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

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 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 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 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 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 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 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.

The invention belongs to the technical field of power system operation and planning, and discloses a power grid net load fluctuation scene generation method, system and device based on an ARIMA and Copula combined model and a medium, so as to solve the problem of poor scene generation accuracy. The method comprises the following steps: decomposing and reconstructing an original time sequence of the net load of the power grid by using discrete wavelet transform; taking permutation entropy minimization as an optimization target, adopting a variable chromosome length hybridization genetic algorithm to divide time segments for the low-frequency linear subsequences, and respectively establishing ARIMA models to generate linear trend scenes; establishing a joint probability distribution model of the high-frequency fluctuation subsequences at adjacent moments based on a Copula function, and deducing conditional probability distribution in combination with a Bayesian formula to generate a fluctuation scene; and the linear trend scene and the fluctuation scene are superposed to form an initial net load scene set, and a k-means clustering algorithm is adopted to reduce the initial net load scene set to obtain a representative scene set.

The present application relates to unmanned agricultural machinery technical field, specifically relates to a kind of full coverage field path planning method and device based on improved genetic algorithm.The method comprises: S1, constructs field environment map using grid method.S2, the free grid in field environment map is chromosomally encoded, and a plurality of initial chromosomes are randomly generated.S3, initial chromosome is adjusted, and legal chromosome is obtained.S4, construct fitness function based on multi-objective equilibrium criterion, determine the fitness of legal chromosome.S5, initial chromosome is evolved, and the next generation population is generated, as the parent population of next iteration.S6, determine whether to end this iteration, if yes, then the highest fitness chromosome in contemporary population is output for decoding, and full coverage path is obtained;If not, return to step S3.The present application can reduce repetitive work area and turning number, improve the field work efficiency and work quality of unmanned agricultural machinery.

The application relates to a mode filter, a topology optimization method and an ultrasonic guided wave damage imaging artifact suppression method, relates to the field of ultrasonic detection and imaging, and the topology optimization method comprises the following steps: acquiring initial design parameters, generating an initial population, each chromosome individual in the initial population is coded by a binary logic matrix representing material distribution and meeting design constraints; a population is optimized by using a genetic algorithm, an adaptability function in the genetic algorithm is constructed based on a mode purity index and an energy transmission index, the mode purity index is the ratio of in-plane displacement integral in a transmission area in finite element simulation with the mode filter to total displacement integral, and the energy transmission index is the ratio of relative energy at a transmission end in the finite element simulation with the mode filter to relative energy of an S0 mode; and final optimized material distribution of the mode filter is output. Compared with the prior art, the application has the advantages of effectively suppressing background artifacts, giving consideration to mode regulation performance and substrate structure integrity and the like.

The invention relates to the technical field of intelligent control of high-altitude operation robots, and discloses a dual-algorithm-driven multi-mode high-altitude operation system and an intelligent control method, and the method comprises the steps: obtaining environment sensing data and robot state parameters, and generating an operation task parameter set, a genetic algorithm optimization module is used for generating a global job task scheduling scheme through chromosome coding and fitness function evaluation, dynamically adjusting a job control strategy based on a reinforcement learning algorithm according to a state space, an action space and a reward function to generate a real-time control instruction, and realizing closed-loop cooperation of the genetic algorithm and the reinforcement learning algorithm; according to the invention, the intelligent control and decision-making problem of the aerial work robot is solved, and autonomous decision-making, path planning and multi-task efficient cooperation are realized.

The invention relates to the technical field of software development, and discloses a method, a device and equipment for arranging and testing laboratory orders, and a storage medium. The method comprises the following steps: according to a current order arrangement strategy generated based on a reinforcement learning model, performing chromosome coding on current laboratory order information, current experimenter information and current experimental equipment information to obtain a corresponding initial population; the fitness of individuals in the initial population is evaluated based on a current fitness function, the initial population is iteratively evolved by selecting and improving crossover and mutation operation of chromosomes, and the current fitness function is obtained based on examination parameters of laboratory orders predicted by a first AI model; and under the condition that a set genetic termination condition is met, an optimal laboratory order scheduling and testing scheme is obtained. Therefore, through the reinforcement learning model and the AI model and in combination with the genetic algorithm, comprehensive optimization of laboratory order arrangement and measurement is realized, and the order arrangement efficiency and the resource utilization rate are improved.

The invention belongs to the field of steel member production and optimal scheduling, and discloses a steel structure production intelligent scheduling method fusing neat property and equipment resource constraint, which is used for solving the problem of scheduling difficulty caused by part neat property constraint and complex processing procedures in a steel structure factory. The method comprises the following steps: acquiring production line information, PBOM information, nesting information, processing quota, production state and plan and other data, and constructing an optimal scheduling model with the goal of minimizing the maximum completion time. The model introduces constraint conditions such as a blanking sequence, equipment allocation, a processing sequence and neat set performance, a composite chromosome genetic algorithm is adopted for solving, scheduling decoding is completed in combination with process sorting and equipment allocation, and a feasible scheduling scheme meeting the neat set requirement is generated. According to the method, task allocation in the component production process can be effectively optimized, the resource utilization rate and the overall scheduling efficiency are improved, and the method has high engineering applicability.

The application belongs to the technical field of mobile visual inspection planning, and specifically discloses a mobile visual inspection station-point joint clustering and sequence planning method. The method is a genetic algorithm based on an alternating evolution strategy, and comprises steps of data encoding / decoding, evolution strategy updating, individual selection, individual crossover, individual mutation, population updating and the like. The length of a chromosome is equal to the number of points participating in sequencing. The integer part of a gene represents a point sequence number, and the decimal part represents a station sequence number to which the point belongs. Point sequences belonging to the same station follow the order of appearance in the chromosome; station sequences follow the order of the first appearance position. The application can integrally realize station-point clustering and sequence planning, and improve visual inspection efficiency; the proposed evolution strategy also significantly reduces the running time of the planning program. The application is suitable for large and complex component mobile visual inspection and other similar scenarios.

This invention relates to the field of logistics distribution and route planning technology, and particularly to a method and system for periodic delivery route planning that balances driver workload and customer service order. The method includes: constructing a delivery plan for C customers over D days using a matrix-based chromosome encoding strategy; decoding chromosomes by inserting driver numbers into each row of the matrix, and constructing the daily delivery route for each driver based on the customer set and order obtained each day; performing population evolution based on a genetic algorithm framework, integrating driver workload balancing operators and customer service order balancing operators; calculating chromosome fitness in the population based on the periodic delivery route planning model, and using a tournament selection strategy to select chromosomes to form the next generation population. This invention better addresses the pain points of uneven driver workload and uneven customer service order in periodic delivery, and has certain practical reference value for optimizing the operation of periodic delivery systems.

The invention discloses a parallel greedy genetic algorithm for cluster job scheduling, which comprises the following steps that: a main scheduler firstly receives jobs and initially arranges the jobs by using a backfill strategy, then encodes the jobs into DAG chromosomes, generates a plurality of initial individuals according to a greedy heuristic form, performs selection, crossover, variation and elitist retention on populations by the genetic algorithm, and iteratively optimizes a scheduling scheme; under the master-slave parallel architecture, a master process is responsible for job preprocessing and population generation, and slave processes complete genetic computation in parallel and update a job queue in real time. The method considers completion time, load balance and resource utilization rate, and can significantly improve cluster scheduling efficiency.

This invention belongs to the technical field of aircraft manufacturing engineering and intelligent scheduling optimization. It discloses an aircraft final assembly scheduling method and equipment based on an improved genetic algorithm. The steps are as follows: (1) Construct a scheduling model based on process and worker data with the optimization objectives of minimizing the final assembly completion time and minimizing the number of workers involved; (2) Encode the process priority and worker skill slots obtained from the scheduling model using an improved genetic algorithm in the encoding stage, and use the encoding as chromosomes to form an initial population; (3) Perform event-driven decoding on the individuals in the initial population to obtain an initial scheduling scheme; (4) Calculate the fitness value of each chromosome according to the total completion time and the number of workers involved corresponding to the initial scheduling scheme, and screen all chromosomes according to the fitness value to obtain a new population, and then finally obtain the aircraft final assembly scheduling scheme. This invention realizes the collaborative optimization of completion time and the number of workers involved.

The application relates to the technical field of antenna array design, and provides a concentric circular array antenna design method based on hybrid DE-GA collaborative evolution, which comprises the following steps: S1, constructing a topological-geometric coupled concentric circular array radiation model; S2, constructing a hybrid coding chromosome and initializing a population; S3, based on the concentric circular array radiation model, performing a hybrid DE-GA collaborative evolution method on the hybrid coding chromosome in the initialized population; S4, calculating fitness and updating a global optimal solution; S5, judging whether a termination condition is met; if the maximum iteration number or error precision requirement is reached, outputting an optimal array element arrangement sequence and a circular ring radius sequence; otherwise, returning to step S3 and continuing iteration. The improved genetic algorithm and the improved differential evolution algorithm are used for collaborative evolution of discrete sparse variables and continuous radius variables respectively, so that global optimal matching of array topology and geometric structure is realized simultaneously in a single optimization process.