Mutation Particle Swarm Optimization Research Articles

Mining Plan Optimization of Multi-Metal Underground Mine Based on Adaptive Hybrid Mutation PSO Algorithm

Mine extraction planning has a far-reaching impact on the production management and overall economic efficiency of the mining enterprise. The traditional method of preparing underground mine production planning is complicated and tedious, and reaching the optimum calculation results is difficult. Firstly, the theory and method of multi-objective optimization are used to establish a multi-objective planning model with the objective of the best economic efficiency, grade, and ore quantity, taking into account the constraints of ore grade fluctuation, ore output from the mine, production capacity of mining enterprises, and mineral resources utilization. Second, an improved particle swarm algorithm is applied to solve the model, a nonlinear dynamic decreasing weight strategy is proposed for the inertia weights, the variation probability of each generation of particles is dynamically adjusted by the aggregation degree, and this variation probability is used to perform a mixed Gaussian and Cauchy mutation for the global optimal position and an adaptive wavelet variation for the worst individual optimal position. This improved strategy can greatly increase the diversity of the population, improve the global convergence speed of the algorithm, and avoid the premature convergence of the solution. Finally, taking a large polymetallic underground mine in China as a case, the example calculation proves that the algorithm solution result is 10.98% higher than the mine plan index in terms of ore volume and 41.88% higher in terms of economic efficiency, the algorithm solution speed is 29.25% higher, and the model and optimization algorithm meet the requirements of a mining industry extraction production plan, which can effectively optimize the mine’s extraction plan and provide a basis for mine operation decisions.

Energy Consumption in Cache-Enabled D2D Networks Based on Matérn Cluster Process

Device-to-device (D2D) communications have significant advantages such as short range and low energy consumption. Caching at mobile user devices by leveraging D2D communications relieves the heavy load on backhaul links and enhances users’ quality of service. Therefore, in a cache-enabled clustered D2D network, we analyze the probability of successful transmission and propose a jointly optimizing bandwidth partitioning and caching policy strategy to solve the average energy consumption minimization problem. To simultaneously have the clustered and bounded characteristics of D2D communication in real hotspots areas, we use Matérn cluster process (MCP) to model the cache-enabled clustered D2D network. For a given D2D receiver (DR), we derive the successful transmission probability of getting its requested content. Moreover, leveraging the analysis of successful transmission probability, a joint bandwidth partitioning and caching policy optimization problem is formulated to reduce average energy consumption. Considering the complexity of directly solving the optimization problem, two sub-problems are decoupled and solved iteratively. The optimized solutions of bandwidth partitioning and caching policy sub-problems are obtained by taking the derivative and adaptive mutation particle swarm optimization (AMPSO) algorithm, respectively. The simulations show the influence of the related parameters on system performance and illustrate that the strategy to jointly optimize bandwidth partitioning and caching policy can significantly reduce energy consumption.

Construction of Artificial Intelligence Application Model for Supply Chain Financial Risk Assessment

An artificial intelligence integrated application model of supply chain financial risk assessment is constructed. Based on the financial data and supply chain data of listed companies in China’s new energy electric vehicle industry, the supply chain financial credit risk evaluation index system is constructed. The data samples are preprocessed by PCA as the input data of the support vector machine, which effectively solves the problem of high-dimensional data in supply chain finance. By improving the inertia weight of particle swarm optimization and introducing mutation operation, a dynamic mutation particle swarm optimization algorithm is proposed to avoid the problem of particles falling into a local minimum in the process of optimization. Finally, the improved optimization algorithm is used to optimize the parameters of SVM and input AdaBoost integration as a weak classifier to build an integrated model with good performance in many aspects. The model has been successfully applied to the credit risk assessment of China’s new energy vehicle supply chain finance. The comparison with other models shows that the constructed model has certain advantages in performance.

Dynamic analysis and optimization of a wind turbine tower subject to wind and earthquake loads

SummaryA dynamic model of a wind turbine tower is established to investigate its dynamic responses under wind and earthquake loads. Then a generalized global spatial discretization method is used to solve the problem. Modal analysis of the wind turbine tower is conducted using the dynamic model and the results are verified by shell models and beam models established in the commercial FE software ANSYS and LS‐DYNA. Transient vibration displacements, as well as normal and shear stress distributions of the wind turbine tower subject to different levels of pulsing wind loads, are calculated based on the dynamic model. Quasi‐static motion and transient motion assumptions are applied to evaluate the strengths of the tower, respectively. Influences of different earthquake ground motions on vibration amplitudes of the tower top are examined. Then, a hybrid mutation particle swarm optimization algorithm is used to perform design optimization on the tower body thickness for vibration reduction with its weights and strength being constrained. The penalty function strategy is used to deal with the constraints on body weight and stress level. Results demonstrate that the comprehensive performances of the wind turbine tower especially the tower top vibration have been greatly reduced after optimization.

Research on comprehensive recovery of liner schedule and container flow with hard time windows constraints

COVID-19 has had a huge impact on the global container market. Many liner companies have adopted a blank sailing for some voyages to adjust capacity, and vessel schedule reliability continues to be sluggish. From the perspective of the container liner company, this paper studies the integrated recovery of liner schedule and container flow under the background of suspension of shipping service. With the goal of minimizing the total cost of the liner company, the hard time window constraints of the container flow on the suspended routes are set to construct the integrated recovery problem.The increased carbon emission cost during the restoration of the container flow is taken into account.A mixed integer nonlinear programming model is established, and the adaptive mutation particle swarm optimization (AMPSO) is used to solve the model. The results show that the total cost of the model is reduced by 10.66% compared with the total cost of the shipping schedule recovery model that did not consider the recovery of container flow.

Combined heat and power plants integrated with steam turbine renovations: Optimal dispatch for maximizing the consumption of renewable energy

Steam turbine renovations, including the high back-pressure (HBP) renovation and the low pressure turbine zero power output (LZPO) renovation, can enlarge the loads supply range of one single combined heat and power (CHP) unit. Focusing on renewable energy consumption and coal saving, the problem regarding the optimal dispatch of a case CHP plant with renovated CHP units is addressed in this study. The energy and exergy analysis models are developed to evaluate the performance of various types of CHP units. The models of the minimum power load and the coal consumption for the whole plant are established based on the unit combinations in case 1 to case 4. The adaptive mutation particle swarm optimization (AMPSO) algorithm is adopted to seek the optimal value and the corresponding loads distributions. Results show that the maximum energy efficiency of the HBP renovated unit and LZPO renovated unit are 87.1% and 88.2%. Moreover, the maximum exergy efficiency of the HBP renovated unit and LZPO renovated unit are 41.3% and 32.1%. In brief, the HBP renovated unit has higher exergy efficiency and lower energy efficiency than LPZO renovated unit. In terms of optimization methods, AMPSO has better performance than PSO in terms of convergence speed and getting rid of local optimum solutions. After reasonably redistributing the loads of the case plant integrating the HBP and LZPO renovations, approximately 0.191 billion kWh more renewable energy would be consumed, and about 0.069 million tons of coal and 0.181 million tons of CO2 emissions could be saved during the heating season. Optimization results show that the LZPO renovations are more effective in consuming renewable energy, and the HBP renovations can be considered as the preferable coal-saving option. This study provides a method for optimizing the operation of renovated CHP plants. In addition, for the un-renovated CHP plant such as case 1, the renovation patterns in case 2 to case 4 provide some reference once the steam turbine renovations to be considered.

An Integrated AMPSO-CLSTM Model for Photovoltaic Power Generation Prediction

To address the challenges of volatile and intermittent nature in photovoltaic power (PV) generation forecasting, a new convolutional long short-term memory network (CLSTM) prediction model optimized by adaptive mutation particle swarm optimization (AMPSO) is proposed. In this model, the local sensing ability of the convolutional kernels in the CNN is used to extract high-dimensional features from the variable influential factors of PV power generation, and a mapping between time series data and PV is established by the memory ability of the gate control unit in LSTM. The AMPSO algorithm is introduced to optimize the network structure and weights of CLSTM simultaneously. The performance of the model is verified by two different two data sets. The results show that compared with that of the CLSTM, Auto-LSTM, LSTM and recurrent neural network models, the root mean square error (RMSE) of the AMPSO-CLSTM model decreases by 1.92–6.53% and 6.23–31.10%, the mean absolute error (MAE) decreases by 6.92–16.87% and 11.71–48.84%, and the mean absolute percentage error (MAPE) decreases by 13.24–31.75% and 12.22–49.00%, respectively. Compared with those of the CLSTM model, the number of channels in the convolutional layer of the AMPSO-CLSTM is reduced by 51.76–71.09% and 61.72–86.72%, respectively, and the number of hidden neurons in LSTM is reduced by 32–60% and 53–84%, respectively.

Detection of Wheel Diameter Difference of Railway Wagon by ACMD-FBD and Optimized MKELM

As a common defect of heavy-haul railway wagons, the wheel diameter difference (WDD) will deteriorate wheel/rail dynamic interaction, which severely threatens the running stability and safety of the wagons. Accordingly, it is of great importance to detect the WDD forms of wagons and take appropriate measures in time. In this paper, a novel method for WDD form detection of the running wagons by analyzing axle box acceleration signals is proposed. To solve the difficulty of weak feature extraction for the vibration signals, a novel feature extraction method combining adaptive chirp mode decomposition (ACMD) with fractal box dimension (FBD) is proposed. Firstly, a 3D feature space is constructed by the FBDs, which is calculated from the extracted chirp modes by ACMD. Then, a multiple kernel extreme learning machine optimized by genetic mutation particle swarm optimization is developed for the classification of the feature vectors. Both simulation and field test results indicate that the proposed detection method is powerful for accurate identification of the wheelsets with standard diameter, in-phase WDD, and anti-phase WDD, and the algorithm efficiency shows practicability in onboard monitoring.

CROP CLASSIFICATION BASED ON A GAOFEN 1/WIDE-FIELD-VIEW TIME SERIES

The accurate acquisition of crop planting area information allows for agricultural management departments to understand crop production information promptly. Concurrently, support vector machine (SVM) algorithms are unable to determine parameter combinations in remote sensing image crop classification to obtain optimal classification results. To solve this issue, this paper proposes an SVM that is optimized by the adaptive mutation particle swarm optimization algorithm. To test the algorithm, we undertook an experiment in Acheng District, Harbin City, Heilongjiang province, China, using Gaofen 1/wide-field-view satellite images to construct a time series for various vegetation indices. The SVM model with optimized parameters was compared with a traditional backpropagation neural network, a decision tree, and an SVM without parameter optimization. The experimental results verified that the improved SVM model obtained the highest classification accuracy.

Steam-Water Modelling and the Coal-Saving Scheduling Strategy of Combined Heat and Power Systems

China aims to peak carbon emissions by 2030. As a result, small-scale coal-fired combined heat and power (CHP) units and self-provided units are gradually shut down, and large-scale coal-fired CHP units are a solution to undertake the industrial heat loads. From the perspective of the industrial heat load allocation during the non-heating season, the problems regarding the coal-saving scheduling strategy of coal-fired CHP units are addressed. The steam-water equations of CHP units are established to analyze the heat-power coupling characteristics. The energy utilization efficiency, exergy efficiency and the coal consumption are analyzed. The optimization model of saving coal consumption is established and the adaptive mutation particle swarm optimization (AMPSO) is introduced to solve the above model. The 330 MW coal-fired CHP unit is taken as an example, and the results show that for the constant main flow rate, each increase of 1 t/h industrial steam extraction will reduce the power output by about 0.321 MW. The energy utilization efficiency and the exergy are mainly influenced by industrial steam supply and the power load, respectively. For the CHP system with two parallel CHP units, the unequal allocation of industrial heat load between two units saves more coal than equal allocation. The coal consumption can be reduced when the unit with lower power load undertakes more industrial heat load. In the typical day, the total coal consumption after optimization is 3203.92 tons, a decrease of 14.66 tons compared to the optimization before. The two CHP units in the case can benefit about 5,612,700 CHY extra in one year.

Fault diagnosis of train rotating parts based on multi-objective VMD optimization and ensemble learning

Rotating machinery is widely used in various systems of trains, and its health status is directly related to the reliability of train operation. Therefore, the fault diagnosis of train rotating machinery plays a promoting role in the safe operation and maintenance of trains. Due to the influence of track excitation and other equipment vibration, the vibration signal of rotating machinery is non-stationary and nonlinear, which brings challenges to the fault diagnosis of rotating machinery. To solve this problem, an intelligent fault diagnosis method for rotating machinery based on multi-objective variational mode decomposition (VMD) optimization and ensemble learning is proposed. Firstly, an improved multi-verse optimizer (MVO) algorithm is proposed by introducing Tent chaos theory and Levy flight theory, and a multi-objective VMD parameter optimization model is established. The improved MVO algorithm is utilized to optimize the parameters and reconstruct the signal. Then, a multi-domain feature extraction method is proposed for multi-domain fault feature extraction and feature selection. Finally, an adaptive mutation particle swarm optimization random forest (AMPSO-RF) pattern recognition algorithm is proposed for fault pattern recognition. Through the verification of bearing and gear fault data, the results showed that the raised method can accurately realize fault diagnosis of train rotating parts, and the accuracy rate reaches 100%, which is better than the compared methods.

Multi-objective optimization of a novel combined cooling, dehumidification and power system using improved M-PSO algorithm

This study proposes a novel combined cooling, dehumidification and power system based on internal combustion engine (ICE) for high temperature and humidity regions. In the proposed system, the electricity demand is mainly provided by ICE while the cooling and dehumidification demands are totally satisfied by absorption chiller (AC) and liquid desiccant dehumidification (LDD) units driven by waste heat of engine exhaust and jacket water. In addition, the electricity demand can be supplemented by battery and Organic Rankine Cycle (ORC) units driven by abundant waste heat of ICE. The aim of this system is designed to succeed the highly efficient utilization of ICE waste heat among AC, LDD and ORC units, and satisfy the multi-energy demands of users by optimizing the operation strategy. Firstly, a case study based on the actual power, cooling and dehumidification demands of a hotel building in Singapore is conducted to assess the performance of the proposed system. The parametric study of prominent design parameters in this system is investigated first to explore their effects on the system performance. Next, considering annual total cost and CO2 emissions as evaluation objectives, augmented ε-constraint method combined with improved Mutation particle swarm optimization (M-PSO) algorithm is used to conduct the multi-objective optimization of equipment capacity and operation. Finally, a sensitivity analysis of significant uncertainty factors is researched. The optimal results show that, compared to the traditional energy supply system, the annual carbon dioxide emission reduction ratio and total cost saving ratio can reach 35.91% and 25.46%, respectively.

Comprehensive improvement of camera calibration based on mutation particle swarm optimization

In order to meet the requirements of high-precision measurement, the method of improving camera calibration is studied. In the calibration process, the quality of the calibration image, the extraction accuracy of the calibration image corner and the nonlinear optimization effect of the camera linear parameters directly affect the calibration accuracy. First of all, in order to solve the problems in image acquisition, especially in the case of over exposure, an adaptive gamma correction method is designed to automatically adjust the image brightness, and enhance the contrast of black and white grid to improve the image acquisition quality. Secondly, a sub-pixel corner extraction algorithm based on homography matrix mapping is designed, which overcomes the error and omission of Harris corner extraction algorithm, and improves the accuracy of corner extraction. At last, adaptive weight and mutation particle swarm optimization algorithm are studied to optimize the camera parameters. Compared with other optimization algorithms, this optimization algorithm needs less parameter settings, fast convergence speed, and can obtain more accurate camera parameters. The average calibration error is 0.038 pixels.

Low-speed sensorless control of permanent magnet synchronous motors via the Cauchy–Gauss hybrid mutation particle swarm optimization algorithm

In the process of tracking the speed and rotor position of a permanent magnet synchronous motor, the conventional proportional–integral (PI) control method has difficulty satisfying the requirements of the control system. Therefore, an alternative based on particle swarm optimization is proposed in this article. To improve the optimization performance, a nonlinear differential decreasing inertia weight and an asynchronous time-varying learning factor are adopted. A Cauchy–Gauss hybrid mutation strategy is introduced to prevent the algorithm from falling easily into a local optimum. Then, the high-frequency injection method is used to estimate the rotor speed and position. Simulations and experiments show that the proposed Cauchy–Gauss hybrid mutation particle swarm optimization method has good dynamic performance and anti-interference capability and can satisfy the requirements of the system.

A Novel Adaptive Mutation PSO Optimized SVM Algorithm for sEMG-Based Gesture Recognition

In the field of noncontact human-computer interaction, it is of crucial importance to distinguish different surface electromyography (sEMG) gestures accurately for intelligent prosthetic control. Gesture recognition based on low sampling frequency sEMG signal can extend the application of wearable low-cost EMG sensor (for example, MYO bracelet) in motion control. In this paper, a combination of sEMG gesture recognition consisting of feature extraction, genetic algorithm (GA), and support vector machine (SVM) model is proposed. Particularly, a novel adaptive mutation particle swarm optimization (AMPSO) algorithm is proposed to optimize the parameters of SVM; moreover, a new calculation method of mutation probability is also defined. The AMPSO-SVM model based on combination processing is successfully applied to MYO bracelet dataset, and four gesture classifications are carried out. Furthermore, AMPSO-SVM is compared with PSO-SVM, GS-SVM, and BP. The sEMG gesture recognition rate of AMPSO-SVM is 0.975, PSO-SVM is 0.9463, GS-SVM is 0.9093, and BP is 0.9019. The experimental results show that AMPSO-SVM is effective for low-frequency sEMG signals of different gestures.

Exergoeconomic analysis for a thermoelectric generator using mutation particle swarm optimization (M-PSO)

Efficiency and cost-effectiveness play dominant roles in the commercialization of thermoelectric generator (TEG) technology. In this paper, the exergy analysis of a TEG module with 199 TE couples was considered. Two objective functions, the exergy efficiency and levelized cost of energy (LCOE), were established for exergoeconomic analysis. The geometric structure and working conditions involving TE couple length, base area ratio, working temperature, and load resistance were varied. The particle swarm optimization (PSO) method has excellent convergence and few parameters need to be adjusted. Mutation can increase randomization for the PSO method, making it possible to improve its search direction. Therefore, the mutation-PSO (M-PSO) algorithm was used to optimize the exergy efficiency and LCOE for the TEG. Through the M-PSO algorithm, the optimum corresponds to an exergy efficiency of 29% and LCOE of 1.93 $US/kWh·m2 under a maximum temperature difference of 40 K. In order to achieve a balance between the two exergoeconomic indices, the ξ-constraint combined with the M-PSO method was used to obtain alternatives, named Pareto solutions. Then, these alternatives were ranked to acquire an ideal solution based on a technique for order preference by similarity ideal solution (TOPSIS) method. The TOPSIS ideal solution corresponds to an exergy efficiency of 22.2% and LCOE of 3.02 $US/kWh·m2.

Energy management and optimization of PEMFC/battery mobile robot based on hybrid rule strategy and AMPSO

Due to the flexibility of seam tracking robot in special environment, independent power supply with different characteristics is adopted. Power allocation of hybrid power system composed of proton exchange membrane fuel cell and lithium battery is mainly researched. According to the requirements of economy and safety, hybrid rule fuzzy state machine control is designed to realize the optimal operation and fast response of power supply system. On the basis of energy allocation, further reduction of hydrogen consumption and power fluctuation is considered, which improves the power supply scheme. The sensitivity analysis method is used to screen the optimization variables, which reduces the complexity of the strategy; in addition, an adaptive mutation particle swarm optimization is studied to optimize the strategy, which combines the mutation idea, adaptively changes the weight and learning factor, and realizes the minimization of power fluctuation and equivalent hydrogen consumption. The results show that the stability of fuel cell and the rationality of lithium battery charging and discharging are greatly improved, and the fuel economy and service life of hybrid power system are improved.

Development of a Driving Cycle for Fuzhou Using K-Means and AMPSO

The driving cycle is a speed-to-time curve, a fundamental technique in the automotive industry, and also a basis to set standards for fuel consumption and emissions of vehicles. A driving cycle is developed based on firsthand driving data collected from fieldwork. First, bad data in the original dataset are preprocessed, the time-series standard smoothing algorithm is used to smoothen the data, and Lagrange’s interpolation is used to realize data interpolation. Next, the rules for kinematic fragment extraction are set to divide the data into kinematic fragments. Last, an evaluation system of kinematic fragment feature parameters is built. On that basis, the K-means clustering method is used to cluster the dimensionally reduced data, and the adaptive mutation particle swarm optimization (AMPSO) algorithm is employed to select the optimal fragments from candidate fragments to develop a driving cycle. The experiment result shows that the developed driving cycle can represent the kinematic features of the experiment car and provides a basis for the development of a driving cycle for Fuzhou.

A Hybrid Dynamic Probability Mutation Particle Swarm Optimization for Engineering Structure Design

Particle swarm optimization (PSO) is a common metaheuristic algorithm. However, when dealing with practical engineering structure optimization problems, it is prone to premature convergence during the search process and falls into a local optimum. To strengthen its performance, combining several ideas of the differential evolution algorithm (DE), a dynamic probability mutation particle swarm optimization with chaotic inertia weight (CWDEPSO) is proposed. The main improvements are achieved by improving the parameters and algorithm mechanism in this paper. The former proposes a novel inverse tangent chaotic inertia weight and sine learning factors. Besides, the scaling factor and crossover probability are improved by random distributions, respectively. The latter introduces a monitoring mechanism. By monitoring the convergence of PSO, a developed mutation operator with a more reliable local search capability is adopted and increases population diversity to help PSO escape from the local optimum effectively. To evaluate the effectiveness of the CWDEPSO algorithm, 24 benchmark functions and two groups of engineering optimization experiments are used for numerical and engineering optimization, respectively. The results indicate CWDEPSO offers better convergence accuracy and speed compared with some well-known metaheuristic algorithms.

Enhancement of travel range of electrostatically driven fixed-fixed microbeam in static and dynamic mode using mutated particle swarm optimization

Particle swarm optimization (PSO) is a substantially exercised technique for optimizing structural design problems. One major snag of particle swarm optimization is a possibility of premature convergence, observed in the specific cases where global optimum is encircled by the solutions having higher objective function values. These territories are unexplored and swarm can fall into local optimum. The present work proposes to suggest one of the variants of the PSO algorithm to enhance the reliability. In the proposed algorithm, during each iteration, few particles will be chosen to follow worst solution, this would enable to perform exploration and exploitation of design space simultaneously. Hence, the probability of not getting caught into local optima gets augmented which gives better assurance to the achieved solution. The mutated particle swarm optimization (MPSO) is efficaciously employed for shape optimization of electrostatically driven fixed-fixed microbeams to augment the static and dynamic travel range. The geometry of fixed-fixed microbeams is optimized using continuous, smooth, parametric functions of width and thickness. For structural analysis, Rayleigh–Ritz energy method is utilized to compute the static and dynamic pull-in parameters. Nonlinear design constraints of engineering interest are implemented through penalty approach. Using modified particle swarm optimization, width and thickness profiles of fixed-fixed microbeams that result in maximum pull-in displacement have been obtained. Augmented pull-in parameters for optimized geometry of microbeams have been endorsed using 3-D finite element analysis. The optimization parameters reveal maximum rise in the static pull-in displacement by 41.25% and dynamic pull-in displacement by 39.15% of fixed-fixed microbeam compared to referential prismatic microbeam. Efficacy of mutated particle swarm optimization is emphasized through 18 representative cases. Mutated particle swarm optimization converges in around 20% of time in comparison with the hybrid simulated annealing, without getting trapped into local minimum in all the test cases.