218results about How to "Avoid premature convergence" patented technology

The invention relates to a vehicle route optimization method with time window constraint based on improved particle swarm optimization (PSO). The method comprises the following steps of: step 10, initially setting parameters; step 20, constructing a particle swarm; step 30, decoding particles according to a decoding rule; step 40, calculating fitness for a distribution route obtained by decoding; step 50, searching an individual optimal state and a group optimal state to select the group optimal state Pg, which is the optimal route of the vehicle route problem under the current iteration conditions, determining the condition that the optimal position searched by the particle swarm is the optimal route in the current state, entering step 601 if a stop condition is not reached, and otherwise, entering step 603; step 601, updating the state; step 602, introducing crossover operator operation, and entering step 30 to repeat particle decoding; and step 603, stopping iteration, and outputting the optimal route result of the vehicle route problem.

The invention provides a method for determining an optimal route of the airway of an unmanned aerial vehicle. According to the method, the threat of an operation area is more sufficiently considered, more efficient global searching ability is achieved and a more accurate flying route is provided for the unmanned aerial vehicle. The method comprises the following steps: by adopting a quantum encoding mode, changing the state of a basic quantum bit by using a quantum rotating gate and a quantum not-gate, and further updating the position of a bat individual. Because of the diversity of the quantum state, a quantum bat algorithm (QBA) is relatively high in global searching ability and an available or even optimal route avoiding the threat and limiting conditions can be found for the unmanned aerial vehicle. The experiment result shows that the quantum bat algorithm is an effective and stable method for solving the airway route planning problem of the unmanned aerial vehicle, and the search performance of the quantum bat algorithm is superior to that of other swarm intelligence algorithms.

The invention relates to the on-line detection technical field by utilizing machine vision and an image processing technology, in particular to an automatic on-line detection method and a device for size of automobile parts based on machine vision, aiming at solving the problems that the labor intensity is high and the detection quality is poor by adopting an artificial on-line detection method for size of automobile parts. An industrial camera is utilized for shooting a clear, complete and flaw-free standard image for the automobile part running in an automatic production line, the image is utilized as a standard image template and is stored in a computer, the precision range of detection parameters for the automobile part is set according to user requirements, the image of the on-line running automobile part, which is shot in real time by the industrial camera, is transmitted to the computer and is compared with the standard image template and is processed, the size of the automobile part can be computed, and if the precision of the part is lower than the setting requirements, the computer starts and gives an alarm, so as to prompt operational staff to treat inferior-quality products. The method and the device have high detection precision to the automobile parts and have rapid speed, so as to greatly reduce the labor intensity for artificial detection.

The invention discloses a server performance prediction method based on a particle swarm optimization nerve network, and belongs to the technical field of computer performance management. The performance of a server in cloud computing is predicted based on an improved Elman nerve network. Firstly, the number of nodes of an input layer of the Elman nerve network according to relevance of sample data; and secondly, the Elman nerve network is trained by a PSO (particle swarm optimization) algorithm based on particle swarm distribution. The concept of particle aggregation degree is introduced in the PSO algorithm based on particle swarm distribution, a particle swarm is scattered when the aggregation degree is high, diversity of the particle swarm is kept, and the optimizing capacity of the algorithm is improved. Fine precision of a prediction model in short-term prediction and long-term prediction is kept, and the training speed of the nerve network is increased.

The invention belongs to the computer field, and discloses a method for scheduling a flow shop based on a multi-swarm hybrid particle swarm algorithm, which solves the problems that the flow shop scheduling method based on the hybrid particle swarm algorithm is easy to result in premature convergence and local optimum. The method comprises the following steps of: setting parameters and generating S sub-swarms; judging whether the terminal condition is satisfied, if so, outputting a current optimum scheduling scheme, otherwise, updating positions of particles in each sub-swarm with the particle swarm algorithm, carrying out a local search on odd and even sub-swarms respectively by using searching operators 1 and 2 to obtain an optimum scheduling sequence of each sub-swarm; sharing information of the obtained optimum scheduling sequence by using a statistics-based probability model; and optimizing an optimum working sequence with a simulated annealing algorithm. In the invention, multiple swarms are added, the local search is carried out by using different searching operators, a good flow shop scheduling scheme is obtained, the production time is shortened, and the method can be used for the selection of the job shop scheduling scheme.

The invention relates to a real-time path planning method of AUV (Autonomous Underwater Vehicle), in particular to a method for carrying out online, real-time local path planning according to an online map in an AUV real-time collision preventation process. The method comprises the steps of: setting the quantity of small populations according to the quantity of path points of the AUV, initializing; carrying out immune selection on each small population to obtain subgroups; carrying out genetic manipulation on one subgroup, carrying out cell cloning on the other subgroup; then clustering through a vaccination and an antibody to form the next generation of small population, judging whether the next generation of small population meets the conditions or not; if yes, selecting optimal individuals of the small populations; and selecting the optimal individuals from the set consisting of all optimal individuals to be used as a planning path. According to the invention, the diversity of the population is maintained by using an antibody clustering principle, the premature convergence of an algorithm is avoided, and the global optimization is facilitated. The established immune genetic algorithm is used for clustering and analyzing generated filial generations by adopting a self-regulating mechanism, and the diversity of the population is ensured.

The invention discloses an improved fuzzy neural network bus intelligent scheduling method based on a chaos theory, and belongs to the field of intelligent transportation. According to the improved particle swarm bus intelligent scheduling method based on the chaos theory, advantages and complementarity of various algorithms are fully utilized, a series of improvement measures are also introduced, such as conjugate gradient optimization, and inertia factor and constraint factor of the particle swarm algorithm etc., the mechanism and the search performance are researched from the theoretical and practical perspectives, problems of poor global search capability and premature convergence of the conventional optimization algorithm are fundamentally solved, the diversity of population can be obviously increased, the global search capability is obviously improved, the problem of fuzzy information can be effectively dealt with, the convergence speed is fast, and a new high-efficiency method is provided for bus intelligent scheduling.

The invention relates to a chaotic particle swarm optimization-based orthogonal frequency division multiplexing (OFDM) system resource allocation algorithm and belongs to the technical field of system resource allocation. The invention provides a utility function maximized framework-based resource allocation algorithm aiming at the power allocation problem in a wireless multi-user orthogonal frequency division multiplexing (OFDM) system. In an actual network environment, the optimized algorithm is non-convex and a classical optimization method is difficult to solve the problem. Therefore, a particle swarm method in intelligent optimization is applied to non-convex optimized algorithm design. Logistics chaotic searching is embedded into a PSO algorithm aiming at the problem that particle swarm optimization easily has a local extremum point, and the chaotic particle swarm algorithm is provided. Compared with like algorithms, the novel algorithm effectively solves the non-convex problem, can make the system have better performance, and can effectively prevent the particle swarm algorithm from having a local solution.

The invention provides a polyclone artificial immunity network algorithm for multirobot dynamic path planning, and relates to an improved polyclone artificial immunity network algorithm. According to the polyclone artificial immunity network algorithm, a polyclone artificial immunity network is applied to multiple mobile robot dynamic path planning, mutual influence among robots and influence on the robots of mobile barriers are considered, a computational formula of antibody concentration is defined, diversity of antibodies is increased through clonal operators, crossover operators, mutation operators and selection operators, and the problem of premature convergence of a traditional immune network is solved. Specific antibodies corresponding to antigens in a specific environment are stored, initial concentration of the specific antibodies is increased, response time is shortened, and multiple mobile robot dynamic path planning in an unknown environment is effectively achieved.

The invention discloses a multi-objective optimization method based on an improved gravitation search algorithm. According to the algorithm, a memory strategy is introduced into a universal gravitation search algorithm, so that particle swarm information and information of previous generations and next generations of particles are shared, the global search capability and the local search capability of the particles are balanced, and the premature convergence problem is solved. On this basis, a diversity enhancement mechanism is introduced into the algorithm, namely, particle speed and position are controlled each iteration, so that the diversity loss is relieved, the particle diversity is improved, and diversity and distributivity of non-dominated solution sets are enhanced. Therefore, by means of the multi-objective optimization method based on the improved gravitation search algorithm, the phenomenon that multi-objective optimization is caught in a local extremum can be effectively avoided, and convergence, diversity and distributivity of non-dominated solutions are remarkably improved when the gravitation search algorithm is applied to the field of multi-objective optimization.

The invention discloses an improved adaptive genetic algorithm based neural network image classification method. The method comprises the following steps: extracting texture features of sample images by adopting a gray-level co-occurrence matrix based feature extraction algorithm to obtain texture features of a training sample and a test sample; by taking the texture feature of the training sample as an input of an RBF neural network, training the RBF neural network with a genetic optimization based neural network learning method to generate a trained RBF neural network; and inputting the texture feature of the test sample into the trained RBF neural network, and performing an image classification test. For the deficiency that a k-means clustering algorithm and other algorithms are sensitive to initial value selection, the method can better avoid the ''prematurity'' convergence of a genetic algorithm, simplify the network structure of a neural network classifier and improve the generalization capability of the network and the correct classification efficiency of the images.

The invention discloses an improved particle swarm optimization (PSO) algorithm of solving the zero-waiting flow shop scheduling problem. Firstly, parameter initialization and population initialization are carried out, wherein initial workpiece sequences are generated, then a factorial encoding method is used to map all permutations to integers to form an initial population, and finally, a feasible initial velocity set is randomly generated; particles are moved; the population is updated through an original PSO population updating strategy, a new population is mapped to corresponding workpiecesequences, and work completion time of each new workpiece sequence is evaluated; an improved variable neighborhood search (VNS) algorithm is used for a local search, and results obtained by the search are used for replacement; a population adaption (PA) operator is used to increase diversity of the population; and checking of a termination condition is carried out, if the termination condition ismet, a process is stopped, and values of variables and corresponding sequences are returned to be used as a final solution, and otherwise, particle velocity is continuously updated. The method has the advantages of improving a particle swarm optimization algorithm, improving global search capability, and avoiding too early convergence.

The invention discloses an intelligent diagnosis method targeting the rotation machinery early-stage fault. The intelligent diagnosis method comprises steps of performing time domain, frequency domain and time frequency domain signal processing on the vibration signal of the rotation machinery on the basis of the vibration signal in the operation process of the rotation machinery, constructing a multi-core support machine as a novel intelligent diagnosis model on the basis of a typical local core function and a global core function, constructing a heredity annealing algorithm on the basis of a heredity algorithm and a heredity annealing algorithm, and using the heredity annealing algorithm to optimize the model parameter of the multi-core support vector machine to implement the multiple parameter parallel optimization. The invention fully takes the advantages that the mixing domain characteristic set performs fault gradual characteristic extraction at the early stage of the rotation machinery performance degeneration, the heredity annealing algorithm performs parallel optimization in the parameter and the multi-core support machine can perform early stage fault diagnosis, can effectively perform diagnosis identification on the early stage fault for the rotation machinery device and has a strong interference resistance capability and a capability of wide popularization.

The invention discloses an auto-disturbance rejection position servo system optimization design method based on an improved CPSO. By aiming at problems of permanent magnet synchronous motor servo systems on high position control precision, fast response, and stable performance, a double-loop control structure is adopted, and a PMSM auto-disturbance rejection position servo control system is established. By aiming at a parameter setting problem of an auto-disturbance rejection position controller, the improved Chaos Particle Swarm Optimization (CPSO) is provided. By adopting the CPSO, a position of a particle is initialized according to cubic chaotic mapping, and an index self-adaptive way having adjustable parameters is used to adjust inertia weight in a non-linear way, and at the same time, the position of the particle is updated by adopting a chaos and stability alternate way, and therefore the convergence rate and the global optimization ability of the CPSO are effectively improved, and the CPSO is used for the optimization of the auto-disturbance rejection position controller parameters. By combining with a fitness function including position control requirements, the optimization design of the PMSM position servo control system is realized, the position control precision and the response speed of the servo system are improved, and a strong disturbance rejection ability is provided.

The invention proposes a traffic flow prediction method based on firefly algorithm and RBF neural network. The method comprises: performing normalization to the sample data so that the input data and output data are on the same order of magnitude; initializing the firefly algorithm parameters; utilizing the random method to initialize the firefly populations and encoding each individual in the populations; using the firefly algorithm to train the RBF neural network to obtain the best individual in the populations; decoding the best individual in the populations to obtain the trained RBF neural network; and utilizing the trained RBF neural network to predict the traffic flow data sample. Compared with the traditional traffic flow prediction method, the method of the invention makes full use of the advantages of the firefly algorithm in the RBF neural network training so that the RBF network possesses a more accurate prediction capability, achieves even faster training efficiency and better generalization capability. The invention belongs to the traffic transportation information engineering technology field and can be used for the predictions of road traffic flows in an intelligent traffic system.

The invention discloses a self-adaptive frog cluster evolutionary tree designing method used for electronic medical record attribute reduction. Firstly, an evolutionary target optimization model for the electronic medical record attribute reduction is established; then an electronic medical record attribute set is divided into evolutionary subtrees according to the optimal selectivity set of the evolutionary subtrees, and a self-adaptive frog cluster dynamic optimizing structure is designed to optimally select each evolutionary subtree elitist; an electronic medical record attribute subset is distributed to each evolutionary vector, evolution is carried out respectively by each elitist and shuffled frog leaping algorithm (SFLA), the best target fitness on a medical record evolutionary subtree is worked out, and the optimal fitness is searched among the evolutionary subtrees for sharing; and finally the optimal attribute reduction set on each medical record attribute evolutionary subtree and the global optimal attribute reduction set are worked out, and whether the medical record attribute reduction rate meets the reduction precision or not is judged. The method disclosed by the invention has the advantages of easiness in construction, higher evolution convergence rate, higher minimum attribute reduction efficiency and precision and the like.

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 discloses an optimal configuration method for an electric automobile charging pile. The method comprises the following steps: predicting the charging power demand of a planning area by a Monte Carlo simulation method on the basis of analysis of various electric automobile behavior characteristics; building a bi-level planning model of charging station investment profit and user perception effect under the consideration of constraint conditions such as a power grid, a charging station and an investor budget; and introducing a KKT (Karush-Kuhn-Tucker) condition to realize equivalent conversion of a double-layer model and a single-layer model, and solving by adopting a variable neighborhood search-particle swarm mixed algorithm with a convergence polymerization degree. Through adoption of the method, the problem of premature convergence of particles is avoided effectively; population diversity is increased; the optimization capacity of the particles and the convergence speed of the algorithm are improved and increased remarkably; the calculation speed and the calculation accuracy of optimal configuration of the charging station are increased; and important references are provided for investors to plan and build the charging station under an enterprise-dominant pattern.

The invention relates to a multi-population simulated annealing hybrid genetic algorithm based on similarity expelling. The multi-population simulated annealing hybrid genetic algorithm includes the following steps: coding is carried out; initialization parameters are set; initial populations are created; fitness values are calculated; selecting operation is carried out; interlace operation is carried out; mutation operation is carried out; gene overturning operation is carried out; simulated annealing Metropolis rules are judged; migration operation based on similarity expelling is carried out; optimal storage is carried out; judgment is ended. The migration operation based on similarity expelling particularly includes the following steps: calculating the fitness values of individuals in a source population and a target population; selecting the individual with the largest fitness value from the source population to serve as the individual to be immigrated; conducting similarity calculation; conducting expelling replacement. The multi-population genetic algorithm with simulated annealing operation can improve the local search capability of the multi-population genetic algorithm, and the algorithm can search for approximate solutions even though optimal solutions to a larger extent. The individual similarity judgment is additionally carried out, attention is paid to differences between the individuals, the diversity of populations is maintained, premature convergence of the genetic algorithm is avoided, the solving quality of the algorithm is improved, and the algorithm is closer to the optimal solutions.

The invention provides an independent photovoltaic dragging system capacity configuration method based on a multi-target optimization algorithm. The method comprises steps of: step 1, determining a topology structure of an independent photovoltaic dragging system; step 2, setting an energy control strategy of the independent photovoltaic dragging system; step 3, establishing an optimal configuration module; step 4, inputting a basic parameter of the independent photovoltaic dragging system; and step 5, according to the established optimal configuration module, using an improved NSGA-II multi-target optimization algorithm to solve an optimal solution set, analyzing the optimal solution set and carrying out weight allocation on the target function of each solution so as to calculate the finial configuration result. According to the invention, when electricity utilization requirements of users are met, environment load is reduced.

The invention discloses a particle swarm optimization manufacturing system double-target production scheduling method based on a bionic strategy, and the method comprises the steps: firstly building amixed flow shop scheduling mathematic model, and determining a scheduling process constraint condition and a target function needing to be solved; proposing particle encoding and decoding based on matrix expression; proposing a speed updating rule based on a hormone regulation mechanism; and proposing a particle swarm optimization algorithm based on a bionic strategy, solving the workshop scheduling model and obtaining a scheduling scheme. The invention provides a particle swarm optimization manufacturing system double-target production scheduling method based on a bionic strategy. Accordingto the system, resource arrangement, capacity balance, quality management, cost and delivery time of enterprises can be controlled, problems on a production line are analyzed and explored, correct technology and management decisions are made for informatization, standardization and automatic construction of the enterprises, and therefore the operation efficiency of the manufacturing enterprises isimproved, and benefits are obtained to the maximum extent.

The invention provides a brain part MRI image segmentation method. The brain part MRI image segmentation method is characterized in that a gray level image of a brain part MRI image to be segmented can be acquired; the gray values of different pixel points of the brain part MRI image can be used as the clustering centers, which are used to form the clustering center sets as the particles, and the optimization of the clustering center sets can be carried out by adopting the particle swarm optimization algorithm; every pixel point of the brain part MRI image belongs to the category having the maximum membership, and then the gray values of the pixel points of the same category are equal to the same gray value, and the brain part MRI image segmentation can be completed. The brain part MRI image segmentation method is advantageous in that according to the chaotic characteristic and the logic self-mapping function, the uniformly-distributed particle swarms can be initialized by adopting the logic self-mapping function, and then the quality of the initial solution, the stability of the PSO algorithm, the speed and the precision of the image segmentation can be improved; the chaotic searching can be carried out, when the particles are in the premature convergence state, and the premature convergence phenomenon caused by the stagnated state of the particles during the iteration process can be prevented, and the optimal solution in the range of the whole situation can be realized, and then the speed and the precision of the image segmentation can be improved.

The invention discloses a method and device of environment and economic dispatching based on a multi-objective hybrid bat algorithm for a power system. The method includes the steps of determining anoptimization target and constraint conditions, and constructing a multi-objective optimization model of the environment and economic dispatching for the power system; solving the multi-objective optimization model using the multi-objective hybrid bat algorithm under the condition that load requirement of current period is met, obtaining a group of non-dominant solutions, and forming a Pareto optimal frontier for the two optimization targets of the environment and economic dispatching; obtaining a global optimal solution as the basis for an optimal dispatching of the power system during the current period using a membership function for the non-dominant solutions. Under the precondition of satisfying electricity load, the method minimizes generation cost of a generating set and minimizes emissions of polluted gas, minimizes use of fossil fuels to a large extent, and provides intelligent decisions for energy saving and synergy of power enterprises.

The invention discloses a regional comprehensive energy system optimization operation method based on a repeated game model. The method comprises the steps of firstly, performing steady-state modelingand power flow analysis on a power distribution network, a gas distribution network and a micro-energy network in a regional comprehensive energy system; then, considering the interaction influence between a power link and an energy coupling link in the regional comprehensive energy system; using a micro-energy network and a power distribution network as game participants; constructing a repetitive game optimization model of the regional comprehensive energy system by taking the daily operation cost of the micro-energy network and the comprehensive satisfaction of the power distribution network as respective utility functions, and solving the repetitive game optimization model by adopting an adaptive mutation particle swarm algorithm to obtain a game equilibrium optimization result of theregional comprehensive energy system; and finally, verifying the correctness and effectiveness of the regional comprehensive energy system optimization operation method based on the repeated game model. The method can give full play to the active regulation and control effect of the power distribution network, gives consideration to the benefits of the micro-energy network and the power distribution network, and achieves the cooperative economic optimization operation of the regional integrated energy system.

The invention is applicable to the technical field of workshop production planning management, and provides a hybrid genetic algorithm-based MES (Manufacturing Execution System) production planning and scheduling method. According to an order priority generation mode and / or random generation mode, a workshop task sequence which meets constraint relations between tasks and a genetic algorithm coding rule is generated for a preset number of initial scheduling schemes; according to the best task starting and ending time and according to one or more distribution combination modes in resource load balancing principle distribution and random distribution, execution resources are set for each workshop task in the preset number of initial scheduling schemes; the preset number of initial scheduling schemes are converted into a series of chromosomes through a coding process to serve as an initial population for the hybrid genetic algorithm; and the initial population is introduced to the hybrid genetic algorithm, and a scheduling result after optimization is calculated according to a preset optimization target. High efficiency of the MES production planning and scheduling results in the prior art is improved.

A low energy snow making gun having at least one but preferably three operational stages each having at least one pair of small aperture water outlets which are oriented at a divergent angle to generate a respective pair of narrow angled water droplet streams which do not interfere with each other until they have reached a distance from the gun. A second pair of water outlets may be provided on each stage with each pair on each stage oriented at a divergent angle to maintain singularity of the streams over a distance thereby increasing the throwing power of the gun.

The invention belongs to the technical field of hydroelectric generation, and particularly relates to improved TLBO (teaching-learning-based optimization) algorithm-based hydroelectric generating set PID (proportion-integration-differentiation) speed regulator parameter optimization. The optimization comprises the following steps of (1) building a hydroturbine speed regulating system simulation model; (2) improving a basic TLBO algorithm; (3) applying the improved TLBO algorithm to optimizing parameters of the speed regulator of a hydroturbine speed regulating system, and obtaining a simulation result. Self-adaptive teaching factors, i.e., absorption weight of students and the after-school tutoring of teachers are added into the basic TLBO algorithm, while the convergence speed and the convergence precision are guaranteed, the phenomena of early-maturing and early convergence of the algorithm are avoided. An ITAE index of rotation rate deviation of a hydroturbine set serves as a standard fitness function, and the improved TLBO algorithm is used to optimize the parameters of the speed regulator, so that the convergence speed optimization efficiency is obviously improved, and the phenomenon of local optimum is avoided.

The invention provides a quantum genetic algorithm-based converter transformer partial-discharge ultrasonic location method. Many ultrasonic sensors at different positions of a transformer are adopted to receive ultrasonic signals that are sent by a partial discharge source, and a distance solving model is established by a Cartesian coordinate system. Relevant parameters of the quantum genetic algorithm are initialized. Chromosome is coded, and population Q(t) is initialized. Every individual of the initialized population is measured and a state P(t) is obtained. The fitness to each state is calculated. The optimal individual and its fitness value are recorded. The result is directly output if the termination condition is satisfied. If the termination condition is not satisfied, then t=t+1 is set and every individual of the initialized population is measured to obtain the state P(t). The fitness to each state is calculated, the population individuals are updated by means of quantum rotation gate operation and quantum non-gate to obtain a progeny population Q(t+1), and the optimal individual and its fitness are recorded, until the terminal condition is satisfied. The method of the invention has the characteristics that the iteration frequency is low and the location precision is high under the condition of a small population size, premature convergence is avoided, and rapid convergence is achieved to get the globally optimal solution.

The invention discloses an ant colony algorithm-based firepower distribution method. The method comprises the steps of firstly building an air combat threat degree model and a firepower distribution decision model according to a fight situation of both sides; and secondly, in the aspect of model solving, performing algorithm improvement for deficiencies of a typical ant colony algorithm: improving the ant colony algorithm in combination with thoughts of typical ant colony system and max-min ant colony system algorithms, so that the improved ant colony algorithm is more reasonable in early evolution trend and higher in convergence speed, and can be better prevented from falling into local optimum. The improved ant colony algorithm proposed for firepower distribution not only can be used for firepower distribution of an air combat but also can be expected to be used for other combination optimization problems such as decision problems of firepower distribution and the like in an attack battle of ground tank groups and a maritime warship formation combat.

The invention discloses a vehicle route optimization method. The vehicle route optimization method includes that defining a vehicle route problem into a graph model, solving an inter-cluster cost route from a global view to acquire a feasible solution space, and optimizing the feasible solution space through a Monte Carlo method, genetic manipulation, a quantum rotating gate adaptive strategy and the like. The vehicle route optimization method is capable of solving the vehicle route problem in the global connection, optimizing the time and space complexity in the problem solving process, and avoiding premature convergence. The vehicle route problem refers to that a certain number of customers have different numbers of goods demands, a distribution center provides goods for the customers, one motorcade is responsible for distributing goods, and proper driving routes are organized to meet the demands of the customers and achieve the aims of shortest journey, lowest cost, shortest time consumption and the like under a certain constraints.