the condition of large amplitude and wide frequency. The hybrid strategy is practically useful for the dynamic stiffness estimate of rubber bushing, and reduces time and computational resources compared with the finite element method.

Fast direction of arrival estimation method for ultra‐high voltage converter valve insulation board partial discharge based on a sparse array

An insulation board is an important insulator used between damping capacitors in a converter valve. The potential creeping discharge phenomenon on the insulation board will affect the insulation between the damping capacitors and even the safe operation of the converter valve; therefore, a modified chaos particle swarm optimization multiple signal classification (MCSPO-MUSIC) localization algorithm based on a sparse array was proposed. The performance of the localization algorithm and the sparse array was analyzed by MATLAB simulation, and a test platform was established to detect the insulation board discharge position localization. The simulation results showed that the calculation time of this algorithm is about 1.5 s, which is an order of magnitude less than traditional MUSIC algorithm, and it is found that when the sparsity of the 4 × 4 array is 4 (the sparse array elements are 5, 9, 14 and 15), the localization accuracy remains high. Ten groups of experimental data were put into the MCSPO-MUSIC algorithm; the root mean square errors (RMSE) of the localization errors are 1.91° (non-sparse array) and 3.12° (sparse array), respectively. Finally, the blind source separation algorithm was used to remove the field noise, which verifies the algorithm and sparse concept accurate and economical in practical application.

The multi-objective inspection path-planning in radioactive environment based on an improved ant colony optimization algorithm

More and more occupational workers work in radioactive environment. Although measures are taken to keep the radiation dose in a safe range, the workers will suffer more radiation during the overhauling of nuclear power plants. The dose they suffer during the overhauling of nuclear power plants account for 80% of the total annual dose so it is necessarily to plan a reasonable inspection path for them according to the safety principle of as low as reasonably achievable (ALARA). An improved ant colony optimization (IACO) algorithm is proposed to solve the multi-objective inspection path-planning problem in radioactive environment. To improve the performance of the algorithm, we not only combine ant colony optimization (ACO) algorithm and chaos optimization algorithm, but also introduce pheromone differentiated update strategy and local search optimization strategy. Additionally, 5 experimental simulation cases are conducted and the results are compared to those from particle swarm optimization (PSO) algorithm, chaos particle swarm optimization (CPSO) algorithm and tradition ACO algorithm. In the first three cases, the probability of IACO algorithm finding the optimal path is obviously greater than that of PSO algorithm and CPSO algorithm. IACO algorithm is more efficient and stable. By analyzing an inspection path-planning case with 35 target positions and a case with 44 target positions in a more complex radioactive environment, IACO algorithm could find the path with less effective dose that ACO algorithm cannot find. Therefore, the effectiveness and validity of IACO to solve multi-objective inspection path-planning problem in radioactive environment are verified by experimental simulations and it can help workers reduce radiation exposure.

Hybrid Scheduling for Multi-Equipment at U-Shape Trafficked Automated Terminal Based on Chaos Particle Swarm Optimization

Aimed to improve the efficiency of port operations, Shanghai Zhenhua Heavy Industries Co., Ltd. (ZPMC) proposed a new U-shape trafficked automated terminal. The new U-shape trafficked automated terminal brings a new hybrid scheduling problem. A hybrid scheduling model for yard crane (YC), AGV and ET in the U-shape trafficked automated terminal yard is established to solve the problem. The AGV and ET yard lanes are assumed to be one-way lane. Take the YC, AGV and ET scheduling results (the container transportation sequences) as variables and the minimization of the maximum completion time as the objective function. A scheduling model architecture with hierarchical abstraction of scheduling objects is proposed to refine the problem. The total completion time is solved based on a static and dynamic mixed scheduling strategy. A chaotic particle swarm optimization algorithm with speed control (CCPSO) is proposed, which include a chaotic particle strategy, a particle iterative speed control strategy, and a particle mapping space for hybrid scheduling. The presented model and algorithm were applied to experiments with different numbers of containers and AGVs. The parameters of simulation part refer to Qinzhou Port. The simulation results show that CCPSO can obtain a near-optimal solution in a shorter time and find a better solution when the solution time is sufficient, comparing with the traditional particle swarm optimization algorithm, the adaptive particle swarm optimization algorithm and the random position particle swarm optimization algorithm.

Research on demand management of hybrid energy storage system in industrial park based on variational mode decomposition and Wigner–Ville distribution

In the industrial park microgrids, the curves of industrial load and photovoltaic output are unstable and unadjustable. The implementation of energy storage system (ESS) has proven successful in tackling these issues. Compared with the single-type battery energy storage (SBES), the hybrid energy storage system (HESS) is composed by energy-type energy storage and power-type energy storage, which can effectively improve the controllability and schedulability of renewable energy generation. The governments of most countries have implemented a two-part time-of-use tariff mechanism for industrial users. In this context, the paper proposes a solution for the reasonable low-cost configuration and operation of HESS in industrial parks. First of all, this paper creatively combines the Variational Mode Decomposition and Wigner–Ville Distribution (VMD-WVD) algorithm to break down the net load of the industrial park system, and installs the super-capacitors and lithium batteries to smooth the high frequency and low frequency components of the original net load. Secondly, this paper establishes a two-stage monthly and day-ahead optimization model, which is solved by the Chaos Particle Swarm Optimization (CPSO) algorithm. The monthly HESS capacity optimization configuration model is to minimize the total installation cost. And the day-ahead scheduling model maximizes the net income of life cycle, which further improves the user's peak-to-valley arbitrage. The results show that, compared with SBES, the installation of HESS can better achieve peak shaving and reduce grid connection fluctuations. Besides, the installation of HESS can greatly reduce the electricity cost and the basic electricity cost of industrial parks, so that it can save industrial users' production costs.

Motion Reliability Analysis of Unlocking Trigger Device Based on CPSO-BR-BP Neural Network with Uncertain Parameters

Aiming at overcoming the problem that the mechanism function of the unlocking trigger device is difficult to obtain and the corresponding reliability analysis cannot be performed, a motion reliability analysis method based on the CPSO-BR-BP neural network proxy model is proposed. Firstly, the particle swarm algorithm is optimized through the chaotic sequence, and the back-propagation (BP) neural network is optimized using Chaos Particle Swarm Optimization (CPSO) and Bayesian Regularization (BR) algorithm. The CPSO-BR-BP neural network proxy model is established, and the reliability of shape memory alloys (SMA) wire unlocking based on the structural function is calculated. Moreover, according to the structural function of the separation process, the motion reliability based on the proxy model and the improved membership function is calculated. Finally, a series reliability model is established based on the unlocking process and the separation process to calculate the reliability of the whole machine. The reliability of the unlocking trigger device is analyzed by the proposed method. Results show that the proposed method is computationally efficient with the calculated reliability of 0.9987.