4331 results about "Particle swarm algorithm" patented technology

The invention discloses a mobile robot path planning method based on an improvement of an ant colony algorithm and particle swarm optimization. The method mainly solves the problems that in the prior art, the operating speed of an algorithm is low, and frequency of turning of an optimized path is high. The planning method includes the steps that modeling is carried out on a work environment of a robot; the particle swarm optimization is utilized to quickly carry out path planning, pheromones more than those around an obtained path are scattered on the obtained path, and guiding is provided for an ant colony; an ant colony algorithm optimized by the principle of inertia is adopted, and optimization is conducted on the basis of the particle swarm optimization; the motion path of the robot is output according to an optimization result. According to the planning method, comprehensive consideration is given to stability and robustness of the algorithm, iterations can be effectively reduced, searching efficiency is improved, the path length is shortened, the frequency of turning is reduced, path quality is substantially improved, and the planning method accords with an artificial planning intention and is suitable for autonomous navigation of various mobile robots in a static environment.

The invention relates to a breaker fault diagnosis method based on separating / closing coil current signals, which includes the following steps: 1, collecting a primary fault dataset and performing normalization processing to the collected data; 2, adopting the algorithm P-KFCM combined by the algorithm PSO (particle swarm optimization) and the algorithm KFCM (kernel fuzzy C-mean clustering) to classify the data set, so as to obtain a primary fault sample membership matrix and classifying the dataset into C types according to the degree of membership; 3, verifying the cluster number C through the cluster effectiveness index MPC, and setting the cluster number C corresponding to the maximum value of MPC as the optimal cluster number; 4, building an SVM predicating trainer through data samples of every two types in the optimal classification, so as to obtain C*(C-1) / 2 SVM training models; 5, inputting the collected test samples in the SVM training models, and obtaining the primary testing results; 6, obtaining the final fault diagnosis by combining all the testing results. Through the adoption of the method, the accuracy of fault classification can be improved, and the on-line detection on fault types of breakers can be realized.

The present invention provides a method, system and storage medium for load dispatch optimization for residential microgrid. The method includes collecting environmental data and time data of residential microgrid in preset future time period; obtaining power load data of residential microgrid in future time period by inputting environmental data and time data into pre-trained load forecasting model; obtaining photovoltaic output power data of residential microgrid in future time period by inputting environmental data and time data into pre-trained photovoltaic output power forecasting model; determining objective function and corresponding constraint condition of residential microgrid in future time period, where optimization objective of objective function is to minimize total cost of residential microgrid; obtaining load dispatch scheme of residential microgrid in future time period by solving objective function with particle swarm algorithm. The invention can provide load dispatch scheme suitable for current microgrid and reduce operating cost of residential microgrid.

The invention discloses a pile-up noise reduction own coding network bearing fault diagnosis method based on particle swarm optimization. The bearing fault diagnosis method provides an improved pile-up noise reduction own coding network SADE bearing fault diagnosis method, SDAE network hyper-parameters, such as cyber hidden layer nodes, sparse parameters, input data random zero setting ratio, are selected adaptively by particle swarm optimization PSO, a SADE network structure is determined, top character representation of malfunction inputting a soft-max classifier is obtained and a classification of defects is discerned. The bearing fault diagnosis method has better feature in learning capacity and more robustness than feature of learning of ordinary sparse own coding device, and builds a SDAE diagnostic model having multi-hidden layer by optimizing the hyper-parameters of noise reduction own coding network deepness network structure with the particle swarm optimization, accuracy of the classification of defects is improved at last.

The invention discloses a method for solving a multiple-depot logistics transportation vehicle routing problem. The method comprises steps as follows: inputting multiple-depot problem basic parameters based on real-time traffic information, establishing a multiple-depot logistics transportation scheduling mathematic model based on the real-time traffic information, adopting a clustering analysis method, introducing the particle swarm optimization algorithm to adjust and optimize ant colony algorithm pheromones, optimizing ant colony algorithm heuristic factors with the particle swarm optimization algorithm, solving an optimal distribution route, and establishing a mathematic model according to the multiple-depot logistics transportation vehicle routing problem based on the real-time traffic information; taking distances between clients and parking lots as main factors, performing area division on the clients and the parking lots with the clustering analysis method, and converting a multiple-depot problem into a single-depot problem; introducing the particle swarm optimization algorithm to improve the ant colony algorithm to solve the model. The method has the better global and local optimization capacity and has higher efficiency and stability when solving the multiple-depot problem.

The invention discloses a neural network method for diagnosing analog circuit failures which is based on a particle swarm algorithm, and comprises the following steps: imposing an actuating signal to an analog circuit to be tested, measuring an actuating response signal in the testing nodes of the circuit, extracting the candidate signal of failure characteristics by implementing noise elimination and then wavelet packet transformation on the measured actuating response signal, extracting the failure characteristics information by further implementing orthogonal principal component analysis and normalization processing on the candidate signal of failure characteristics, and sending the failure characteristics information as samples to the neural network for implementing classification. The method adopts the particle swarm algorithm instead of a gradient descent method in traditional BP algorithms, thus leading the improved algorithm to be characterized in that the algorithm avoids the local minimum problem and has better generalization performance. The BP neural network method for diagnosing the analog circuit failures which is optimized on the basis of particle swarm can obviously reduce iteration times in the algorithm, improve the precision of network convergence, and improve diagnosis speed and precision.

The invention relates to a static path planning method of a robot. The method comprises that a target point is set, and an artificial potential field is established within the map range by taking the target point as a terminal point; particle swarm optimization is used, a start point of the robot is provided with m particle swarms, the flight speed of the ith particle in the tth step is vi(t), simulated walking along the path from the start point to the terminal point is carried out on each particle by combining the artificial potential field with the particle swarm optimization, and each particle forms self movement track in the simulated walking process; most particles are converged to one of the multiple tracks gradually, and an optical walking path from the start point to the terminal point is obtained within the map range; and the robot moves from the start point to the terminal point according to the optimal walking path. According to the invention, the potential field method, the grid method and the particle swarm optimization are combined, potential field distribution in a grid map is calculated directly, the pre-planned path is obtained from the target point of the potential field along the direction in which the potential field decreases most rapidly, the method is safe and effective, and path planning is accurate and reliable.

The invention relates to a wind power station energy storage capacity control method based on particle swarm optimization. The wind power station energy storage capacity control method includes the steps of taking the interval reference value of the wind power station output power which is adapted to the dispatching cycle of a power grid as a foundation, taking the influence of the wind-abandoning energy of a wind power station and the lost energy of an energy storage system into consideration, taking the lowest costs of the energy storage investment and a wind and power operation system as target functions, establishing a policy model for energy storage capacity optimizing based on a storage battery energy storage system, and then applying the improved particle swarm optimization to solve the functions. By the aid of the wind power station energy storage capacity control method based on the particle swarm optimization, the wind power which is output under effect of the energy storage system can be output smoothly at intervals, so that effective connection between the energy storage system and the existing dispatching operation manner can be realized, and the best economic benefit can be achieved simultaneously.

The invention provides a mobile robot formation control method based on leader-follow. The method is formed by a global positioning system, a wireless communication system, an algorithm processing system, and a speed control system. The global positioning system obtains the pose information of each robot and sends the pose information to an arithmetic processing system through a wireless communication system, and the formation motion control is finally realized through the information interaction with the speed control system. In a control algorithm, firstly a leader-follow formation motion model is established, a follow robot motion control rate is given, then a follow robot trajectory prediction model is established, a nonlinear least squares method prediction model is employed, a prediction model parameter is optimized by using an improved particle swarm algorithm, a communication data abnormal range is defined, and a prediction point is started to substitute an abnormal point so as to ensure formation motion. According to the method, the prediction model is introduced, the formation order deviation phenomenon caused by temporary communication abnormality, the reliability of follow robot motion is ensured, and the stability of the formation is greatly improved.

The invention discloses a method for estimating the health state of a power battery. The method comprises steps of acquiring the constant-current charging voltage V, current I and time t of a batteryto obtain charging capacity Q, establishing a V-Q relationship curve, acquiring a capacity increment curve peak value and peak position information, establishing a RBF neural network, training a RBF neural network model by a particle swarm optimization algorithm, and estimating the health state of the battery by using the generated RBF neural network. The method establishes a mapping relationshipamong the constant-current charging capacity increment curve peak value, the peak position and the health state of the battery by means of a data driving mode without establishing an equivalent circuit of the electric vehicle power battery, thereby improving estimation accuracy and realizing online real-time estimation for the overall estimation of a battery pack.

The invention discloses a sewage-disposal soft measurement method on the basis of an integrated neural network, and belongs to the field of sewage disposal. A sewage disposal process is high in nonlinearity, time-varying characteristics and complexity, and measurement for key water quality indexes is crucially significant in control of water pollution. In order to improve precision of simultaneous soft measurement for various key water quality parameters in a sewage-disposal soft measurement process by the sewage-disposal soft measurement method, an integrated neural network model is provided for measuring COD (chemical oxygen demand) of outlet water, BOD (biochemical oxygen demand) of the outlet water and TN (total nitrogen) of the outlet water, coupling relation between the three key water quality parameters is sufficiently utilized in the model, the integrated neural network model contains three feedforward neural sub-networks, and the various neural sub-networks are trained by particle swarm optimization, so that the optimal structure of each neural sub-network can be obtained. The COD of the outlet water, the BOD of the outlet water and the TN of the outlet water are predicted by the trained neural network finally, and prediction results are accurate.

The invention discloses a combined cold heat and power supply microgrid multi-objective dynamic optimal operation method; characteristics of translatable electrical load are firstly considered in an optimization process, then schedulability of source side and energy storage system are considered, contribution in each period in three kinds of controllable units serves as optimization variables, minimum system operation cost and minimum pollutant emission control expense serve as optimal operation targets, and a mathematical model of current multi-objective optimal operation problem is established; an excellent particles leading multi-objective particle swarm optimization algorithm is adopted to solve the optimization problem, that is, a single objective genetic algorithm is utilized to respectively find two points including minimum system operation cost and minimum pollutant emission control expense, and the two points serving as excellent particles is utilized to lead an optimal direction of the multi-objective particle swarm algorithm; the invention provides an effective multi-objective dynamic optimal operation method, and the method is significant for improving energy source comprehensive utilization efficiency of a multiple energy coupled system and promoting renewable energy source development.

The invention relates to a multi-target reactive power optimization method, in particular to a multi-target reactive power optimization method based on an adaptive chaos particle swarm algorithm. The method aims at solving the problem that multi-target reactive power optimization control variables are probably trapped in a locally optimal solution, and the speed for acquiring an optimal solution is low. The method includes the steps that firstly, original data of a particle swarm are input to an adaptive chaos particle swarm optimization algorithm program; secondly, first m particles are selected from the particle swarm as initial positions of the particle swarm according to fitness values in a preferred mode; thirdly, inertia weights w of the particles are acquired through calculation of inertia weight coefficients, and first M preferred particles are selected from the particle swarm for chaos optimization calculation; fourthly, the speed and the positions of the particles are updated according to the particle swarm reactive power optimization algorithm, and then iteration allowances and values of the control variables can be acquired; fifthly, whether iteration stop conditions are met or not is judged, and then the multi-target reactive power optimization method based on the adaptive chaos particle swarm optimization algorithm is finished. The multi-target reactive power optimization method is applied to the field of electric systems.

The invention discloses a wind power plant parameter identification and dynamic equivalence method based on operation data, comprising the following steps of a) performing recognition on wind generation set control model parameters based on testing data, b) combining with operation data and choosing characteristic variables reflecting the wind generation set and the wind power plant under influence and utilizing the improved fuzzy K average value dynamic clustering algorithm to perform cluster division, c) performing network simplification and parameter optimizing to obtain a wind power plant dynamic equivalence model based on global optimal position mutation particle swarm algorithm, and d) under disturbance input, comparing the wind power dynamic equivalence model with the detailed model dynamic respond to verify the validity of the equivalence model. The wind field dynamic equivalence model constructed by the invention can accurately reflect the dynamic characteristics of grid-connection points of the wind plant and have important construction application values, and can be used in the analysis of the stability of the double-fed wind power plant access power system and provide theory support for the programming and operation scheduling of the wind power plant power system.

The present invention discloses a method and system for collaborative scheduling of production and transportation in supply chains based on improved particle swarm optimization. The method includes the following steps: 1. setting algorithm parameters; 2. randomly generating an initial population; 3. correcting codes; 4. calculating fitness values and updating the speed and the position of particles; 5. performing tournament selection; 6. performing crossover mutation; 7. updating the population; and 8. determining whether a termination condition is satisfied; if so, outputting a globally optimal solution; if not, returning to the step 3. In the present invention, an approximately optimal solution can be obtained in view of the collaborative scheduling problem of production and transportation considering distributed storage, so that the cost is reduced for supply chains and the service level of supply chains is enhanced.

The present invention relates to a short-term load prediction method based on a particle swarm optimization least squares support vector machine. Aiming at the deficiency of a single kernel function least squares support vector machine model, the Gaussian kernel function and the Polynomial kernel function are combined to obtain a new hybrid kernel function so as to improve the learning ability and the generalization ability of the least squares support vector machine model; the particle swarm optimization algorithm based on double populations is employed to optimize parameters of the least squares support vector machine of the hybrid kernel function, the particle swarm optimization algorithm based on double populations has advantages of good global search and local search performances, and a strategy having dynamic accelerated factors is employed so as to greatly increase the variety of particles and prevent the search from being caught in a local extremum. The short-term load prediction method based on the particle swarm optimization least squares support vector machine maximally utilizes information in computation, and in the process of selecting the optimal parameter value, the average mean square error of load data and actual data is employed as the adaptation value of the particle swarm optimization algorithm so as to improve the short-item load prediction accuracy value.

The invention discloses a travel time prediction method for optimizing an LSTM neural network through a particle swarm optimization algorithm, and the method comprises the following steps: S1, collecting travel time data, performing data normalization, and dividing the data into a training set and a test set proportionally; S2, optimizing each parameter of an LSTM neural network prediction model by using the particle swarm optimization algorithm; S3, inputting the parameters, optimized through the particle swarm optimization algorithm, and the training set, and performing the iterative optimization of the LSTM neural network prediction model; S4, predicting the test set through the trained LSTM neural network model, and evaluating a model error. The method is quick in optimization. Compared with a random forest, SVM and KNN in the traditional prediction algorithm, the method of the invention has the least mean square error and square error for the data prediction, and the model reducesthe calculation burden, so the method shows better prediction performance.

The invention discloses an optimized scheduling method for a wind-solar storage hybrid system. A wind-solar output power reference value is set and day-ahead prediction output curves and load prediction curves of wind power and photovoltaic energy are obtained; a wind-solar storage energy output curve as well as an operation mode of an energy storage device are determined; a multi-objective optimization scheduling model is established by taking intra-day operation cost minimization of system scheduling as an optimization objective; and then an input load prediction curve, a wind-solar storageenergy output curve and the operation mode of the energy storage device are inputted, and the optimization scheduling model is calculated by using an improved particle swarm optimization algorithm toobtain a day-ahead set combination output curve. According to the invention, with utilization of the complementary characteristic and frequency modulation capability of the wind-solar energy, the frequency regulation pressures of the energy storage device and the conventional energy set are reduced; and with consideration of the frequency safety constraint, the optimal operation mode of the wind-solar storage hybrid energy system is determined, so that the economic and security of the operation of the hybrid energy system are improved.

The invention discloses an underwater vehicle path planning method based on ocean current historical statistic information, comprising the following steps of: determining a sailing region, rasterizing the sailing region, generating an ocean current field in the sailing region by an ocean current historical statistic database, taking an electronic chart as an environment field to simplify and combine obstacles, islands and phytal zones in the sailing region and generate a prohibited area, storing ocean current information and prohibited area information according to grids, creating a path evaluation function, searching for an optimal path by a particle swarm optimization algorithm, outputting the optimal path and ending the path planning process. In the method provided by the invention, the ocean current field closer to a true value is generated by the ocean current historical statistic database, under the condition of taking full account of ocean current influence, the path evaluation function is designed based on safety, economical efficiency and smoothness, the particle swarm optimization algorithm is used as a path searching algorithm to perform global path planning for the underwater vehicle so as to plan an underwater vehicle sailing path which is closer to a practical sailing path.

An RGB (red, green and blue) based method for adjusting white balance of an LED backlight comprises the following steps: when a display runs independently, adjusting the backlight to a new brightness level after powering up; reading the value of a temperature sensor on a backlight board; reading the drive current duty ratio of the backlight corresponding to the brightness level and the standard value of a color sensor at the temperature; turning on the backlight according to the read drive current duty ratio; reading the values of red, green and blue in the color sensor on the backlight board and comparing the actual value and the standard value of the current color sensor; and if the actual value is more than the standard value of the current color sensor, reducing a bit of the current duty ratio of the backlight, conversely, increasing a bit of the current duty ratio of the backlight. In order to obtain the drive current duty ratio and the standard value of the color sensor, the method adopts particle swarm optimization. The method has the advantages of adjusting white balance automatically at wide temperature, accelerating the response speed, relieving the labor intensity and improving the efficiency.

The invention provides a virtual synchronous generator virtual inertia and virtual damping coefficient adaptive control method, which relates to the technical field of smart grid and intelligent algorithms. Firstly, the inverter based on the virtual synchronous generator is modeled, and the correlation between the output frequency of the inverter and the virtual inertia J and the virtual damping coefficient D is obtained. Then the fitness function of the adaptive control method of virtual inertia J and virtual damping coefficient D based on the improved particle swarm optimization algorithm isdetermined. Finally, the improved particle swarm optimization algorithm is applied to the active power. In the frequency control part, the real-time adaptive control of virtual inertia J and virtualdamping coefficient D is realized in order to minimize the frequency deviation and stabilize the system. The invention provides a virtual synchronous generator virtual inertia and virtual damping coefficient real-time adaptive control method, which fully utilizes the characteristics of the virtual inertia and introduces the virtual damping coefficient, so that the inverter is more stable and the frequency offset is smaller.

The invention relates to a multi-resolution medical image registration method based on the quantum-behaved particle swarm optimization, which is characterized in that the method comprises the following step of: firstly removing the backgrounds of the two images to be registered to keep the images off the interference of noise; next obtaining the images with low resolution from the two background-removed images via the wavelet transform, using the low resolution to be the object, taking the normalized mutual information as the objective function, using the quantum-behaved particle swarm optimization, then the images with the high resolution to be the object, and obtaining the rotation amount and the translation amount between the two images to be registered to finish the image registration by using the Powell method. With solving a plurality of local extrema based on the objective function of the mutual information, the invention greatly improves the registration precision and speed, reaching up to the sub-pixel level; and is widely applicable to the fields of the image discrimination of the clinical diagnosis, the framing of the radiation treatment, the image guided surgical, etc.

The invention discloses a soft-sensing modeling method and a soft meter of a multi-model neural network in a biological fermentation process. The method comprises the following steps: a data preprocessing module preprocesses input variable data by a normalization and principle component analysis method; and then the data preprocessing module carries out cluster division on a preprocessed principle component variable set; through and then a BP neural network model module respectively establishes sub neural networks according to different clusters and finally establishes a soft-sensing model of the multi-model neural network. The soft-sensing model of the multi-model neural network is used for measuring biomass concentration in a fermentation process on line, and a measurement value is displayed through a biomass concentration soft-sensing value displayer. The invention introduces a core fuzzy C mean clustering algorithm based on a particle swarm algorithm and combines the mean clustering algorithm with the modeling method of the multi-model neural network, and the established model is simple, realizes the on-line measurement of the biomass concentration and has timely control, high measurement accuracy and strong capacity of resisting disturbance.

The invention provides a power distribution network fault positioning method based on improvement of a binary particle swarm algorithm, the conventional binary particle swarm algorithm is improved, and the method is applied to positioning of power distribution network faults. The method comprises following steps: firstly, determining parameters including the particle swarm scale and the maximum iteration frequency etc.; then forming an expectation function of a switch according to fault information of the switch, and constructing a fitness function of power distribution network fault positioning; initializing a particle swarm, setting particle positions, and setting the speed of the particles as 0; calculating the fitness values of the particles according to the fitness function, and setting an initial global extremum; updating an individual extremum and the initial global extremum; updating the speed and position of the particle swarm; and stopping calculation when reaching the maximum iteration frequency, and outputting the global optimal position of the particle swarm, namely the practical fault state of each feed line section of a target power distribution network. According to the method, the problem of premature convergence of the conventional method can be overcome, and the convergence and the stability of the algorithm can be further improved.

The invention discloses a charging and discharging scheduling method for electric vehicles connected to a micro-grid. The method comprises the steps of: (1) determining a system structure of the micro-grid and the characteristics of various units; (2) building a micro-grid optimal scheduling target function of considering the battery depreciation cost of the electric vehicles under time-sharing electrovalence; (3) determining constraints of various distributed power supplies, batteries of the electric vehicles and the like and forming a micro-grid optimal scheduling model by the constraints and the micro-grid optimal scheduling target function; (4) determining the number, the start-stop time, the start-stop charged states and other basic calculated data of the electric vehicles connected to the power grid under the time-sharing electrovalence; and (5) solving the micro-grid optimal scheduling model through a particle swarm algorithm and determining the charge and discharge power when the electric vehicles are connected to the power grid. The batteries of the electric vehicles are connected to the micro-grid as mobile distributed energy storage devices to achieve the peak load shifting effect; the operation safety and stability of the micro-grid in the time-sharing electrovalence environment are improved; and the energy utilization efficiency and the power grid operation efficiency are simultaneously improved.

A particle swarm algorithm is applied to an optimum coverage problem of wireless sensor network nodes, and the invention provides a coverage mechanism of the particle swarm optimization algorithm based on a discrete binary edition, and the mechanism is used for performing optimal solution on the wireless sensor network node coverage problem. The method comprises the following steps: defining the wireless sensor network node coverage problem as a 0 / 1 planning problem, encoding individuals of a binary particle swarm algorithm as a 0 / 1 binary string and then performing optimization by an evolutionism of the particle swarm algorithm. In the method, two important indexes are defined to evaluate the solution results, one index is 'coverage' of a region, and the other index is 'consumption rate' of the sensor. Validity and high efficiency of the algorithm are verified by a simulation experiment.

The present invention discloses a short-term load prediction method based on variable selection and Gaussian process regression. The method includes the following steps that: 1) bad data elimination, supplementation and normalization pre-processing are performed on sample data; 2) candidate input variables are selected from the perspectives of historical load, temperature and humidity, and the date type of a prediction date, and the scores of the importance of the variables are calculated through a random forest algorithm, and the scores of the importance of the variables are sequenced; 3) an optimal variable set is determined through adopting a sequence forward search strategy and based on a Gaussian process regression model; 4) the Gaussian process regression model is trained based on the determined optimal variable set, and the parameters of the model are optimized based on improved particle swarm optimization; and 5) the predictive performance of the model is verified in a test set. With the method provided by the invention adopted, prediction accuracy can be effectively improved, and the load prediction problem of a power system can be solved.

The present invention relates to a target tracking cooperative control system and method based on multiple unmanned surface vehicles. The system is formed by connecting a shore-based global location host and a single unmanned surface vehicle control system through a wireless communication module. The method comprises the operation steps of: 1) a formation generation process: employing an auction algorithm to find a multi-target distribution scheme of the multiple unmanned surface vehicles with the maximum income of an unmanned surface vehicle group; 2) motion of the unmanned surface vehicles to perform geometric path planning from any initial state to a target point; and 3) prediction of a target motion track through adoption of a prediction model based on a particle swarm to replace communication abnormal data and perform formation track tracking. The method reduces the calculation amount of the multiple auction processes, achieves the real-time demands of task distribution of the unmanned surface vehicles, employs the path planning method based on the geometric method and the track tracking method based on the neural network to meet the timeliness and the accuracy requirements ofsingle-vehicle track tracking control, employs the motion track predicted by employing the particle swarm optimization to perform compensation, improves the tracking capacity of the unmanned surfacevehicles in the limitation of the communication condition and allows the formation tracking to have high reliability and stability.

The invention discloses a PID controller parameter setting algorithm based on an improved PSO (particle swarm optimization) algorithm, and the algorithm comprises the following steps: 1, initializing the algorithm parameters; 2, switching to an iterative loop, and carrying out the updating of the position and speed of each particle; 3, randomly searching a new position in the neighborhood of a current position; 4, calculating the adaptability difference between two positions, and judging whether to accept the new position or not through a simulated annealing mechanism when the adaptability of the new position is inferior to the adaptability of an original position but is superior to the adaptability of a global optimal position; 5, updating the global optimal position of a population, carrying out the natural selection operation, carrying out the arrangement of all particles according to the adaptability values, and employing the information of a part of better particles to replace the information of the other half particles; 6, judging whether to stop the iteration or not; 7, outputting PID controller parameters or executing step 2 again. The method can achieve the automatic setting of control parameters, irons out a defect that a conventional PSO algorithm is very liable to be caught in local optimization, achieves the complementation of the simulated annealing operation and a natural selection strategy, improves the convergence precision of the algorithm under the condition that the number of convergence times of the algorithm is guaranteed, is higher in robustness and precision, and enables the PID controller to generate a more excellent control effect.