Annealing Particle Swarm Optimization Algorithm Research Articles

Vibration control by using active electromagnetic shunt damper

It is well-known that the traditional electromagnetic shunt damping (EMSD) techniques are limited by the damping force of electronic components and require a negative resistance (NR) shunt circuit to enhance performance. However, the NR shunt circuit could lead to the EMSD system being unstable. Addressing this, this study proposes an advanced control system that employs active control technology combined with EMSD for vibration control. We first developed a dimensionless mathematical model of the control system, which was then finely tuned using an adaptive simulated annealing particle swarm optimization algorithm. Subsequently, the relationship between control gain and optimal shunt circuit parameters was predicted using a BP neural network. Finally, the proposed Active-EMSD (AEMSD) was experimentally verified. The experimental results demonstrate that the proposed AEMSD not only surpasses traditional thresholds but also excels in isolating low-frequency vibrations. Compared to traditional EMSD, the proposed AEMSD showed improved effectiveness.

Site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands

Aiming at the problems of high investment and low efficiency in the planning and construction of electric vehicle (EV) charging stations in cities, an optimization model for site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands is proposed. Firstly, based on the travel chain theory and the Origin-Destination (OD) matrix, the travel characteristics of EVs are studied, and the spatial and temporal distribution prediction model of EV charging load is established through the dynamic Dijkstra algorithm combined with the Monte Carlo method. Secondly, a site selection model for the charging station is established which takes the minimum annualized cost of the charging station operator and the annualized economic loss of the EV users as the goal. At the same time, the weighted Voronoi diagram and Adaptive Simulated Annealing Particle Swarm Optimization algorithm (ASPSO) are adopted to determine the optimal number/site selection and service scope of charging stations. Finally, an uncertain scenario set is introduced into the capacity determination model to describe the uncertainty of the users’ dynamic charging demands, and the robust optimization theory is utilized to solve the capacity of the charging station. A case study is carried out for the EV charging station planning problem in some urban areas of a northern city, and the validity of the model is verified.

Novel Active Disturbance Rejection‐Based Sliding‐Mode Control for Permanent Magnet Linear Synchronous Motor Drives

Active disturbance rejection control (ADRC) has been widely used in the permanent magnet linear synchronous motor (PMLSM) drive system for its strong anti‐disturbance ability and fast response speed. However, a large number of adjustment parameters add difficulties to its application. To solve this problem, a novel sliding mode‐based active disturbance rejection control (SM‐ADRC) with autotuned parameters is proposed in this paper. In this control strategy, the SM is incorporated with extended state observer (ESO) and nonlinear state error feedback (NLSEF) to reduce the number of adjustment parameters. The simulated annealing particle swarm optimization (SAPSO) algorithm is carried out to achieve the optimal configuration and realize parameter self‐tuning. The experimental results of the PMLSM speed servo system demonstrate the proposed control method has better suppression of chattering effect, disturbance rejection ability and fast dynamic response. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Disassembly Sequence Optimization of Lithium Battery Pack Based on Priority Constraint Graph

With the rapid development of the new energy vehicle power industry, the power system of automobiles is about to usher in a wave of retirement. At present, most of power lithium battery packs are disassembled violently, which lack a reasonable disassembly order. This causes a waste of time and cost during the disassembly process. Some harmful parts will also pollute the environment. In order to improve the disassembly efficiency of retired lithium-ion battery packs, the disassembly sequence planning problem (DSP) based on priority constraint graph is studied in this paper. After analyzing disassembly characteristics, the priority constraint graph is constructed as the constraint matrix. Considering the disassembly efficiency, a mathematical model is established to optimize the direction and the number of tool changes. An improved Simulated Annealing Particle Swarm Optimization (SA-PSO) algorithm is designed, which is to update the disassembly sequence by the population update method. The particles are assisted to jump out of the local optimum by the Metropolis criterion of SA algorithm. An adaptive method is proposed for the PSO inertia weight ω. In order to verify the validity of the model and algorithm, an example is introduced to compare SA-PSO with PSO and SA algorithm. The results show that the improved algorithm performs well in the evaluation index of fitness. The convergence rate is fast and more stable.

Combination optimization of green energy supply in data center based on simulated annealing particle swarm optimization algorithm

At present, the high energy consumption of data centers based on grid power supply not only brings huge direct cost of electricity, but also indirectly produces a lot of greenhouse gases, which affects the natural environment. Academia and industry are beginning to introduce clean renewable energy sources such as wind and solar power into data centers to reduce operating costs and environmental damage by building new green data centers. To solve this problem, this study considers the use of waste heat for refrigeration while taking natural gas power generation into account, and introduces wind energy as a green energy source. On the premise of considering the response level of data centers, the two resources are combined and deployed to improve resource utilization and reduce energy consumption costs. Aiming at the instability of wind power generation, a particle swarm energy scheduling optimization algorithm based on simulated annealing algorithm was proposed by combining simulated annealing algorithm and particle swarm optimization algorithm. The research shows that, considering the response level of data centers, the use of natural gas and wind energy as the main energy supply can effectively reduce the overall energy consumption of data centers.

An Improved Adaptive Simulated Annealing Particle Swarm Optimization Algorithm for ARAIM Availability

Civil aviation transportation equipment is more convenient and faster than other transportation tools and is an essential part of intelligent transportation. It is significant to study the reliability of positioning information and enhance traffic safety. Advanced receiver autonomous integrity monitoring (ARAIM) can provide vertical guidance during the different navigation stages in civil aviation fields. The traditional multiple hypothesis solution separation (MHSS) algorithm distributes the probability of hazardous misleading information (PHMI) and probability of false alarm (PFA) uniformly over all visible satellites resulting in reduced global availability of ARAIM. Aiming at this problem, we proposed an adaptive simulated annealing particle swarm optimization (ASAPSO) algorithm to redistribute integrity and continuity risks and establish a protection level optimization model. Based on the real BeiDou navigation satellite system/global positioning system (BDS/GPS) data, the experimental results show that the optimized algorithm can reduce the vertical protection level (VPL), and the ARAIM global availability of BDS/GPS is improved by 1.73%∼2.73%. The optimized algorithm can improve the availability of integrity monitoring at different stages of the navigation system and provide a basis for ensuring the reliability of the positioning results.

Multi-index dominant grouping of rock mass discontinuities based on the combined weighting method: A case study for the Huayang tunnel

The properties of discontinuities are determined by their occurrence, trace length, aperture, roughness, and the degree of particle fill; therefore, a multi-index dominant grouping method for rock mass discontinuities based on the combined weighting method was proposed. Firstly, the combined weighting method was used to calculate the weighted value of each index participating in the similarity measurement, and a reasonable elimination of outlier discontinuities is achieved based on the principle of possibility clustering. The adaptive chaotic annealing particle swarm optimization algorithm was then used to search the global optimal initial clustering centers of the fuzzy C-means clustering algorithm. The objective function was established by minimizing the weighted sum of the distance between the discontinuities to be grouped, and the clustering centers to complete the multi-index dominant grouping of rock mass discontinuities. 230 artificial discontinuities with outliers were grouped and compared with other methods, which indicated that this method has higher grouping accuracy. The proposed method was applied to group the measured discontinuities of the Huayang tunnel in Chongqing City, China. Based on the grouping results, a discrete element model for tunnel excavation was established, and the construction was guided according to the calculated results from the model. The engineering applicability of the method was further verified.

Multi-objective based demand response strategy optimization considering differential demand on reliability of power system

The reliability of power system will vary with different demand response strategies, and it is significant to make a decision on demand response for satisfying differential reliability demand of power system. First, a peak-valley period partition model with an objective of maximizing silhouette coefficient was established based on the fuzzy clustering and step-wise iteration technology. Second, this paper proposed a multi-objective time of use electricity price optimization model considering the interests of both supply and demand sides, as well as reliability demand, and a multi-objective simulated annealing particle swarm optimization algorithm was developed to obtain solution. Third, the Pareto front curve of the dual-objective function was fitted by using a third-order Hermite interpolation algorithm, and the time of use prices corresponding to the reliability demand was obtained by using the back propagation neural network algorithm. Finally, the proposed model and algorithm were verified by the RBTS system, and the results indicate good rationality and effectiveness of the proposed method.

Optimization design of stepped rotor type bearingless switched reluctance motor

In order to solve the problems of small starting torque and large torque ripple existing in traditional 12/14 hybrid stator pole bearingless switched reluctance motor (BSRM), a stepped rotor type BSRM is proposed. The proposed motor not only inherits the advantages of traditional 12/14 hybrid stator pole BSRM, but also can start at any rotor position and has relatively small torque ripple. To obtain better electromagnetic characteristics, simulated annealing particle swarm optimization (SAPSO) algorithm is proposed to optimize the structure of the stepped rotor type BSRM. Then, the torque, inductance and suspension force characteristics of the stepped rotor type BSRM before and after optimization are analyzed and compared. Finally, the effectiveness of the proposed optimization algorithm is verified by analysis results.

Bi-objective optimization framework for prefabricated construction service combination selection using genetic simulated annealing algorithm

PurposeThe purpose of this paper is to develop a bi-objective optimization framework to select prefabricated construction service composition. An improved algorithm-genetic simulated annealing algorithm (GSA) is employed to demonstrate the application of the framework.Design/methodology/approachThe weighted aggregate multi-dimensional collaborative relationship is used to quantitatively evaluate the synergistic effect. The quality of service is measured using the same method. The research proposed a service combination selection framework of prefabricated construction that comprehensively considers the quality of service and synergistic effect. The framework is demonstrated by using a GSA that can accept poor solutions with a certain probability. Furthermore, GSA is compared with the genetic algorithm (GA), simulated annealing algorithm (SA) and particle swarm optimization algorithm (PSO) to validate the performance.FindingsThe results indicated that GSA has the largest optimal fitness value and synergistic effect compared with other algorithms, and the convergence time and convergence iteration of the improved algorithm are generally at a low level.Originality/valueThe contribution of this study is that the proposed framework enables project managers to clarify the interactions of the prefabricated construction process and provides guidance for project collaborative management. In addition, GSA helps to improve the probability of successful collaboration between potential partners, therefore enhancing client satisfaction.

Optimal Allocation of Distributed Generators in Active Distribution Network considering TOU Price

As the distributed generators (DGs) are connected to active distribution network (ADN), it strengthens the communication interest between the customs and DGs and can facilitate energy integration. This article proposes hybrid pricing for DG configuration by taking advantage of fixed pricing and dynamic pricing. The time sequence scenario of wind-photovoltaic-load power can be got by k-means, which can balance the calculation burden with multiple scenarios. The planning model is built as the optimal objective for minimal investment in operation and maintenance of DGs, reduction of network loss, and decrease of voltage deviation. An improved simulated annealing particle swarm optimization algorithm is also proposed by refining the initialized population based on the niche fitness, introducing inertia weight with chaotic disturbance and accelerating local search with learning factor of dynamic parameter. The effectiveness and rationality of the proposed methodology are verified by simulation in the IEEE 69-bus system.

Inverse kinematics analysis of rock drilling manipulator based on improved SA-PSO

In the inverse solution of the rock drilling manipulator, the particle swarm optimization algorithm has been widely used, but due to the complex structure of the manipulator, its disadvantage is that the convergence speed is slow and it is easy to fall into the local optimal solution. Therefore, the adaptive annealing coefficient is introduced into the simulated annealing particle swarm optimization algorithm and improved; the overall search and convergence are improved. Through the simulation experiment of the PSO algorithm, it is proved that this method is better than the conventional PSO method in terms of convergence speed and precision, and can better solve the problem of slow convergence speed and falling into local optimum.

A migration method for service function chain based on failure prediction

With the deep application of network technologies in different industries, the demand for network services is becoming more and more diversified. Network operation and maintenance are facing severe challenges, which can be solved by network function virtualization (NFV). NFV technology provides services for users through deploying service function chain (SFC) on servers and substrate links. However, once a server fails, the services it hosts will be affected or even interrupted. Therefore, it is very important to predict failures and migrate SFCs in advance according to predicted results. In this paper, we propose a failure prediction method based on the improved long short-term memory neural network (PMILSTM), which employs LSTM to predict failures. To further improve prediction accuracy, the simulated annealing particle swarm optimization algorithm is adopted to optimize the number of neurons in each long short-term memory layer and the time window length. A server may host multiple SFCs. When a server fails, in order to reduce the impact on users, it is necessary to simultaneously migrate all the SFCs hosted by the server. We propose an improved sparrow search algorithm (ISSA) and a service function chain migration method based on the ISSA (MMISSA). The ISSA introduces tent chaos, opposition-based learning, dynamic weight factor, and mutation operation into SSA to achieve the better global optimization ability. The MMISSA method adopts the ISSA to migrate SFCs so that it can simultaneously search for migration servers for all the virtual network functions deployed on a soon-to-fail server. The better global optimization ability of the ISSA enables better migration results. Therefore, the migration success ratio is improved. Moreover, the fitness function simultaneously considers the average migration cost and migration time. As a result, the MMISSA method effectively reduces the migration cost and migration time.

An improved algorithm for resolving overlapping peaks in ion mobility spectrometry and its application to the separation of glycan isomers.

Despite the popularity of ion mobility spectrometry (IMS) for glycan analysis, its limited structural resolution hinders the effective separation of many glycan isomers. This leads to the overlap of IMS peaks, consequently impacting the accurate identification of glycan compositions. To this end, an improved algorithm, namely second-order differentiation combined with a simulated annealing particle swarm optimization algorithm based on sine adaptive weights (DWSA-PSO), was proposed for the separation of overlapping IMS peaks formed by glycan isomers. DWSA-PSO first performed second-order differentiation to automatically determine the number of components in overlapping peaks and exclude impossible single-peak combinations. It then introduced sinusoidal adaptive weights and a simulated annealing mechanism to improve the algorithm's search capability and global optimization performance, thereby enabling accurate and efficient separation of individual peaks. To evaluate the performance of DWSA-PSO and its application to the separation of glycan isomers, multiple sets of overlapping peaks with different degrees of overlap were simulated, and various types of multi-component overlapping peaks were formed using six disaccharide and four trisaccharide isomers. The experimental results consistently demonstrated that the DWSA-PSO algorithm outperformed both the improved particle swarm optimization (IPSO) algorithm and the dynamic inertia weight particle swarm optimization (DIWPSO) algorithm in terms of separation accuracy, running time, and fitness values. In addition, the DWSA-PSO algorithm was successfully applied to the separation of glycan isomers in malt milk beverage. All these results reveal the capability of the DWSA-PSO algorithm to facilitate the accurate identification of glycan isomers.

Fast detection of volatile fatty acids in biogas slurry using NIR spectroscopy combined with feature wavelength selection.

To analyze the state of anaerobic digestion (AD), fast detection models of volatile fatty acids (VFAs) were constructed using near-infrared transmission spectroscopy combined with partial least squares regression to measure concentrations of the acetic acid (AA), propionic acid (PA) and total acid (TA) in biogas slurry. CARS-SA-BPSO algorithm was proposed based on competitive adaptive reweighted sampling (CARS) and simulated annealing binary particle swarm optimization algorithm (SA-BPSO) for selecting feature wavelengths of the AA, PA and TA. Regression models were established with the determination coefficient of prediction (Rp2) of 0.989, root mean squared error of prediction (RMSEP) of 0.111 and residual predictive deviation (RPD) of 9.706 for AA; Rp2 of 0.932, RMSEP of 0.116 and RPD of 3.799 for PA; Rp2 of 0.895, RMSEP of 0.689 and RPD of 3.676 for TA. It is sufficient to meet the fast detection needs of the AA and PA concentrations in biogas slurry, and basically meet the measuring demand of the TA concentration. CARS-SA-BPSO effectively improves the performance of the calibration model using sensitive wavelength selections, which provides theoretical support for establishing the spectral quantitative regression model to meet the requirements of practical application.

Location and Capacity Determination Method of Electric Vehicle Charging Station Based on Simulated Annealing Immune Particle Swarm Optimization

As the number of electric vehicles (EVs) continues to grow and the demand for charging infrastructure is also increasing, how to improve the charging infrastructure has become a bottleneck restricting the development of EVs. In other words, reasonably planning the location and capacity of charging stations is important for development of the EV industry and the safe and stable operation of the power system. Considering the construction and maintenance of the charging station, the distribution network loss of the charging station, and the economic loss on the user side of the EV, this paper takes the node and capacity of charging station planning as control variables and the minimum cost of system comprehensive planning as objective function, and thus proposes a location and capacity planning model for the EV charging station. Based on the problems of low efficiency and insufficient global optimization ability of the current algorithm, the simulated annealing immune particle swarm optimization algorithm (SA-IPSO) is adopted in this paper. The simulated annealing algorithm is used in the global update of the particle swarm optimization (PSO), and the immune mechanism is introduced to participate in the iterative update of the particles, so as to improve the speed and efficiency of PSO. Voronoi diagram is used to divide service area of the charging station, and a joint solution process of Voronoi diagram and SA-IPSO is proposed. By example analysis, the results show that the optimal solution corresponding to the optimisation method proposed in this paper has a low overall cost, while the average charging waiting time is only 1.8 min and the charging pile utilisation rate is 75.5%. The simulation comparison verifies that the improved algorithm improves the operational efficiency by 18.1% and basically does not fall into local convergence.

Corrosion life prediction of glass fiber reinforced plastics by optimized BP neural network

• The annealing particle swarm optimization algorithm is improved. The improved algorithm(im SAPSO) has stronger global search ability. • im SAPSO is used to replace the traditional neural network training method(most of these algorithms are based on gradient descent algorithm, so there will inevitably be local extremum problems), the effect is better. • The optimized neural network is used to predict the corrosion life prediction of glass fiber reinforced plastics. The output results of the network are in good agreement with the measured results, which proves the reliability of the algorithm. The retention rate of bending strength (RRBS) of glass fiber reinforced plastics (GFRP) in service under corrosive conditions is responsible for structural safety. However, it is very difficult to measure the RRBS by conventional measurement methods. In order to predict the service life of GFRP under corrosive conditions, a back-propagation (BP) neural network was establised. A series of experiments were carried out, considering three factors including temperature, time and corrosive medium concentration. 60 groups of RRBS were obtained and used as samples for neural network training. The network was optimized by improved simulated annealing particle swarm optimization algorithm (imSAPSO). The optimized network was then used to predict the other six groups of test data. The results show that the predicted values compare well to the measured, with maximum relative error of −0.615417% and minimum 0.015934%, and standard deviation of 0.00254567.

Simulated Annealing Particle Swarm Optimization for a Dual-Input Broadband GaN Doherty Like Load-Modulated Balance Amplifier Design

In this brief, an automatic power amplifier (PA) design system based on simulated annealing particle swarm optimization (SA-PSO) algorithm is proposed to perform multi-objective optimization in designing broadband Doherty like load-modulated balanced amplifier (LMBA). Specifically, by combining the programming environment with commercial electronic design automation (EDA) software, the optimization process can be implemented automatically. Then, complex and rigorous impedance trajectory aligning at different power levels can be easily obtained using the proposed optimization algorithm, demonstrating the automatic PA design system’s superiority over the built-in optimizer in commercial EDA software for designing broadband Doherty like LMBA. A 2.5-3.5 GHz dual-input Doherty like LMBA is simulated and measured, showing a saturated output power of 45.4-48.2 dBm, a saturation drain efficiency of 60.7% - 69.7%, a 6 dB power back-off DE of 59.2% - 65.8%, and a 9 dB power back-off DE of 52.7% - 66.9%.

Research on Bridge Damage Identification Based on WPE-MDS and HTF-SAPSO

In recent decades, structural damage identification based on the wavelet analysis method has been widely developed, but it is still confronted with many difficulties, such as large decomposition error and complex data. In order to overcome the shortcomings of analysis based on wavelet, the wavelet packet analysis method is adopted to decompose the acceleration data into wavelet packets, and the frequency band energy value after wavelet packet decomposition (WPE) is taken as the different dimensions of the Mahalanobis distance squared (MDS) in this study, where the MDS value of the same element between different samples is calculated, and the mean value of 30 groups of MDS values for each element is processed. The change rate between the MDS value of the element that exceeds the MDS value in the healthy state and the MDS mean value in the healthy state as the objective function. The combination of weight coefficient and hyperbolic tangent function is used to improve the simulated annealing particle swarm optimization (SAPSO) algorithm, and the improved hyperbolic tangent function-simulated annealing particle swarm optimization (HTF-SAPSO) is used to iteratively calculate the damage severity. The numerical simulation and vibration testing of a steel beam are conducted to verify the identification performance of damage location and the analysis of damage severity by this method, respectively. The numerical model of the experimental I-beam is established based on the MATLAB modeling platform, and the different damage cases are utilized to illustrate the correctness of this study. The different proportions of noise effects are adopted to the numerical simulation analysis, where the correlations between noise effects and MDS value and damage severity are analyzed. In the numerical simulation, although the MDS value increases to different degrees with the increase of the noise ratio, the damage identification result of the damaged element remains mostly constant, which indicates that the influence is negligible. In conclusion, it is feasible to construct the damage index via the combination of WPE and MDS values, the damage location can be judged from whether the MDS value of the element exceeds the threshold, and the HTF-SAPSO algorithm is more efficient and accurate to be adopted in the quantification of the damage severity.

Short-Term Power Load Forecasting Based on SAPSO-CNN-LSTM Model considering Autocorrelated Errors

Accurate power load forecasting is essential for power grid operation and dispatching. To further improve the accuracy of power load forecasting, this study proposes a new power load forecasting method. Firstly, correlation coefficients of influential variables are calculated for feature selection. Secondly, the form of input data is changed to adjust for autocorrelated errors. Thirdly, data features are extracted by convolutional neural networks (CNN) to construct feature vectors. Finally, the feature vectors are input into long short-term memory (LSTM) network for training to obtain prediction results. Moreover, for solving the problem that network hyperparameters are difficult to set, the simulated annealing particle swarm optimization (SAPSO) algorithm is used to optimize the hyperparameters. Experiments show that the prediction accuracy of the proposed model is higher compared with LSTM, CNN-LSTM, and other models.