466 results about "Selection (genetic algorithm)" patented technology

The invention relates to the technical field of unmanned aerial vehicle inspection, and discloses a power transmission line unmanned aerial vehicle inspection task intelligent allocation collaborative planning method based on a genetic algorithm. The method comprises the following steps: firstly, collecting and analyzing basic information of a power transmission line, an unmanned aerial vehicle, a surrounding environment and weather, and establishing a space model of unmanned aerial vehicle inspection by using a grid method; based on the collected information, establishing a mathematical model to allocate inspection tasks of the unmanned aerial vehicle; a genetic algorithm is used for coding and optimizing a routing inspection path, meanwhile, a fitness function is designed to comprehensively consider multiple factors of a search effect, and quantization, weight distribution and optimization selection are carried out; and an optimal solution is found through iterative optimization, and intelligent distribution and collaborative planning of unmanned aerial vehicle inspection tasks are realized, so that the requirements of regional inspection are met. According to the technical scheme, the flight threat degree under different meteorological conditions can be quantitatively evaluated by considering the wind speed, rainfall and visibility, and the flight plan is adjusted according to the flight threat degree.

The invention discloses a network edge monitoring and early warning method based on video image AI analysis, and the method comprises the following steps: S1, obtaining video data, processing the video data, and generating an image frame sequence; s2, analyzing an image frame sequence, and extracting space and time features; s3, taking the target as a hypergraph node, constructing a hyperedge based on space and time features, and dynamically adjusting hyperedge connection by using a genetic algorithm; s4, constructing a multi-layer hypergraph Transform network, extracting multi-scale spatial-temporal characteristics, and calculating a semantic relationship between nodes; s5, setting a butterfly optimization algorithm initial population, and dynamically optimizing model parameters through global and local search; s6, constructing an anomaly detection model, identifying an abnormal behavior, and feeding back a result to optimize model parameters and hyperedge selection; and S7, deploying the model at an edge node, triggering early warning when an abnormal behavior is detected, and pushing information to a management platform. According to the invention, through video image AI analysis, accurate detection and real-time early warning of abnormal behaviors in a network edge scene are realized.

The invention discloses a method for intelligently regulating and controlling production parameters in a fruit concentrated juice production process, which comprises the following steps of: acquiring multi-dimensional process parameters such as temperature, pressure, flow, concentration, equipment state and the like in real time through a multi-channel sensor network, and forming a standardized data sequence after filtering, normalization and drift correction; extracting stage features by using technologies such as a sliding window and Fourier transform, and inputting the stage features into the lightweight classification model to realize production stage identification; in combination with an identification result, dynamically calling a corresponding multi-target optimization sub-model, and realizing nonlinear prediction and optimal solution selection of process parameter setting by adopting an LSTM and a multi-target genetic algorithm; on the basis of real-time feedback, the performance of the model is automatically evaluated, self-adaptive adjustment and optimization of the optimization algorithm are achieved through reinforcement learning and an incremental updating mechanism, multi-target collaborative optimization, self-adaptive adjustment and model switching in the production process can be achieved, and the consistency of production efficiency and product quality is improved.

The invention belongs to the technical field of industrial intelligence, and relates to a deep reinforcement learning-based non-correlation parallel machine scheduling method, which comprises the following steps of: constructing a mathematical model suitable for non-correlation parallel machine scheduling; the machining time of each workpiece on each heterogeneous machine is collected, and normalization processing is carried out; initializing a genetic algorithm scheduling population and a depth Q network; constructing a deep reinforcement learning training framework; expressing a state vector by using the average fitness, the optimal fitness and the optimal individual code; operating parameters of the genetic algorithm are controlled by using the action space, and parameters of the deep Q network are updated by using a reward function; and dynamically controlling operator selection in a genetic algorithm iteration process by using the trained deep reinforcement learning model to obtain an optimal scheduling solution and realize scheduling of the non-correlation parallel machine. According to the method, a deep reinforcement learning algorithm is provided for solving similar problems in the manufacturing industry production scheduling field by analyzing a non-correlation parallel machine scheduling problem data model, and the production efficiency is improved.

The invention discloses an intelligent path planning method and system for an unmanned aerial vehicle. The method comprises the following steps: initializing related parameters and optimization algorithm parameters of the unmanned aerial vehicle; coding the path points of the unmanned aerial vehicle; calculating fitness values of all individuals in the population; generating a new population through selection, crossover and mutation operations; carrying out local search on individuals with better fitness in the population; dynamically adjusting algorithm parameters according to population fitness changes; and outputting the path with the highest fitness value as the optimal flight path of the unmanned aerial vehicle. According to the method, a local search mechanism and a dynamic adjustment strategy are introduced on the basis of a traditional genetic algorithm, the method is suitable for a combinatorial optimization problem, excellent performance is shown in unmanned aerial vehicle path planning, and a path with the shortest time can be efficiently found.

The invention discloses a ship power system optimization scheduling method and system based on a genetic algorithm, and relates to the technical field of ship power control, and the method comprises the steps: obtaining real-time operation parameters of a ship power system, building an initial population of the genetic algorithm based on the operation parameters, and carrying out the optimization scheduling of the ship power system based on an energy consumption characteristic index and an emission characteristic index. And calculating the comprehensive fitness value of each individual, performing selection operation, interlace operation and mutation operation on the initial population to generate a new population, repeatedly performing population iterative optimization until a preset condition is met, outputting a navigational speed adjustment parameter combination, a generator set start-stop decision sequence and a power distribution scheme, and generating a ship power system control instruction set. According to the ship power system optimization scheduling method and system based on the genetic algorithm provided by the invention, the operation efficiency of the ship power system is improved, the energy consumption is reduced, and the carbon emission is reduced.

The invention provides a method for solving the computing power resource utilization rate based on a genetic algorithm, and relates to the technical field of computing power resource utilization. Comprising the following steps of S1, performing population initialization, randomly generating a group of initial solutions, and representing a computing power resource allocation scheme by each solution; s2, adaptability evaluation: defining a fitness function to calculate the fitness value of each individual; s3, genetic operation: performing selection operation, crossover operation and mutation operation to generate a new offspring; s4, generating a new-generation population, and selecting the most excellent individuals to form the new-generation population; s5, performing iterative computation, iteratively executing the genetic operation and the new generation population generation step, and continuously updating the population until a termination condition is met; and S6, outputting a result, and identifying an optimal solution or a group of excellent solutions from the current population. By simulating natural selection and genetic mechanisms in the biological evolution process, allocation of computing power resources is optimized, so that the resource utilization rate and the system performance are improved.

The invention relates to a multi-distribution-center open type vehicle path intelligent optimization method and system, and belongs to the technical field of logistics distribution optimization and intelligent transportation, and the method comprises the steps: firstly obtaining the input data of a multi-distribution-center vehicle path optimization problem, selecting a multi-distribution-center processing strategy according to the problem scale and constraint conditions, and carrying out the optimization of the multi-distribution-center vehicle path; a vehicle path optimization model is constructed, the vehicle path optimization model comprises a single-target model and a multi-target model, a multi-algorithm collaborative optimization framework is adopted for solving, and the multi-algorithm collaborative optimization framework comprises an ant colony algorithm, a variable neighborhood search optimization ant colony algorithm and a non-dominated sorting genetic algorithm; and outputting an optimal vehicle path scheme, wherein the optimal vehicle path scheme comprises a distribution route, a distribution sequence and a corresponding objective function value of each vehicle. According to the method, strategy adaptive selection and algorithm collaborative optimization are carried out, global exploration, local optimization and multi-target equalization are carried out by combining the advantages of the ant colony algorithm, the variable neighborhood search algorithm and the non-dominated sorting genetic algorithm, and the method is good in reproducibility, high in scene adaptability and high in decision support capability.

The invention discloses a large-scale infrastructure intensive land-saving development potential analysis method and system, and the method comprises the following steps: 1) constructing a large-scale infrastructure stock land geospatial database based on Arcgis, and collecting the related element data of the large-scale infrastructure stock land; 2) extracting land suitable for optimizing stock of large infrastructures, and screening and extracting municipal public facility land and road and traffic facility land which are larger than 5 hectare in land scale and are not dangerous; 3) constructing an AHP-entropy weight method hybrid model, optimizing the weight by means of a genetic algorithm to establish a site selection evaluation system, and comprehensively evaluating the alternative land; 4) constructing a land development population service coverage model to obtain the number of each development land service population; and 5) guiding a land redevelopment time sequence in combination with the comprehensive score of the alternative land and the number of serviceable population. The method provides a path for improving the utilization efficiency of land resources, eliminating the neighborhood effect of large infrastructures and realizing high-quality development.

The invention relates to the technical field of aviation safety, and provides an NSGA-based flight ordering method, system, device, product and medium, and the method comprises the steps: obtaining the flight information of to-be-parked aircrafts, carrying out the random ordering of the to-be-parked aircrafts, and obtaining an initial population; calculating adaptive parameters so as to formulate optimization constraint conditions; under the constraint of an optimization constraint condition, performing multi-target optimization sorting on the initial population according to adaptive parameters, calculating the congestion degree of an approach sequence, performing elimination to obtain a first intermediate population, performing genetic algorithm screening to obtain a second intermediate population, and performing crossover variation to obtain a third intermediate population to obtain a target population; calculating a parking position selection parameter and a shortest taxiing path of the aircraft to be landed, thereby calculating a taxiway adaptability parameter; and calculating iteration adaptability parameters of the target population, and performing genetic algorithm iteration on the target population to obtain a target approach sequence. According to the invention, the scheduling efficiency of flight landing is effectively improved.

The invention provides a video monitoring point location layout method, system and device and a medium, and belongs to the technical field of video monitoring layout, and the method comprises the following steps: collecting geographic position information in a video monitoring target area and point location information of a built video probe, and processing the geographic position information and the point location information; calculating the coverage area of each video probe and the actual coverage area of all the video probes, and constructing a fitness evaluation function; based on point location information of a built video probe, a genetic algorithm is used, a population of monitoring configuration layout is initialized, fitness calculation is carried out, population selection and optimization of crossover and variation are carried out with the maximum fitness as the target, and the optimal monitoring configuration layout is obtained and applied to an actual monitoring system when no monitoring blind area exists. The video monitoring layout is optimized through the genetic algorithm, efficient configuration and blind area complementation are achieved, the monitoring coverage rate is increased, repeated investment is reduced, and the method flexibly adapts to monitoring demand changes.

The invention relates to the technical field of road engineering, in particular to a large language model-driven road change design method, which comprises the following steps of: 1) performing rough selection by using a large language model and a prompt project, and selecting a corridor zone; and 2) performing optimization through an improved artificial potential field MAPE algorithm. According to the method provided by the invention, by applying a natural language processing technology, automatic generation of a preliminary corridor belt design scheme is realized, the manpower and time cost of a route pre-selection stage is saved, a large number of alternative schemes can be produced to assist design, and the optimization effect is also higher than that of a relatively mature genetic algorithm; the method is a successful exploration of a large language model in intelligent road line selection application.

The invention relates to an intelligent power grid cooperative scheduling method for automobile access, and relates to the field of electric automobile charging scheduling and intelligent power grid optimization. The method comprises the following steps: acquiring multi-source real-time data of an electric vehicle, a power grid and a charging station, constructing a collaborative scheduling graph structure, and performing multi-target optimization to generate an initial charging guide strategy and a charging station resource allocation scheme; then, reinforcement learning iteratively optimizes the guide strategy to dynamically adapt to environmental changes; further optimizing charging time and power parameters through a dynamic adaptive optimization algorithm, and realizing charging station congestion early warning and selection suggestions in combination with a probability prediction algorithm; a charging station power distribution strategy is optimized based on early warning information, fine optimization is carried out by adopting a genetic algorithm, and a scheduling scheme is evaluated and adjusted in real time through a feedback control algorithm. According to the invention, dynamic, refined and global optimization of electric vehicle charging scheduling is realized, the stability of a power grid, the operation efficiency of a charging station and the charging experience of a user are improved, and new energy consumption is promoted.

The invention relates to the technical field of air conditioner fan blade design optimization, and discloses an air conditioner fan blade optimization method and system based on a BP neural network and a GA algorithm. The method comprises the following steps: obtaining initial geometric parameters and performance data of a fan blade, and cleaning and standardizing the initial geometric parameters and the performance data to form a standard data set; a BP neural network is used for training to obtain a fan blade performance prediction model; a genetic algorithm is applied to optimize the prediction model, and a new design parameter population is generated through genetic operations such as selection, crossover and variation; the optimized parameters are input into a CAD system to generate a candidate fan blade model, numerical simulation is carried out, and performance indexes of the candidate fan blade model are calculated; and screening excellent individuals based on a multi-objective optimization method, iteratively executing optimization and simulation processes until convergence, and finally outputting an optimal fan blade design. According to the method, the fast prediction of the neural network and the global search capability of the genetic algorithm are combined, the dependence of traditional optimization on high-frequency numerical simulation is reduced, and the design efficiency and quality are improved.

The invention discloses a digital automobile maintenance and repair market intelligent operation system, particularly relates to the field of automobile maintenance and repair, and comprises a database, a vehicle information acquisition module, a data preprocessing module, a repair auxiliary diagnosis module, a work order intelligent scheduling module and a user information terminal. The maintenance auxiliary diagnosis module constructs a fault diagnosis model based on a vehicle data processing text and a historical maintenance case library, the accuracy and efficiency of fault diagnosis are improved, a maintenance work order is generated according to a diagnosis result and an association rule table, accurate selection of accessories needed by maintenance is ensured, and the maintenance time and cost are reduced; according to the intelligent work order scheduling module, the objective function and the genetic algorithm are constructed, related variables are comprehensively considered, reasonable distribution of maintenance work orders is achieved, resource utilization of maintenance enterprises is effectively balanced, work order waiting time and total maintenance time are shortened, and overall operation efficiency is improved.

The invention provides an evaluation method and system based on soil buffer capacity, and relates to the technical field of soil evaluation.The method comprises the steps that a microbial community diversity index is coded to serve as a genotype of a genetic algorithm; randomly generating an initial population, wherein each individual represents one environment factor combination; calculating the fitness value of each individual, carrying out selection, crossover and mutation operations, and repeating genetic operations until a termination condition is met so as to obtain a final key environment factor combination; obtaining microorganism activity data according to the soil respiration rate in the microorganism sample; and according to the final key environment factor combination, the microbial activity data and the trained neural network model, predicting a soil buffer capability value. According to the method, the buffer capacity of the soil can be evaluated more accurately.

The invention relates to an intelligent available link prediction and optimal selection method for unmanned cluster anti-interference communication. The method comprises the following steps: acquiring a multi-channel interference power matrix; an LSTM-KAN interference prediction model is established; predicting channel interference power of a plurality of time slots in the future by using an LSTM-KAN interference prediction model; channel selection is modeled as a dual-objective optimization model for minimizing channel switching times and minimizing channel average interference power; and solving the dual-objective optimization model based on a classification sorting genetic algorithm, improving a crossover variation strategy and an elitist retention strategy to increase the generation probability of a low-channel switching frequency solution, and retaining the low-channel switching frequency solution as much as possible in an iteration process, and finally generating an optimized channel switching and interference power solution. Compared with the prior art, the method has the advantages of remarkably reducing the channel switching times, improving the throughput and reducing the packet loss rate, and has important application value in resisting unmanned trunking communication in a strong electromagnetic interference environment.

The invention discloses a ground-air cooperative fire barrier opening method based on digital twinborn and neural networks, and belongs to the technical field of forest steppe fire prevention. Geographic information, weather and fire monitoring data are collected and preprocessed, and a digital twinborn model is constructed to simulate meteorological elements. And then constructing an evaluation index system to evaluate the fire risk, and dividing risk grades. An isolation belt is planned by adopting a genetic algorithm, and a parameter combination is explored through selection, crossing and mutation operations. During air-ground collaborative operation, the unmanned aerial vehicle guides construction, the helicopter lifts materials, and the scheme is monitored and adjusted in real time. The fire retardant effect is evaluated through simulation and actual data, the efficiency and the cost effectiveness are set up, and all links are optimized according to results. According to the method, the fire risk is accurately assessed, the isolation belt is scientifically planned, the ground-air collaborative operation efficiency is improved, dynamic monitoring and optimization are realized, the cost is reduced, and an effective means is provided for forest steppe fire prevention and control.

The invention provides a variable-frequency control method and system for noise of an exhaust system of a transformer substation room based on a genetic algorithm, and the method comprises the steps: collecting the temperature and noise data of the transformer substation room in real time through a distributed sensor network, and building a multi-target optimization model through the genetic algorithm in combination with a variable-frequency speed regulation technology; and the noise and the temperature are used as constraint conditions to be jointly solved with an optimization target. The genetic algorithm dynamically adjusts the operation frequency of the exhaust fan through the operations of population initialization, fitness evaluation, selection, crossover, variation and the like, so that the requirements of temperature control and noise minimization are balanced. The rotating speed of the exhaust fan can be adjusted in a self-adaptive mode. Compared with a traditional single-machine fixed wind speed or simple linkage control method, the technology can remarkably improve the comprehensive energy efficiency of the system, reduce energy waste and improve the operation environment of the transformer substation room. The technology is suitable for noise and temperature control requirements of various types of indoor transformer substations and similar scenes, and has high applicability and popularization value.

The invention relates to a node switching method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: obtaining scores corresponding to a plurality of performance indexes of a target physical machine; inputting the scores of the plurality of performance indexes into a multi-target genetic algorithm model, and obtaining a target solution output by a multi-target genetic algorithm; the target solution represents a scheduling scheme of the pod resource and comprises a target node corresponding to the pod resource; and switching the source node where the pod resource is located to the target node according to the target solution. According to the method, by considering a plurality of performance indexes, service continuity and non-interruption are ensured, high availability of the system is realized, and meanwhile, selection of the target node is more flexible.

Disclosed are a genomic mating method for Huaxi cattle based on whole genome single nucleotide polymorphism (SNP) information and an application thereof. The method includes the following specific steps: step 1, extracting deoxyribonucleic acid (DNA) from to-be-hybridized Huaxi cattle individuals for genotyping; step 2, performing genotype data imputation to obtain high-density chip data; step 3, calculating an additive genetic relationship matrix, utilizing genomic best linear unbiased prediction (GBLUP) to obtain genomic estimated breeding values of five important economic traits of a to-be-hybridized Huaxi cattle population, and calculating a comprehensive selection index of the individuals; and step 4, using a genetic algorithm to construct a population optimal mating combination list. In the present invention, the breeding cost is greatly saved and an inbreeding level of offspring populations is reduced.

The invention discloses an improved NSGAII-based satellite ground station optimization layout method and device, and relates to the technical field of satellite ground station optimization layout, and the improved NSGAII-based satellite ground station optimization layout method mainly comprises the steps: generating an initial population according to a satellite ground station optimization layout problem parameter, and an optimization evaluation system is constructed, an improved non-dominated sorting genetic algorithm II is utilized to obtain a preliminary offspring population, a dynamic congestion degree selection mechanism is utilized to obtain an offspring population until the maximum number of iterations is reached, and a final optimization result is obtained. By implementing the satellite ground station optimization layout method and equipment based on the improved NSGAII provided by the invention, the problem of ground station layout optimization under double indexes of satellite-ground connectivity and ground station flow load balance can be solved.

The invention discloses a reconfigurable flexible job shop scheduling optimization method with secondary clamping constraint, and relates to the technical field of intelligent manufacturing and production optimization. The method comprises the following steps of: 1) establishing a mixed integer linear programming model considering a reconfigurable flexible job shop scheduling problem of secondary clamping by taking minimization of maximum completion time and minimum number of chemical workers as targets; 2) designing a three-segment coding mode and a decoding mode corresponding to the mixed integer linear programming model based on process sorting, machine selection and worker selection; and 3) based on the three-segment coding mode and the decoding mode, adopting an improved multi-target genetic algorithm to solve an optimal scheduling scheme of the mixed integer linear programming model. According to the method, processing machine selection, auxiliary module selection, processing sequence sorting and secondary clamping worker selection of a manufacturing workshop can be considered at the same time, the workshop production efficiency is improved, and the method has the advantages of being good in model performance, small in result fluctuation and high in stability.

The invention relates to the technical field of intelligent manufacturing, and particularly discloses a space-time constraint modeling-based multi-AGV-mechanical arm collaborative carrying system, which comprises a space-time constraint modeling module, a task decomposition and priority evaluation module, a collaborative path planning module, a dynamic collision prediction and adjustment module and an energy consumption and efficiency optimization module, according to the method, the total energy consumption, the total duration and the congestion index are subjected to normalization weighting, the unified fitness function is constructed, and the genetic algorithm and the particle swarm optimization are adopted for joint optimization, so that energy consumption minimization and operation period minimization can be taken into consideration at the same time, and a high-congestion section is actively avoided on path selection. According to the multi-target fusion, side effects caused by single index optimization are avoided, the sustainable operation capacity of the system is improved, the actual time length fed back after task execution, energy consumption and collision near-loss information are improved, model parameters are updated through a reinforcement learning or online incremental learning module, and self-evolution of a strategy is achieved.

The invention discloses an unmanned driving extreme case generation and verification method based on a simulation engine. The method comprises the following steps: S1, constructing a high-fidelity virtual simulation environment; s2, initializing a parameterized model generated by an extreme driving case; s3, generating an initial extreme driving case population based on a genetic algorithm; s4, collecting test result data of the unmanned driving system; s5, quantifying the test effect of each extreme driving case; s6, based on a fitness evaluation result, performing selection, crossover and mutation operation through a genetic algorithm; and S7, the extreme driving case population generated by evolution is subjected to unmanned driving system testing again in the simulation environment, S4 to S6 are executed repeatedly until a preset termination condition is reached, and the termination condition comprises that the scene diversity reaches a target value or the fitness is converged. According to the invention, the generation efficiency and coverage range of the extreme driving scene are improved.

The invention relates to a large-scale low-orbit satellite constellation network access node selection method, and belongs to the technical field of satellite communication. The method comprises the following steps: firstly, calculating positions and beam coverage conditions of high and low orbit satellites in an earth-fixed coordinate system at each moment through ephemeris, platform and load information of the satellites; secondly, designing an adaptability function according to the position relation between satellites, beam coverage, communication link channel quality and service demand conditions; and finally, a satellite suitable for serving as a network access node is selected from a large-scale low-orbit satellite constellation through a genetic algorithm. The method can be used for realizing efficient dynamic connection node selection in high-orbit and low-orbit satellite combined networking, so that functions of resource coordination, load balancing and the like among satellites are realized, and the deployment efficiency of a large-scale low-orbit satellite constellation is improved.

The invention belongs to the technical field of petroleum and natural gas drilling engineering, and particularly relates to a borehole friction coefficient segmented inversion method based on a genetic algorithm and string mechanics, which comprises the following steps of: acquiring specified data of a construction well, dividing a borehole of the construction well to be predicted into well sections with fixed lengths, defining depth ranges of the well sections, and setting a global search range of a friction coefficient; initializing the population size, the maximum number of iterations, the selection probability, the crossover probability and the mutation probability of the genetic algorithm, and performing downward inversion section by section from a wellhead section: initializing a candidate friction coefficient population of a current well section, setting a sectional friction coefficient, calculating a hook load at the tail of the current section, and calculating the maximum number of iterations; iterative optimization is carried out until a termination condition is met, and then the optimal friction coefficient of the section is output and fixed; and iteratively executing until inversion of all well sections is completed, outputting a full-borehole segmented friction coefficient profile, and realizing intelligent segmented inversion of the borehole friction coefficient by combining a tubular column mechanical model and a genetic algorithm, so that the prediction precision of the friction coefficient is improved, and the well drilling design and construction are optimized.

The invention belongs to the field of heat exchange equipment design, particularly relates to a heat exchanger optimization design system based on a genetic algorithm, and aims at solving the problem that heat exchange equipment designed by an existing design system for designing a shell-and-tube heat exchanger is low in performance. The heat exchanger optimization design system based on the genetic algorithm comprises a design index input module and a heat exchanger parameter optimization module. According to the system, seven key design parameters including the tube pass number, the tube outer diameter, the heat exchange tube length, the tube inner flow speed, the shell side flow speed, the tube wall thickness and the heat exchange tube number serve as optimization variables, and a fitness function with the heat transfer coefficient and pressure drop ratio as the target is established. A hybrid coding strategy is introduced to carry out differentiation processing on continuous parameters and integer parameters, an improved genetic algorithm is adopted to realize iterative evolution of a population through intelligent optimization operations such as coding, selection, crossover and variation, and finally, a global optimal parameter combination meeting engineering constraint conditions is converged.

The invention discloses a wind power generation tower drum steel-concrete composite structure design method based on genetic algorithm optimization. The method specifically comprises the steps that operation data and design parameters of different types of wind power generation tower drums under different working conditions are obtained to construct an original data set; secondly, determining a design target function and quantifying, and setting each target weight coefficient; a genetic algorithm population is initialized, and individuals are represented by design parameter codes; decoding individuals of the population to obtain an actual design scheme, performing structural mechanical analysis, and calculating mechanical property indexes; calculating an individual fitness value according to the target function and the weight coefficient; new individuals are generated through selection, crossover and mutation operation, whether termination conditions are met or not is judged, if yes, the design scheme corresponding to the individual with the highest fitness is output, and if not, iterative optimization continues. According to the method, the genetic algorithm is used for iterative optimization, the comprehensive performance of the design scheme of the steel-concrete composite structure of the wind power generation tower under multiple targets is effectively improved, and a better design result is obtained.

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.