213 results about "Chromosome (genetic algorithm)" patented technology

The invention discloses a path planning and obstacle avoidance method for an unmanned ship formation, and relates to the technical field of formation path planning. Decoding the quantum chromosome into an actual path by using a quantum genetic algorithm, screening out an optimal path, and discretizing the optimal path to obtain a global navigation path; a follower navigation path is constructed according to formation characteristics and a reference formation, the initial formation is adjusted when the follower starts, a communication link with a navigator is established at the same time, and the follower carries out local obstacle avoidance by using an improved artificial potential field method to carry out local formation adjustment; when a dynamic obstacle is detected, the stress is recalculated by using an improved potential field method, and the local path of the re-planned road section is optimized; and judging whether a target point is reached or not, if so, ending, and if not, circularly executing until the target point is reached. According to the invention, the formation system keeps stable formation in a dynamic unknown environment, and the targets of safe obstacle avoidance and optimal path are achieved.

The invention discloses a satellite communication method and device and a storage medium. The satellite communication method comprises the following steps: determining the suitability between different sub-bands of a satellite and different beams of the satellite; constructing a chromosome population formed by a plurality of chromosome vectors; according to the suitability, initializing the chromosome vector; a fitness function is constructed, and the fitness function is used for indicating the severity degree of same-frequency interference occurring in the multiple coverage areas after sub-bands are allocated for all the beams according to the chromosome vectors; performing iterative optimization on the chromosome population through the fitness function according to a genetic algorithm, and determining an optimized chromosome vector; and according to the optimized chromosome vector, allocating a corresponding sub-band to each beam, and carrying out satellite communication. Therefore, the distributed sub-band with the lowest fitness can be determined for each beam, and interference between same bands is avoided.

The invention discloses a UUV cluster dynamic task planning method based on a multi-target genetic algorithm, a program, equipment and a storage medium, and belongs to the field of underwater multi-UUV cooperative detection of multiple targets. According to the method, firstly, for path planning of regional task points, chromosome representation is completed through sequential coding, chromosomes are selected and ranked randomly, and the chromosome with the highest fitness value is selected from each group; then, in each group of the current population, selecting an optimal parent individual to carry out crossover and mutation operations so as to improve the quality of offspring individuals, and carrying out updating to obtain a next-generation population; and finally, obtaining a plurality of optimal individuals, and outputting a task planning path result. Dividing the search area into a plurality of task sub-areas, and completing the dynamic task planning of the UUV cluster according to the target distribution condition of the sub-areas and the condition of each UUV. According to the method, the search strategy can be adjusted in real time according to the real-time detection result and the environment change, so that the target distribution non-uniformity and the cluster efficiency difference are effectively relieved.

The invention provides an HC-MOGA-based agent task collaborative scheduling method in an industrial internet, and aims to solve the key problems of low scheduling efficiency, poor system robustness and the like caused by heterogeneous types of equipment (subsequent modeling is an agent), complex task dependence and non-uniform resource distribution in an industrial site. According to the method, a heterogeneous agent scheduling model with task identification, resource matching and dependent modeling capabilities is constructed for a multi-stage operation task cooperatively completed by a data acquisition agent and an operation type agent in an industrial internet environment. An HC-MOGA (heterogeneous constraint multi-objective genetic algorithm) is provided, multi-dimensional chromosome coding, a layered mapping mechanism, a dual-stage variation strategy and a dynamic constraint repair mechanism are introduced, and a dual-objective optimization model fusing work completion income and comprehensive resource consumption and potential loss is established. The method is suitable for industrial internet typical application scenes such as intelligent manufacturing, automatic production lines and multi-agent task collaborative management.

The invention relates to the field of white carbon black modifier production, and particularly discloses an activity monitoring and early warning method of a white carbon black modifier based on Internet of Things, which comprises the following steps: acquiring reaction temperature, catalyst dosage and equipment operation parameters in real time through an Internet of Things sensor, and constructing a parameter and efficiency mapping table to form a multi-dimensional process database; encoding the temperature, the stirring rate and the raw material ratio into chromosome genes by utilizing a genetic algorithm, and constructing a multi-target model by taking the bifunctional grafting rate as an optimization target and combining the equipment load rate and the energy consumption constraint; a high-adaptability parameter set is generated by adopting iterative optimization, and a three-level response mechanism of local fine tuning, population resetting and historical optimal parameter loading is realized in combination with a hierarchical early warning system. According to the method, the problems that the synthesis conditions of the bifunctional silane coupling agent cannot be accurately controlled and the continuous production data response is delayed in the traditional process are solved, and the activity dynamic optimization and green and efficient production of the white carbon black modifier are realized.

The invention provides a workshop scheduling method and system fusing a decision tree and a genetic algorithm. The method comprises the following steps: firstly, collecting operation process and machine information through an ERP system and extracting related features; generating a scheduling scheme by using historical orders of an ERP system or manually added orders, and constructing a training set training decision tree to accurately judge machine allocation conflicts; constructing a multi-target flexible job shop scheduling model, and constructing a target function based on a hierarchical Pareto dominance relationship; and secondly, realizing job-shop scheduling scheme coding by adopting double-layer chromosome coding, carrying out selection, intersection and mutation operations in combination with a genetic algorithm, carrying out conflict detection and repair by utilizing a decision tree, solving an objective function, continuously iterating until a convergence condition is met, and outputting a Pareto optimal solution. According to the workshop scheduling method, the scheduling efficiency and feasibility are improved by establishing a data model of a multi-target flexible job workshop scheduling problem and through dynamic conflict detection, hierarchical multi-target optimization and a closed-loop feedback mechanism.

The invention discloses a satellite beam scanning scheduling method and device and a storage medium. The method belongs to the field of satellite communication, and comprises the following steps: acquiring parameter characteristics of coverage areas of multiple beams of a satellite; inputting the parameter features into a pre-trained neural network model, and determining sub-bands adapted to the plurality of beams through the neural network model; a chromosome vector is initialized, an initial population is constructed, and the chromosome vector is used for indicating the beam position scanned by each beam of the satellite in each beam hopping time slot of the beam hopping period; constructing a fitness function; performing iterative optimization on the initial population according to a genetic algorithm, and determining an optimal chromosome vector; and according to the optimal chromosome vector, determining the beam position scanned by each beam in each beam hopping time slot of the beam hopping period. Therefore, coverage overlapping and sidelobe leakage interference of same-frequency beams are avoided to the maximum extent from the scanning time sequence dimension, and dual targets of sub-band adaptation and interference suppression are synchronously achieved.

The invention relates to the technical field of panel processing, in particular to a panel furniture workpiece typesetting method based on an improved genetic algorithm, which comprises the following steps: acquiring parameter information of a to-be-typeset workpiece; and performing classification based on the parameter information of all the workpieces to be typeset to obtain a plurality of workpiece categories, endowing each workpiece category with a unique type code, constructing an initial population according to the category and the number of the workpieces to be typeset, and inputting the initial population into the improved genetic algorithm to obtain an optimal chromosome. According to the method, the workpieces with the same size and shape are classified into workpieces with the same specification, and a repeated gene coding mode is adopted, so that the defect that actual repeated chromosomes are generated when a plurality of workpieces with the same specification are processed in a traditional unique coding mode is effectively avoided, and the algorithm efficiency is improved. And secondly, in the gene crossover operation, an improved ordered crossover and order crossover strategy is introduced, and a random deletion mechanism is combined, so that the algorithm population diversity and the solution space coverage are effectively improved, and the search divergence and convergence efficiency are improved.

The invention discloses an industrial chain typical power utilization mode extraction method fusing heredity and a K-Means clustering algorithm, and belongs to the technical field of power system data analysis and mode recognition. The method comprises the following steps: firstly, carrying out standardized preprocessing on power consumption data, wherein the standardized preprocessing comprises missing value filling, abnormal value processing, feature normalization and redundant feature elimination; then optimizing a K-Means initial clustering center by using a genetic algorithm, and obtaining an optimal initial centroid through chromosome coding, fitness function evaluation and genetic manipulation; and finally, K-Means clustering is executed, and typical power utilization characteristics of each cluster are extracted. According to the method, the problem that a traditional K-Means algorithm is sensitive to an initial value and is prone to falling into local optimum is effectively solved, the clustering quality is remarkably improved, the DB index is reduced by about 15%, and the extracted power consumption mode is highly matched with actual production characteristics. Experiments show that the load prediction error based on the extraction mode of the method is averagely reduced by more than 8%, and reliable technical support is provided for power load prediction, energy efficiency evaluation and intelligent scheduling.

The invention relates to the technical field of laser cutting, in particular to an ultra-precision laser cutting path optimization method and system.The method comprises the steps that a CAD model of a mechanical part is obtained, contour information of the mechanical part is extracted, the contour is discretized into a series of nodes, and technological parameters of laser cutting and motion constraint conditions of cutting equipment are determined; a laser cutting path is obtained according to the extracted contour information, nodes in the cutting path are coded into chromosomes in sequence, and each chromosome represents a cutting path scheme; designing a fitness function to evaluate each chromosome to obtain a fitness value of each chromosome; and performing genetic manipulation and iterative optimization on each chromosome according to the fitness value until a termination condition is met, obtaining the chromosome with the highest fitness, and decoding the chromosome into an optimized ultra-precision laser cutting path. According to the method, the genetic algorithm and the fitness function are planned and designed for the ultra-precision laser cutting path, global optimization of the cutting path is achieved, and the cutting efficiency and quality are improved.

The invention discloses a heterogeneous cluster task scheduling fusion method based on Q learning and a genetic algorithm, and belongs to the technical field of intelligent scheduling. Collaborative optimization is carried out through a dynamic feedback mechanism of Q-Learning and the global search capability of the genetic algorithm: in an initialization stage, a cluster state space and an action space are defined, and a multi-Q-value table and an initial population are generated; during task scheduling, nodes are selected based on a # imgabs0 #-greedy strategy, reward values are calculated, and a Q value table is updated; meanwhile, the scheduling scheme is coded into chromosomes, a new population is generated through roulette selection, crossover and variation, and a Q value table is optimized; in the iteration process, the convergence is judged according to the Q value change or the population fitness change, and the strategy is dynamically adjusted. According to the method, the advantages of double algorithms are fused, local optimum is avoided, the task processing efficiency and the resource utilization rate are remarkably improved, adaptive strategy updating during cluster state change is supported, and the method is suitable for an efficient task scheduling scene of a large-scale heterogeneous cluster.

The invention discloses an unmanned aerial vehicle cluster task allocation method based on a large language model optimization genetic algorithm, and belongs to the field of computers. The method comprises the following steps: setting a specific chromosome coding mode; generating a multi-constraint initial population; calculating fitness to quantify the advantages and disadvantages of individual genes of the population; when the optimal individual meets the requirement or the maximum iteration round is reached, ending; retaining the optimal individual as a filial generation; generating a batch of new filial generation individuals by the large language model, and fusing the new filial generation individuals with the current filial generation population; calling an optimized large language model to analyze individual chromosome semantics, and outputting an evolutionary potential score; obtaining an individual comprehensive selection probability by integrating the fitness and the evolution potential score, and executing a selection operation; and selecting individuals based on the individual comprehensive selection probability to carry out crossover and mutation operation to generate offspring. The large language model is embedded into the core link of the genetic algorithm, and the algorithm efficiency is improved by improving the population diversity of the genetic algorithm in the unmanned aerial vehicle cluster task allocation scene.

The invention discloses a bridge splicing part route optimization design method based on a constraint condition genetic algorithm, and relates to the technical field of bridge engineering design, and the method comprises the steps: building a hard constraint and soft constraint double-layer constraint system based on a standardized data set and a parameterized model in a step 2, and associating the system to fitness calculation of chromosomes; and based on a constraint processing result, integrating multi-objective optimization requirements, and constructing a fitness function. According to the method, a route is abstracted into a third-order cubic B-spline curve and parameterized coding is carried out, iteration is carried out in combination with a genetic algorithm, a design space can be globally explored in an optimizable control point range, and compared with traditional experience design, a better global solution can be found, and the problem of local optimum caused by experience limitation of engineers is avoided. The fitness function realizes multi-target quantitative integration through normalized indexes and adjustable weights, the weights of cost, smoothness and environmental influence can be flexibly adjusted according to project scenes, the problems of multi-target qualitative and difficult comparison in traditional design are solved, and the scheme adaptability is remarkably improved.

The invention relates to the technical field of guide vehicle distribution, in particular to a medical material automatic guide vehicle distribution path optimization system and method, which are used for investigating and analyzing hospital layout, and understanding and analyzing personnel, traffic flow, medical material characteristics and distribution requirements; selecting a proper AGV type and navigation mode according to an analysis result; minimization of distribution time is determined, and AGV energy consumption and equipment loss cost are reduced; the method comprises the following steps: establishing a mathematical model, abstracting a hospital environment into a directed graph, constructing a shortest path model or a multi-objective optimization model, and considering constraint conditions including a time window and a priority; an improved genetic algorithm is adopted to solve the model, the distribution path of the AGV is encoded into chromosome, fitness function design, selection, intersection and mutation operation are performed, the optimal distribution path is searched, the distribution path of the medical materials is optimized through model construction and algorithm improvement, the distribution time is shortened, and the distribution efficiency is improved.

The invention discloses an electronic circuit energy efficiency optimization design method based on a genetic algorithm, and the method comprises the following steps: S1, carrying out the modeling of a to-be-optimized electronic circuit, forming a mixed type chromosome, and initializing a genetic algorithm population; s2, constructing a fitness function based on a genetic algorithm population; s3, executing a selection operation on the current population, and replacing a parent by a child with high adaptability; s4, performing crossover operation on the parent individuals to generate new offspring individuals; s5, performing mutation operation on the offspring individuals to form new individuals; s6, performing circuit simulation on the new individual after the genetic manipulation is executed, and inputting a simulation result into a fitness function for evaluation; s7, updating the population according to the fitness score, and entering next-generation evolution; and S8, configuring an individual parameter which meets a convergence condition and has the highest output fitness score. Based on a local competition algorithm, a double-layer block recombination strategy and a Pareto frontier algorithm are fused, and energy efficiency optimization of the electronic circuit is achieved.

The present invention relates to the technical field of radar signal waveform design. Disclosed are a signal determination method based on a quantum genetic algorithm, a quantum computing device based on a quantum genetic algorithm, and a medium. The method comprises: constructing quantum bits on the basis of the number of phase coding radar signals transmitted by a radar system, the length of the phase coding radar signals transmitted by the radar system, and the number of chromosomes; determining gene sequences of first chromosomes on the basis of all the quantum bits; determining whether a first chromosome meeting a preset termination condition is present in all the first chromosomes; if yes, taking the first chromosome meeting the preset termination condition as a phase coding radar signal transmitted by the radar system; if not, updating all the first chromosomes on the basis of the mutation probability, a preset crossover strategy, a quantum catastrophe strategy, and a rotation angle to obtain second chromosomes; replacing all the first chromosomes in each population with all the second chromosomes, returning to the determination step, and ending the operation until the termination condition is met. The local optimal solution is avoided, and the search quality and the search efficiency are improved.

The invention relates to the technical field of fabric processing, in particular to a fabric cutting path planning method and device, equipment and a storage medium. Performing enhancement processing on the historical cutting path data based on a multi-point crossing method to obtain an enhanced chromosome population; constructing a path planning model based on a preset genetics algorithm and the enhanced chromosome population; generating a cutting total path based on the path planning model, the fabric characteristic data and the fabric image; adjusting the total cutting path based on a preset bounding box algorithm, the fabric characteristic data, the cutting tool state data and the cutting table component coordinate data to obtain an optimized total cutting path; by combining the fabric characteristics and the equipment state to plan the path, the cutting efficiency and the fabric utilization rate can be effectively improved, the cost is reduced, the service life of a cutter is prolonged, the system environment adaptability is enhanced, the cutting accuracy and stability are guaranteed, and the intelligent and efficient development of the fabric cutting industry is promoted.

The invention provides a discrete mixed job production line scheduling method based on an improved genetic algorithm, and relates to the technical field of industrial automation and production scheduling, and the method comprises the steps: S1, carrying out the modeling and data input of a production line scheduling problem, constructing a flexible job shop scheduling model, defining a decision variable, a constraint condition and a target function, and inputting basic data; s2, generating an initial population by adopting a hybrid initialization strategy, wherein the initial population comprises randomly generated individuals and individuals generated based on a heuristic rule; s3, chromosome coding is carried out on the scheduling scheme in a two-segment coding mode, wherein a process sorting segment and a machine distribution segment are included; s4, the fitness is calculated, and individual selection is carried out by adopting a tournament selection method; s5, executing improved genetic operations including adaptive crossover and mutation operations; s6, carrying out local search on the elite individuals, wherein the local search comprises key path identification and neighborhood disturbance; and S7, judging a termination condition, if the termination condition is met, outputting an optimal scheduling scheme, otherwise, returning to the step S4.

The invention provides a heterogeneous cloud multi-target task scheduling method and system based on quantum collaborative optimization, and the method comprises the steps: generating a DAG graph based on a user request, and constructing a chromosome code of charging mechanism collaboration; an initial population is randomly generated, quantum fitness evaluation is performed, non-dominated sorting is accelerated by applying a Grover search algorithm, a quantum-assisted crowding distance is introduced, and a to-be-evolved individual is obtained. Performing crossover and mutation operation on the sample by using a quantum genetic algorithm, and stopping when a convergence condition is reached; and finally, converting a Pareto solution set obtained by the quantum genetic algorithm into pheromones, initializing by using an ant colony algorithm and carrying out routing updating to complete scheduling and distribution. According to the invention, global search is carried out through the quantum genetic algorithm, and local optimization is carried out through the ant colony algorithm, so that the overall efficiency of the task scheduling method is improved; meanwhile, by improving a genetic algorithm chromosome coding mode and redefining a virtual machine cost function, the problem of billing mechanism difference in heterogeneous clouds is solved.

The invention provides a modularized large screen automatic layout optimization method and system based on a constraint genetic algorithm. The method comprises the following steps: constructing decision variables and constraint conditions of modularized large screen layout optimization; encoding components in the large modularized screen to obtain different layout schemes corresponding to different chromosomes, and forming an initial layout group; performing optimization operation on each chromosome in the initial layout group by adopting a genetic operator to obtain a secondary layout group; and taking the secondary generation layout group as a new initial layout group, performing optimization operation again until a preset requirement is met, and outputting a final layout scheme. The system is used for implementing the method. The method is suitable for a visual platform in the fields of smart cities, industrial internet of things and the like, and solves the problem of automatic layout of large-scale components on the premise of meeting complex business rules and standard index requirements.

The present invention discloses a method for path planning and obstacle avoidance of an unmanned boat formation, and relates to the technical field of formation path planning, including system initialization and basic settings; using a quantum genetic algorithm, decoding quantum chromosomes into actual paths, screening out the optimal path, and discretizing the optimal path to obtain a global navigation path; constructing a follower navigation path according to the formation characteristics and the reference formation, adjusting the initialization formation when the follower starts, and establishing a communication link with the navigator at the same time, and the follower using the improved artificial potential field method to avoid obstacles locally and adjust the local formation; detecting a dynamic obstacle, recalculating the force using the improved potential field method, and optimizing the local path of the replanned section; judging whether the target point has been reached, if it has been reached, then ending, if not, then executing in a loop until the target point has been reached. The present invention enables the formation system to maintain a stable formation in a dynamic unknown environment, and achieve the goals of safe obstacle avoidance and optimal path.

The invention provides a heterogeneous unmanned aerial vehicle path planning method and system, and the method comprises the steps: carrying out the intersection processing and variation processing of gene segments of chromosomes in an unmanned aerial vehicle path planning chromosome population in a current iteration period based on an improved genetic algorithm, and obtaining a new unmanned aerial vehicle path planning chromosome population; and when it is determined that the individual fitness of chromosomes in the new unmanned aerial vehicle path planning chromosome population meets preset fitness, obtaining a target chromosome from the new unmanned aerial vehicle path planning chromosome population, and performing unmanned aerial vehicle path planning according to unmanned aerial vehicle path planning corresponding to the target chromosome. And constructing an optimal cooperative flight path of the first unmanned aerial vehicle and the second unmanned aerial vehicle in the target area. According to the method, the problem of multi-target optimization path planning of the multi-heterogeneous unmanned aerial vehicle can be effectively solved, and the efficiency and adaptability of path planning of the unmanned aerial vehicle are improved.

The invention discloses a vehicle route optimization method based on K-Medoids clustering and an improved genetic algorithm, and the method comprises the steps: obtaining input data which comprises a distribution center position, a customer demand, a road distance, a traffic flow, vehicle information and a time window constraint; dividing the customer points into K clusters by adopting a K-Medoids clustering algorithm according to the distribution center position, the customer demand and the road section distance; numbering each customer point in the K clusters, and generating K initial chromosome populations in combination with a time sequence considering road congestion; in each chromosome population, a new fitness function is adopted to calculate the fitness value of each chromosome, and genetic iteration operation is executed; and judging whether a termination condition is met, if the termination condition is met, outputting an optimal solution, otherwise, continuing iteration. According to the method, K-Medoids clustering and the improved genetic algorithm are combined, and the technical problems that when a traditional genetic algorithm is used for solving the VRP problem, the convergence speed of the solving process is low, and the problem is prone to falling into a local optimal solution are solved.

The invention discloses a horizontal centrifuge real-time PID gain optimization control method based on an improved adaptive genetic algorithm. The method comprises the steps that a chromosome population is defined based on PID gain of a horizontal centrifuge; constructing a first fitness function based on the first index of the horizontal centrifuge, and calculating the fitness of individuals in the chromosome population; and screening parent individuals by using a champion for genetic generation of offspring, obtaining the crossover rate and mutation rate of the individuals based on the fitness of the individuals in the genetic process, repeating the genetic process until convergence, and applying the PID gain corresponding to the current optimal individual to control the horizontal centrifuge. In the process, the parent is screened, and the genetic crossover rate and the mutation rate are adaptively adjusted, so that the PID parameters are converged more quickly, and the robustness is high.

The invention relates to a multi-process-route-oriented production scheduling integrated optimization method, and relates to the technical field of flexible workshop multi-target scheduling. The method comprises the steps that firstly, a production line process planning and production scheduling integrated optimization model is constructed, the model adapts to a machining sequence flexible scene, and optimal process route determination and procedure machining equipment distribution are synchronously achieved; secondly, double optimization targets are set, namely, the maximum completion time is minimized, and the equipment utilization rate is maximized; thirdly, designing an optimization solution process based on a genetic algorithm framework, generating a chromosome population containing a process route selection machine allocation process sequence, and fusing double targets through an adaptive fitness evaluation mechanism; and finally, developing a visual interaction system in a matched manner, and dynamically outputting an optimal process route scheme and an equipment scheduling plan. The method can effectively solve the problems of long processing task completion time and unbalanced equipment utilization rate in a multi-process route scene, is suitable for a scheduling scene of a flexible job shop, and improves the overall operation efficiency of a production line.

The invention relates to a high-speed rail freight train operation optimization method, device and equipment. The high-speed rail freight train operation optimization method comprises the following steps: establishing an operation optimization double-target nonlinear model of a high-speed rail freight train; decomposing into a train alternative set limitation main model, a freight train alternative set generation sub-model, a first single-target optimization model and a second single-target optimization model according to different modes; solving a train alternative set limitation main model and a freight train alternative set generation sub-model according to the high-speed rail freight train alternative set to obtain a current optimal freight train alternative set taking the initial freight train alternative set as a starting point; substituting into the first single-target optimization model and the second single-target optimization model to solve and obtain an optimal target value vector; and solving the optimal target value vector by adopting a genetic algorithm to obtain an optimal chromosome, and outputting the optimal chromosome as a final optimal solution. According to the method, the genetic algorithm and the column generation algorithm are combined, economical efficiency and transport capacity requirements are considered, matching of cargo flow and train flow is achieved, and a feasible high-speed rail freight train operation optimization scheme is obtained.

The invention relates to the technical field of geotechnical parameter analysis, and discloses a sparse data probability distribution modeling method applied to geotechnical parameter data acquisition, which comprises the following steps of: establishing a sparse data set according to acquired sample data and sample quantity; based on the sparse data set, adopting a Gaussian function as a kernel function, and obtaining an approximate probability density function and an approximate cumulative distribution function through a kernel density estimation algorithm; obtaining an empirical cumulative distribution function of the sparse data set; obtaining a maximum absolute difference through a difference value between the approximate cumulative distribution function and the empirical cumulative distribution function; taking a plurality of random bandwidth parameters as an initial group of chromosomes, carrying out optimization solution on the maximum absolute difference through a genetic algorithm, and obtaining the chromosome corresponding to the maximum fitness as an optimal bandwidth parameter of an approximate probability density function and an approximate cumulative distribution function; and substituting the optimal bandwidth parameter into the approximate probability density function and the approximate cumulative distribution function to obtain a target approximate probability density function and a target approximate cumulative distribution function.

The invention provides a semiconductor production scheduling method and device, equipment and a storage medium. The method comprises the following steps: acquiring an initial population of a plurality of batches of products; decoding each chromosome in the initial population to obtain the scheduling time corresponding to each chromosome; performing genetic algorithm iterative computation based on a preset fitness function and the scheduling time corresponding to each chromosome, and outputting N chromosomes with relatively large fitness function values; aiming at the N chromosomes, respectively calculating average in-process waiting time corresponding to each non-bottleneck process, and determining a target chromosome according to the average in-process waiting time corresponding to each non-bottleneck process; and decoding the target chromosome to obtain target scheduling time corresponding to the target chromosome, so that all bottleneck processes and non-bottleneck processes of all batches of products are executed according to the target scheduling time to produce all batches of products. The method provided by the invention is helpful for improving the production efficiency of the semiconductor.

The invention belongs to the technical field of production scheduling, and provides a genetic algorithm self-learning variation method and system for solving a scheduling problem, and the method comprises the steps: a chromosome initialization step: setting a workshop variable and a dynamic scheduling rule, and generating an initial solution through a greedy method heuristic strategy; and a chromosome iterative optimization step: iteratively optimizing the initial solution according to a genetic algorithm to obtain a variation result. According to the method, the GA is combined with the self-learning mutation method, and a single-task and batch-task moving mechanism of the same unit and cross units is adopted, so that self-adaptive adjustment of a chromosome structure is realized, the global optimization capability and scheduling efficiency of the algorithm are improved, the global search capability of the algorithm is enhanced, the convergence speed is increased, and the optimization result is improved; by replacing heuristic variation driven by traditional rules with self-learning variation, the complexity of customized development is reduced, the scheduling efficiency is improved, and meanwhile, the code quantity and the implementation cost are reduced.

The invention discloses an official vehicle scheduling and optimizing method and system for mixed type tasks, and belongs to the field of official vehicle intelligent scheduling. The method comprises the following steps: acquiring basic information of official business tasks, drivers and vehicles, constructing a task distance matrix, then establishing a scheduling model which takes the minimum total fuel consumption as a target and contains constraints such as grade matching and passenger-carrying upper limit, and setting genetic and simulated annealing algorithm parameters; constructing an initial scheme population through chromosome codes matched with task levels, and calculating the fitness value of each scheme; and taking the chromosome as a simulated annealing initial solution to complete local optimization, transmitting the solution into a genetic algorithm to carry out crossover and mutation iteration, and finally outputting an optimal scheduling scheme. According to the method, a hybrid algorithm framework is adopted, global search and local optimization capabilities are considered, the compliance, low oil consumption and high resource utilization rate of a scheduling scheme are realized, the problems of level mismatching, resource waste and algorithm limitation of traditional scheduling are solved, and the method is suitable for official car scheduling scenes of various organizations, enterprises and public institutions.