279results about How to "Strong global search capability" patented technology

The invention discloses a maximum-minimum ant colony optimization method and a maximum-minimum ant colony optimization system for solving a vehicle scheduling problem. The maximum-minimum ant colony optimization method comprises the following steps of acquiring address information of a customer according to delivery information in an order ticket; reading relevant information in a maximum-minimum ant colony algorithm; and performing initialization, condition termination judgment, path establishment, path improvement and information updating on the ant colony algorithm. The numerical value of pheromone polatility is changed dynamically, convergence is accelerated, a plurality of paths are searched, global searching capability of the algorithm is improved, and premature and stagnation are avoided. The maximum-minimum ant colony optimization method has the advantages that the method is easy to implement and the rapid convergence capability and the rapid searching capability are high when the method is used for solving a vehicle path problem.

The present invention discloses a parking system path planning method on the basis of an improved ant colony algorithm. The method comprises: creating an AGV operation environment model through adoption of a link visible graph; planning the initial path of the AGV from an origin to a terminal point based on a Dijkstra algorithm; performing optimization improvement of the ant colony algorithm through introduce of a node random selection mechanism and a maximin ant system and changing of a sociohormone update mode; and selecting the improved ant colony algorithm to optimize the initial path, and completing the parking system path planning method. The parking system path planning method on the basis of an improved ant colony algorithm is able to allow an AGV to effectively avoid a barrier and then find out an optimal path through fusion of an ant colony algorithm; and moreover, a mixed algorithm shows up a high global searching ability and a good convergence, so that the path search efficiency is improved, the search path length is shortened, the search path quality is improved, the parking land occupation area is small, and the purposes of large number of effective parking and the intelligence are achieved.

The invention relates to a rolling bearing fault classifying method based on FOA-MKSVM (fruit fly optimization algorithm-multiple kernel support vector machine), and belongs to the technical field of fault diagnosis of a rolling bearing. The invention aims at providing a rolling bearing fault classifying method which is fewer in initialization parameters, simple for setting parameters, high in global search capability and high in classifying accuracy. The method is characterized by comprising the following steps: extracting characteristics of each vibration signal of the rolling bearing at various states; establishing a multiple-kernel kernel function to achieve the multinucleation of a support vector machine; adopting a training characteristic set as the input of the multiple kernel support vector machine (MKSVM), and carrying out the parameter optimizing for a penalty coefficient C, each kernel function parameter and a kernel function weight gamma m of the MKSVM by utilizing a fruit fly optimization algorithm (FOA); inputting a test characteristic set into an MKSVM model to be tested, and then obtaining the classifying accuracy of the rolling bearing at a normal state, an inner ring fault state, an outer ring fault state and a rolling body fault state. The rolling bearing fault classifying method has the advantages of fewer initialization parameters, simplicity in parameter setting, high global search capability and high classifying accuracy.

The invention discloses an inner and outer layer nesting ECMS (equivalent fuel consumption minimization strategy) multi-objective double-layer optimization method. The inner and outer layer nesting ECMS multi-objective double-layer optimization method includes steps of building multi-objective optimization models of plug-in hybrid electric vehicles; solving the multi-objective optimization modelsby the aid of inner and outer layer nesting multi-objective particle swarm algorithms to obtain multi-objective optimized Pareto solution set front edges; weighting equivalent fuel consumption per hundred kilometers and variation ranges of deviation of SOC (state of charge) final values and target values, building total evaluation functions related to the equivalent fuel consumption per hundred kilometers and SOC deviation and selecting the optimal charge and discharge equivalent factors and engine and motor power distribution modes corresponding to the optimal charge and discharge equivalentfactors. The inner and outer layer nesting ECMS multi-objective double-layer optimization method has the advantages that output power of engines and motors of the plug-in hybrid electric vehicles canbe reasonably distributed at CS (charge sustaining) stages, so that fuel consumption can be reduced as much as possible, battery SOC balance still can be effectively kept, and the fuel economy of theintegral vehicles can be improved.

The invention discloses a mobile robot path planning method based on a whale optimization algorithm. The method comprises the following steps: step S1, initializing the whale optimization algorithm, setting parameters of the algorithm, using a fitness function to obtain fitness values of a whale at all positions, and determining an initial individual optimal position and a global optimal positionof a whale population; step S2, using a new convergence factor, re-calculating a coefficient vector and updating the new position of the whale individual; step S3, calculating the fitness value of thewhale individual at the new position and comparing the fitness value with the fitness value of the original position; step S4, if the fitness value of the new position is superior than the fitness value of the original position, updating the individual best position of the whale population, and updating the global optimal position; and step S5, after reaching the number of iterations, selecting awhale path with the minimum fitness value as the optimal path for the mobile robot path planning, otherwise, executing steps S2 to S4. The mobile robot path planning method based on the whale optimization algorithm provided by the invention has high convergence precision, fast convergence speed, and can avoid falling into local optimum in the later stage of the algorithm iteration.

The invention discloses a network intrusion detection method. The network intrusion detection method includes: searching network data to construct a test network data set; performing feature extraction on the test network data set by utilizing a kernel principal component analysis method; constructing a training data set, putting the training data set into a support vector machine classifier for training; obtaining feature datasets, obtaining an optimal feature subset from the feature data set by using a genetic algorithm; utilizing a firefly swarm optimization algorithm to obtain the overalllocal optimal feature subset and the optimal support vector machine parameters from the optimal feature subset, processing the training data set according to the overall local optimal feature subset,and inputting the training data set into a support vector machine classifier for classification modeling to obtain a network intrusion detection model. According to the method, the simplicity and convenience of the algorithm are improved, abnormal data can be more effectively found from samples, the detection accuracy of network intrusion is effectively improved, the missing report rate and the false report rate are reduced, and the overall performance of network intrusion detection is improved.

The invention relates to the technical field of job shop scheduling, in particular to an improved culture gene algorithm for solving a multi-objective flexible job shop scheduling problem. The algorithm comprises the following steps of performing process-based encoding; generating an initialized population; performing local search by a hill-climbing method; calculating fitness; judging whether an optimization criterion is met or not (if yes, generating an optimal individual and ending the algorithm, otherwise, executing the next step); performing selection; performing SPX crossover; performing mutation; performing local search by the hill-climbing method; generating a new-generation population; calculating fitness; and circulating the process. The algorithm is improved as follows: the local search is performed by utilizing the hill-climbing method, so that local optimum can be escaped for obtaining a better solution, and the calculation time can be shortened; and in addition, the crossover and mutation modes of the algorithm are improved, the SPX crossover method is adopted, and one of two methods of insertion mutation and replacement mutation is randomly selected for mutating individuals in the population by an equal probability Pm during mutation.

The invention provides a traffic information predication method based on a fruit fly optimization least-squares support vector machine for solving the problem of low predication accuracy of an existing traffic information predication method. The method comprises the following steps: carrying out normalization preprocessing on original traffic information data, normalizing the data into the interval [0, 1], generating a data set, and grouping the data set into a training set and a testing set; selecting a radial basis function as a kernel function of a least-squares support vector machine model, and determining a parameter combination (gamma, sigma); optimizing the parameter combination (gamma, sigma) of the least-squares support vector machine according to the fruit fly optimization algorithm to obtain the optimal value in a global scope; substituting the optimal value into optimized parameters, and establishing a traffic information predication model based on the fruit fly optimization least-squares support vector machine; inputting the data set, and generating a traffic information predication result according to the predication model; carrying out evaluation analysis of predication errors.

The invention discloses a calibrating method for an electronic compass. The calibrating method is used for promoting measuring accuracy of the electronic compass based on a self-adaption differential evolution algorithm and a Fourier neural network theory and is especially suitable for an orienting system with low cost and higher precision. The calibrating method comprises the following steps: utilizing a Fourier neural network to perform error modeling on the electronic compass and utilizing an improved self-adaption differential evolution algorithm to optimize a weight value of the Fourier neural network, so as to acquire an accurate error model to compensate a measuring value of the electronic compass. The error model which is established according to the calibrating method is capable of realizing accurate mapping of a sample space and has a higher nonlinear approaching capability. According to the calibrating method, a minimum local part is avoided, the defects of over-slow convergence rate and oscillation of the neural network are overcome and the influence of an outside magnetic field on the electronic compass is efficiently compensated, thereby greatly promoting the measuring accuracy of the electronic compass.

The invention relates to a method for determining optimization design parameters of a heat radiator, which is characterized by comprising: determining a group of parameter combinations which minimize the value of a comprehensive object function (M(X)) in a solution space of design parameter values of the heat radiator according to thermodynamic characteristic parameters of the heat radiator, wherein the comprehensive object function is positively correlated with the dynamic response characteristic and the pressure drop loss characteristic of the heat radiator, and the processing of the group of parameter combinations which minimize the value of the comprehensive object function comprises parameter optimization of the comprehensive object function so as to obtain the optimization design parameters meeting comprehensive assessment indexes of the heat radiator.

The invention discloses an ant colony optimization-differential evolution fusion method for solving traveling salesman problems, which comprises the following steps: (1) algorithm parameters are initialized; (2) an ant colony is initialized; (3) a first iteration is carried out; (4) a mutation operation and an interlace operation are carried out to the pheromones of various squads from the second generation, so as to generate new pheromones; (5) the first squad is selected; (6) the ants of each squad establish the respective optimal path in accordance with the primitive pheromones; (7) the ants of each squad establish the respective optimal path in accordance with the new pheromones; (8) the two optimal paths are compared to pick out the pheromones with a better result of path optimization; (9) the pheromones of various ant squads are updated and passed down to the next generation; (10) the sixth step is carried out again until all squads finish the calculation; (11) the optimal path of the current generation and the length thereof are determined; (12) the fourth step is carried out again to carry out the calculation of the next generation until the termination condition is met; and (13) the whole optimal path and the length thereof are determined. The method has better astringency and stronger global optimization capability and is an effective way to solve the large-scale and complicated optimization problems such as traveling salesman problems, etc.

The invention provides a mixing intelligent optimizing method for a semiconductor production line production plan. The method comprises two-stage optimization: firstly, adopting a fuzzy modeling and intelligent optimizing strategy; describing the uncertainty of a usable energy producing resource, caused by the uncertainty of equipment faults and fault restoring time, by using a fuzzy number, establishing a production plan model which aims to maintain the product level to be balanced and uses the fuzzy capability resource limitation as the constrain condition by utilizing a credibility plan; solving the plan model by adopting a mixing intelligent algorithm combined with the fuzzy simulation, a neural network and the artificial immune algorithm to obtain a feasible and optimum month batch plan; finally, constructing a heuristic strategy based on the delivery emergency and production period condition of the product, and further detailing the previous month batch plan into day batch plans.

The invention relates to a clustering method, in particular relates to an improved fuzzy C-mean clustering method based on quantum particle swarm optimization, and belongs to the technical field of data mining and artificial intelligence. The improved fuzzy C-mean clustering method comprises the steps of: firstly, based on the conventional fuzzy C-mean clustering algorithm, improving the fuzzy accuracy of the conventional clustering algorithm by using a novel distance standard in place of a Euclidean standard; meanwhile classifying singly and quickly through using an AFCM (Adaptive Fuzzy C-means) algorithm to replace a randomly distributed initial clustering center to reduce the sensitivity of the clustering algorithm on the initial clustering center; and finally, introducing a QPSO (AQPSO (Adaptive-Quantum Particle Swarm Optimization)) parallel optimization concept based on distance improvement in a clustering process, so that the clustering algorithm has relatively strong overall search capability, relatively high convergence precision, and can guarantee the convergence speed and obviously improve the clustering effect.

The invention relates to a wavelet transform, multi-strategy PSO (particle swarm optimization) and SVM (support vector machine) integrated based remote sensing image classification method. The method includes the following steps of 1), optionally selecting a remote sensing image to be classified, subjecting the image to grey processing and transforming the same into a corresponding grey image; 2), subjecting the grey image to noise suppressing preprocessing to acquire a preprocessed remote sensing image; 3), subjecting the preprocessed remote image to textural feature extraction by adopting wavelet transform prior to normalization processing to acquire textural feature vectors of the remote sensing image; 4), realizing the wavelet transform, multi-strategy PSO and SVM integrated based remote sensing image classification method by adopting a multi-strategy improved particle swarm optimization algorithm and parameters used for optimizing a SVM classifier, and classifying the textural feature vectors of the remote image to be classified to acquire attributes of the remote image. Therefore, the wavelet transform, multi-strategy PSO and SVM integrated based remote sensing image classification method is widely applicable to the technical field of computer image retrieval.

A customized public transit network optimization method based on intelligent search comprises the following steps: using network terminals to obtain passenger travel demand data, and building a passenger demand database; building an evaluate index mathematics model satisfying customization public transit and constrained conditions; using N stations ranking in the front in terms of passenger travel population as basis, and building the start point of a backup route set of the customization public transit and initialization routes of N backup route sets; using the initialization routes of N backup route sets as the basis, combing with public transit GIS and station passenger demands, and building a backup route set; using a passenger nonstop rate as a fitness function on the basis of the backup route set, and using a heredity algorithm to search the customized public transit network satisfying the customization public transit evaluate index mathematics model. The method can optimize the passenger nonstop rate so as to build the customized public transit network, thus alleviating partial transport problems, and providing conveniences for passengers to travel.

The invention relates to the technical field of intelligent sensing and particularly discloses a Brillouin scattering signal processing method and a distributed fiber sensing system thereof. The system comprises a sensing fiber, a laser signal source, an annular device, a detection pulse optical path modulation module, a frequency shift reference optical path modulation module, a coherent detection unit and a data acquisition processing module, so relative variation quantity of Brillouin frequency shifts can be precisely acquired, backward Brillouin scattering spectrums are acquired and the complete Brillouin scattering spectrum is obtained. The method comprises steps of firstly, establishing a detected Brillouin scattering signal spectrum model; and using a fast iteration algorithm based on numerical optimization to carry out characteristic extraction on the Brillouin scattering spectrum. In the method, scattering signal frequency spectrums obtained through frequency scanning of each time are fit, so sensing information of temperature or stress distributed along the fiber is precisely acquired. A solving method has strong global searching ability, so algorithm action time is shortened and measurement precision and timeliness of the system are effectively improved.

The invention relates to the field of unmanned vehicle control, and provides a parallel design scheme using quantum particle swarm optimization, to ensure that the control output meets the physical constraints of the vehicle and the comfort requirement for a human body so as to enable the vehicle to preferably adapt to the current road condition. The technical scheme of the parallel design schemeusing quantum particle swarm optimization includes the steps: establishing a kinetic model based on an unmanned vehicle, and performing discretization on the kinetic model; based on the above step, constructing a generalized cost function with a punishment item and an encouragement item by using a generalized Lagrangian multiplier so as to convert the constraint problem into a nonrestraint problem; and performing parallel design of quantum particle swarm optimization, performing optimized solution on the cost function of model prediction control by means of the parallel design to obtain a series of controlled variables, and finally acting the first component of the controlled variables on the vehicle. The parallel design scheme using quantum particle swarm optimization is mainly applied tothe unmanned vehicle control occasion.

The invention relates to a fault-tolerant routing recovery method of a heterogeneous wireless sensor network. The method comprises the following steps: when a path in a cluster of the heterogeneous wireless sensor network is broken due to node failure, establishing a multi-path routing generation graph in the cluster and performing path coding; selecting an optimal substitutive path by a multi-particle swarm immune cooperative optimization algorithm and performing routing recovery; and maintaining the network system by an algorithm-based protocol. The multi-particle swarm immune cooperative optimization algorithm has the characteristics of relatively strong global search capability, relatively high solving precision, fast convergence and the like. The invention improves the fault tolerance of the heterogeneous wireless sensor network, improves the success rate of data transmission by quickly establishing the optimal substitutive path and prolongs the survival time of the network.

The invention discloses a binary system particle swarm optimization (BSPSO) algorithm-based chaotic time series prediction method, and belongs to the technical field of digital signal processing. The method comprises the following steps of: after a known chaotic time series is subjected to phase space reconstruction, selecting neighboring points of a predicted state by means of Euclidean measurement, training the data of the neighboring points by using a local area linear model, solving coefficient parameters of the model, and predicting the chaotic time series by using the model, wherein the longer chaotic time series can be predicted by repeating the predicting step or changing the prediction step length. The parameters of the local area linear model and the parameters of the phase space reconstruction are of different values, the optimal values of the parameters can be sought by using a BSPSO algorithm, and the number of the selected neighboring points is greater than the embedded dimension, so that interference of false neighboring points is avoided as much as possible, and the prediction precision is improved.

An optimization problem of train schedule dispatching is a basic problem in the railway industrial field. The invention applies a differential evolution algorithm to the optimization for the periodic train schedule dispatching and relates to two large fields of train dispatching and intelligent calculation. The method utilizes the differential evolution algorithm to optimize departure times of trains of all service lines of a railroad system adopting a periodic departure way, so as to minimize the waiting time of a passenger for transferring in a relay station. According to the invention, dual population-based parameters and operator control mechanism are introduced into the algorithm, which greatly reduces the parameter sensitivity of the algorithm and improves the optimization efficiency and robustness of the algorithm. Taking a Guangzhou subway network and six man-made railway networks as examples for simulation tests, the method is proved to be very effective.

The invention discloses an adaptive particle swarm algorithm-based grayscale threshold obtaining method and an image segmentation method, and belongs to the technical field of image processing. The grayscale threshold obtaining method is characterized by comprising the following steps of S01, performing population initialization on a grayscale value of an image; S02, calculating a fitness value of an individual in a population; S03, calculating an optimal position and a global optimal position of the individual in the population; S04, updating the optimal position and the global optimal position of the individual in the population; and S05, judging whether a stop condition is met or not, and if the stop condition is met, obtaining an optimal solution and obtaining an optimal grayscale threshold, otherwise, executing the step S02 to enter a next-generation population, wherein the optimal position and the global optimal position of the individual are dynamically adjusted by adopting an inertial weight in the step S04. The grayscale threshold obtaining method has autonomic learning property, adaptivity and relatively high robustness, can concurrently solve the grayscale threshold globally and better avoid local optimum, and is accurate and efficient.

The invention provides an irregular part stock layout method based on a multi-factor particle swarm algorithm. The method comprises the following steps of 1, performing preprocessing on a sample sheet, performing sorting merging on some sample sheets, and finally obtaining sample sheets requiring the stock layout; 2, extracting contour points of a material and feature points of the sample sheets, and judging the overlapping relationship of the sample sheets and the material by a downwards sinking left and right dispersed stock layout algorithm; 3, performing an improved PSO algorithm searching process. A plurality of factors are added into the PSO algorithm; the factors are continuously changed according to a certain rule, so that the particle swarm has higher global and local searching capability in each stage, and the local optimum is avoided; and when the stock layout effect meets the requirements or the number of iteration times reaches the set value, the global optimum stock layout scheme is used as the final stock layout scheme. The irregular part stock layout method based on the multi-factor particle swarm algorithm provided by the invention has the advantages of high global searching capability, high local searching capability, good convergence property and good stock layout effect.

The invention discloses a Macpherson suspension hard point coordinate optimization method based on an inner layer and outer layer nested multi-objective particle swarm algorithm. The method comprises the following steps: 1, building a multi-objective optimization model for Macpherson suspension hard point coordinates; 2, solving the multi-objective optimization model through the inner layer and outer layer nested multi-objective particle swarm algorithm, thus obtaining a multi-objective optimized Pareto solution set front edge; 3, carrying out weighting treatment on a change range of each locating parameter of a front wheel, and building an evaluation function on the change ranges of the locating parameters of the front wheel, thus selecting the optimal hard point coordinates from the Pareto solution set front edge according to the evaluation function. According to the Macpherson suspension hard point coordinate optimization method based on the inner layer and outer layer nested multi-objective particle swarm algorithm, the change ranges of the locating parameters of the front wheel can be effectively reduced when mechanical parameters of a suspension are not changed, thus substantially improving the operation stability of an automobile; meanwhile, the automobile still can obtain good operation stability when the mechanical parameters of the suspension are changed, thus effectively guaranteeing the robustness of the optimal design of the suspension hard point coordinates.

The invention discloses a shaft system thermal error modeling method based on an SLSTM neural network. The method comprises the following steps: 1) inputting thermal error data of a shaft system changing with time; 2) decomposing the thermal error data into N intrinsic mode components and a residual component by using an EMD algorithm, and respectively converting the component data into a three-dimensional input matrix; 3) encoding the initial time window size, the batch processing size and the unit number of each piece of component data to obtain an original generation bat population; 4) initializing the original generation bat population by adopting a BA algorithm to obtain SLSTM neural networks with different time window sizes, different batch processing sizes and different unit numbers; 5) training the SLSTM neural network by using the thermal error data of the shaft system to determine hyper-parameters; and constructing an EMD-BA-SLSTM network model by using the optimal hyper-parameter, and then reconstructing a prediction component to obtain the output of a prediction result, i.e., the invention also discloses a shaft system thermal error compensation system based on the SLSTM neural network.

The invention discloses a GEO (Geosynchronous orbit)-UAV (unmanned aerial vehicle) Bi-SAR (synthetic aperture radar) route planning method based on differential evolution. The GEO-UAV Bi-SAR route planning method based on differential evolution comprises 1) generating a three dimensional landform; 2) modeling a UAV accepting station route; 3) modeling the route planning as a multiobjective optimization problem for a constraint condition; 4) utilizing a multiobjective differential evolution algorithm to solve; and 5) obtaining the optimal solution, generating a UAV optimal path, and realizing autonomous navigation and Bi-SAR imaging of the UAV in the three dimensional complicated landform. The GEO-UAV Bi-SAR route planning method based on differential evolution models the UAV route planning problem which comprehensively considers the route length, the flight safety and the SAR imaging performance as a multiobjective optimization problem, and utilizes the improved differential evolution algorithm to solve and obtain a set of optimal UAV accepting station flight routes.

The invention provides a routing method of a small-scale wireless sensor network, belongs to the field of routing of a wireless sensor network, and solves the problem that a Harmony search algorithm in the prior art cannot be directly used for solving the routing of the wireless sensor network. The routing method comprises a step of transmitting global information, a step of sending data packet, a step of transmitting the data packet and a step of updating rest energy information. Through improving the encoding manner and the candidate Harmony generation method of the existing Harmony search algorithm, with the consideration of both path energy consumption and path length, the energy consumption of the whole network can be balanced effectively, and the service life of the whole network is prolonged significantly.

Embodiments of the invention provide a relay satellite task scheduling method and apparatus. According to the method, after a preset relay satellite task scheduling restraint planning model is obtained, the model is solved by adopting an adaptive genetic simulated annealing algorithm to obtain an optimal scheduling scheme. Compared with a mode for solving the model by a conventional genetic algorithm, the method provided by the embodiment of the invention solves a maximum value of the restraint model by utilizing the adaptive genetic simulated annealing algorithm, has the advantages of good global search capability, high convergence speed and the like, and is more suitable to solve a high-complexity task.

The invention discloses a subway train energy-saving optimization method based on an improved genetic algorithm, and the method comprises the steps of firstly, building a train energy consumption model according to the conservation of energy consumption, setting the constraint conditions, and solving a train energy-saving operation strategy through the improved genetic algorithm, wherein the operation strategy is concretely solved by the two stages of at the first stage, taking the speed, acceleration, time and the like of each working condition of a train as genes, combining the genes into achromosome, namely a solution, solving the speed and distance of a conversion point of each working condition, and determining an optimal operation curve; and at the second stage, solving the maximumoverlapping time of multi-train operation traction and braking, determining the regeneration energy utilization rate, and obtaining the optimal operation departure strategy of the train. The method isbased on the complex lines and conforms to the actual operation condition of the train, the adopted solving method is fast in speed, high in precision and complementary in length, the global searching capacity and the local searching capacity are fully utilized, and the total operation energy consumption of the subway train is effectively reduced with the Nanning subway line 1 as an example verification.

The invention brings forward a novel image registering method. The method comprises the following steps: establishing gray-scale-based mutual information registering adaptation value function; establishing and initializing a group, wherein four dimensions of each individual in the group respectively represents horizontal translation, vertical translation, a rotation angle and a zoom coefficient of a floating image; according to the mutual information registering adaptation value function, calculating a fitness value of each individual, and calculating an optimal position of the whole group; by use of an iteration mechanism of a differential evolution algorithm, updating a position vector of each individual, and updating the optimal position of the whole group; determining whether conditions for executing an alternative strategy is satisfied, and if so, executing the corresponding alternative strategy; and repeatedly executing the aforementioned steps until maximum iteration frequency Tmax of the differential evolution algorithm is satisfied. The novel image registering method has the advantages of good registering stability and high precision, greatly improves the performance of an image registering algorithm and lays a reliable foundation for subsequent image processing work.

The invention discloses a short-term peak regulation scheduling collaborative optimization method and system for a cascade hydropower station group, and belongs to the fields of efficient water resource utilization and water and electricity dispatching. The method comprises the following steps of randomly generating an initial population, and then evaluating the fitness value of each individual and updating an individual extreme value and a global extreme value; utilizing the gauss neighborhood search to improve population global exploration capability, utilizing an elitist guiding strategy toenrich the evolution directions, utilizing a random variation strategy to improve the individual diversity, repeating the above process until a search stopping condition is met, and using a population global extreme value obtained when the maximum iteration number as an optimal scheduling process of the cascade hydropower station group. Compared with a traditional hydroelectric power dispatchingmethod, the method has the advantages of being high in convergence speed, low in programming implementation difficulty, high in global searching capacity and the like, a reasonable and feasible dispatching scheme can be rapidly obtained, and an effective method is provided for the short-term peak regulation dispatching of the cascade hydropower station group.