319 results about "Crossover" patented technology

The invention relates to a visual-language model cue word evolution generation method based on a genetic algorithm, which comprises the following steps of: randomly generating different cue words under a target task to construct an initial cue word set, preprocessing the randomly generated cue words, and reserving N groups of cue words as an initial population; and designing a multi-dimensional fitness evaluation function to evaluate the performance quality of each group of cue words in a vision-language task, and selecting high-quality cue word individuals based on an elitism strategy. Performing crossover and mutation operation on the selected high-quality cue word individuals by referring to a genetic algorithm to generate a new-generation cue word population; and finally, carrying out iterative optimization for multiple times until a preset termination condition is met, and outputting an optimal cue word set. By the adoption of the visual-language model cue word evolution generation method based on the genetic algorithm, high-quality and diversified cue words can be automatically generated, the performance of a visual-language model is remarkably improved, and meanwhile the visual-language model cue word evolution generation method has good interpretability and adaptability.

System and method for hybrid analysis of quantum and classical genetic algorithms is disclosed. The method includes, receiving an input bitstring, the input bitstring being an output of a genetic optimization module, processing the input bitstring to generate quantum processed bitstrings, mutating the input bitstring, mutating the quantum processed bitstrings as a function of the input bitstring and the mutated input bitstring, performing crossover on a combination of the mutated input bitstring and the mutated quantum processed bitstrings, and selecting a set of individuals from the quantum processed bit strings and output of the crossover. The method further includes, determining, after selecting the set of individuals, if the hybrid analysis is complete or incomplete based on predetermined criteria, returning, in response to the hybrid analysis being incomplete, the set of individuals as the input bit string, else, outputting the set of individuals as a result of the hybrid analysis.

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.

There is provided a system and methods to determine an improved solution to a Mixed Integer Problem (MIP) using a quantum-assisted MIP solver. The methods are performed by a digital processor in communication with a quantum processor. Methods include: selecting at least one feasible solution determined by an MIP solver, determining a first sub-problem of the MIP based on the at least one feasible solution; casting the first sub-problem as Binary Quadratic Models (BQMs); solving the BQMs using the quantum processor to generate sample solutions; determining a second sub-problem based on at least the sample solutions, and obtaining a current solution to the MIP by evaluating the second sub-problem; and updating an incumbent solution if the current solution improves over the current incumbent solution. The quantum-assisted MIP solver uses hybrid crossover and mutation heuristics to improve the convergence time and accuracy of solutions obtained using Branch-and-Cut solvers.

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 discloses a multi-objective continuous optimization method for a chemical reaction kettle. The method comprises the following steps: S1, initializing a population and a trainable parameter set; s2, calculating an individual target value and a gradient, and obtaining a performance steepest improvement direction after standardization; s3, constructing a conjugate direction in combination with historical gradients, and identifying sensitive key dimensions of the target function; s4, fusing the gradient and the conjugate direction to generate a main direction candidate solution, and applying refined disturbance to the sensitive dimension to generate a structural disturbance candidate solution; s5, updating the Pareto optimal solution set, and if a termination condition is met, outputting a multivariable optimization parameter set such as temperature-pressure-flow of the reaction kettle for actual operation; otherwise, returning to S2; according to the method, through gradient correction, conjugate direction fusion, sensitive dimension directional disturbance and parameter adaptive adjustment, the efficiency, precision and stability of multivariable optimization of the reaction kettle are remarkably improved.

The invention discloses a green robust independent parallel locomotive scheduling method with uncertain processing time. The method comprises the following steps: acquiring a to-be-scheduled parameter set; constructing an irrelevant parallel machine scheduling model taking worst scene completion time WC and scene average energy consumption MTEC as double targets based on the parameters; a scene-driven double-population discrete artificial bee colony algorithm is adopted for solving, and the method comprises the steps of population initialization, employed bee global search based on ternary championics and two-point crossing, division into two sub-populations according to MTEC, MN local search based on a mean value scene, WN local search based on a worst scene, LN observation bee self-adaptive neighborhood search based on Q-learning and scout bee disturbance. And finally, outputting a robust scheduling solution set with both robustness and low-carbon property according to a Pareto criterion. And a plurality of scheduling schemes considering robustness and energy consumption optimization are provided for decision makers.

The invention relates to the technical field of intelligent breeding and logistics optimization control, and solves the technical problems of high energy consumption, unstable efficiency, unbalanced distribution, lack of an intelligent scheduling mechanism and the like in feed conveying of a multi-storey pig farm. The method comprises the following steps: acquiring static parameters (physical characteristics of feed, physical attributes of a conveying system and a pig house structure) and dynamic parameters (real-time feeding requirements, equipment and material states and external environment factors); establishing a multi-objective optimization model of a collaborative optimization energy consumption model E (x), a time model T (x) and a conveying balance degree model U (x); solving by adopting a genetic algorithm with a special design crossover and mutation operator to obtain an optimal scheduling scheme; in the execution process, a closed-loop self-learning calibration mechanism is started, actual power is measured through a current sensor, and when the deviation between predicted energy consumption and actual energy consumption exceeds a preset threshold value, efficiency parameters in the energy consumption model are reversely corrected through a gradient descent method; the weight coefficients of the three models are dynamically adjusted according to the real-time electricity price and the inventory state. The system adopts a three-layer architecture of a perception and data acquisition layer, a decision and control core layer and an execution layer. According to the method, multi-target collaborative optimization and intelligent adaptive scheduling are realized, the total energy consumption is effectively reduced, the transmission time is shortened, the distribution balance degree is improved, and the energy consumption prediction accuracy is remarkably improved.

The invention discloses an ecological flow process multi-objective optimization method based on an NSGA-II algorithm, and relates to the technical field of water conservancy projects. Comprising the steps of defining discrete decision variables, setting an objective function, constructing constraint conditions and a mixed penalty function, and configuring algorithm parameters; generating an initial solution meeting the ecological flow constraint, and repairing the solution which does not meet the constraint after genetic manipulation to ensure the feasibility of the solution; performing non-dominated sorting on the population to divide Pareto frontier layers, calculating a congestion distance of a solution in each frontier layer, realizing genetic manipulation through tournament selection, binary crossover simulation and polynomial variation, and updating the population in combination with environment selection; and extracting non-dominated solutions, screening optimization schemes from the non-dominated solutions, and outputting monthly scheduling schemes and water transfer and ecological flow shortage statistics of each reservoir under each scheme. According to the invention, automatic distribution of the ecological base flow, the basic ecological flow and the target ecological flow in the interannual period is realized.

The invention discloses an underwater multi-target routing method based on an improved non-dominated sorting genetic algorithm. The method comprises the following steps: firstly, arranging sensor nodes in a target water area, networking, and encoding a feasible path from a source node to a target node by adopting a segmented structure; secondly, constructing a routing multi-target fitness function, and introducing a dynamic weighting function to form a comprehensive fitness index; and finally, grading the population by using an improved non-dominated sorting genetic algorithm, and exploring a better path through crossover and mutation operations. And designing a reference point elite selection strategy, improving the coverage of the solution set in the target space, and outputting an optimal routing path until the algorithm converges. According to the method, the dynamic balance among different optimization targets can be realized, and the link quality is considered while the energy consumption and the time delay are reduced, so that the network routing performance is improved.

The invention relates to a low-temperature high-strength steel lining polytetrafluoroethylene material based on a novel plasticizer and a preparation method of the low-temperature high-strength steel lining polytetrafluoroethylene material, and belongs to the field of anticorrosive materials. The method comprises the following steps: constructing a multi-dimensional associated training set by taking a historical plasticizer component ratio, tensile strength, elongation at break and temperature resistance indexes of a material and sizes and functional parameters of different products as input; by defining a multi-objective fitness function, synthesizing the material strength, low-temperature toughness and cost economy, utilizing the global search capability of genetic algorithm selection, crossover and mutation operation, and combining with a simulated annealing algorithm, the local optimization characteristic of collaborative iterative optimization is realized by dynamically adjusting the inferior solution accepting probability, and the optimal proportioning scheme of the plasticizer is gradually approached. According to the SA algorithm, global exploration and local development are balanced through a temperature attenuation mechanism, the GA premature convergence problem is effectively avoided, the robustness of a matching scheme is improved, and the efficiency and precision of material performance optimization are remarkably improved.

The invention relates to a water supply network multi-leakage node positioning method based on hydraulic model and WLS-GA fusion, and belongs to the technical field of water supply network leakage detection. According to the technical scheme, the method comprises the steps that firstly, a weighted least square method WLS is adopted to conduct single-leakage-point leakage amount estimation on all nodes of a pipe network, candidate leakage points with the high leakage possibility are screened out based on residual errors and a sensitivity matrix, then the candidate leakage points are combined, finally, an optimized combined solution serves as an initial population, and the leakage amount of all the nodes of the pipe network is estimated. The improved genetic algorithm GA is adopted to carry out multi-leakage-point accurate positioning, and the final multi-leakage-point position and leakage amount are determined through adaptive crossover probability and elitist retention strategy iterative optimization. The method has the beneficial effects that the search space of the genetic algorithm is reduced through weighted least square method pre-screening, the calculation efficiency is improved, the algorithm is prevented from falling into local optimum by improving iterative optimization of the adaptive cross strategy and the elitist retention strategy, and the convergence speed of the genetic algorithm is improved.

The invention provides an adaptive optimization method for process parameters of a process forming scheduling workshop, and the method comprises the following steps: carrying out the feature extraction and feature alignment of the multi-modal data of the process forming scheduling workshop, and obtaining the feature vector of each modal data; according to the contribution degree of each modal data to the target, calculating a modal weight, and carrying out weighted fusion on the feature vectors; the method comprises the following steps: respectively coding process parameters and processes into chromosomes, constructing a fitness function according to feature vectors after weighted fusion, calculating influence weights of the process parameters based on the sensitivity and modal contribution degree of the process parameters, and adaptively adjusting crossover probability and mutation probability of each parameter gene according to the influence weights. Solving an optimal process parameter and a process scheduling scheme; and inputting the optimal process parameters and the process scheduling scheme into a simulation system, comparing the optimal process parameters and the process scheduling scheme with an on-line actual measurement result, if an error between a predicted defect rate and an actual defect rate exceeds a threshold value, updating the step length of genetic search and the contribution degree of each mode and then carrying out iteration again, otherwise, issuing the optimal process parameters and the process scheduling scheme to a production line for execution.

The invention discloses a large model output data security detection method and system based on adversarial attacks. The method comprises the following steps: constructing and optimizing a strategy space containing a plurality of attack strategies, and grading and sorting the strategies to improve the attack efficiency; generating a single-strategy antagonism prompt by the attack model according to the optimized strategy space, and performing effectiveness evaluation and feedback correction on the prompt by the judgment model; inputting a prompt passing the evaluation into the target large model to obtain a response, and performing malicious degree scoring on the response by the judgment model; and if the single-strategy attack is not successful, introducing an optimization mechanism based on a genetic algorithm, generating a more complex multi-strategy antagonism prompt through strategy variation and crossover, and carrying out iterative attack until the target large model is successfully broken into the prison. According to the method, the security defects of the large model can be efficiently and comprehensively detected in a self-adaptive and multi-strategy attack mode.

The invention provides an energy-saving cloud manufacturing multi-target scheduling method and system for improving rate-driven heterogeneous aggregation, and the method comprises the steps: A, setting algorithm parameters, job attributes and machine constraints, generating a weight vector and a neighborhood, and randomly binding an initial aggregation method; b, generating an initial population, performing heuristic decoding, and initializing an ideal point and an external archive set; c, calculating a dynamic switching threshold value based on the current iteration progress; d, executing sequential crossover and swap mutation operators to generate offspring individuals, and performing heuristic decoding based on consistency increment evaluation; e, updating an ideal point and maintaining an external archive set; f, executing self-adaptive environment selection according to the dominating relation and the relative improvement rate, and updating a neighborhood solution and a bound aggregation method; and G, if the termination condition is not met, returning to the step D, otherwise, outputting a non-dominated scheduling scheme set. The method has the advantages that the convergence problem under the multi-target conflict is effectively solved through self-adaptive cooperation of heterogeneous strategies. According to the method, heuristic batch decoding and time sequence linkage are adopted, a heuristic decoding algorithm with cluster constraints is designed, through real-time calculation of idle increments and switching losses, deep fusion of cross-process and cross-region resources is achieved, the cooperation efficiency of the whole cloud manufacturing process is guaranteed, and the maximum completion time and the total manufacturing cost can be balanced on the premise that production constraints are guaranteed; and thus, a high-quality collaborative scheduling solution set is stably obtained.

The invention discloses an intelligent production scheduling method and system based on a fixed sequence genetic algorithm. The method comprises the following steps: dividing independent production areas of a workshop; key operation is determined, and modeling is carried out on time of the key operation; generating a process set containing a fixed sequence key job based on a process path, performing crossover variation by using an improved genetic algorithm, and keeping the sequence of internal processes of a task unchanged to avoid an invalid solution; and calculating the shortest processing duration through population iteration, and generating a production scheduling Gantt chart. The system comprises a workshop function area module, a key operation type module, a key operation modeling module and a production scheduling result output module, so as to realize the output of an intelligent production scheduling result. The method improves the production scheduling efficiency and the applicability of the production scheduling result, and is suitable for complex production scenes such as electronic assembly and cabinet debugging.

The invention provides an unmanned aerial vehicle cluster weapon target collaborative allocation method and system under space-time constraint, and the method comprises the steps: defining a collaborative allocation object, constructing a collaborative combat scene target function, defining a collaborative combat scene constraint condition, and finally solving the target function through employing a multi-gene population parallel ant colony algorithm, encoding the weapon set, the target set and the unmanned aerial vehicle set into a weapon gene sequence, a target gene sequence and an unmanned aerial vehicle gene sequence respectively to generate an initial population, and performing staged joint optimization by fusing a pheromone guiding mechanism of an ant colony algorithm and crossover mutation operation of a genetic algorithm, the optimal cooperative allocation scheme is searched when the constraint condition is satisfied, the unmanned aerial vehicle task allocation result can be better obtained and optimized through the cooperative allocation mode, and the unmanned aerial vehicle cooperative allocation efficiency is improved.

The invention discloses a resource-constrained scene-oriented user interactive neural architecture search method, and relates to the technical field of neural architecture search. The method comprises the following steps: setting initial parameters; the method comprises the following steps: pre-defining a search space, coding the architecture of the search space, and generating an initial parent population containing different candidate architectures; on the basis of a current parent population, an offspring population is generated through crossover and mutation operation, then the performance of all individuals, namely candidate architectures, in the population is evaluated, a performance index penalty value and an optimization target are calculated, the population is updated through a two-stage hierarchical non-dominated sorting strategy, and therefore the performance of the individuals, namely the architectures is optimized step by step; based on the user interaction frequency m in the evolutionary search parameters, triggering user interaction once every m generations, adjusting preference weights according to user instructions, and guiding the next architecture optimization direction; and when the user interaction is not triggered, judging whether a preset iteration termination condition is met, and if the iteration termination condition is met, outputting the current Pareto frontier solution set for the user to screen the architecture and deploy.

The invention discloses a game optimization solving method for a multi-user power control problem in a wireless network, and the method comprises the following steps: building a multi-user power control game model, and arranging a joint strategy vector x according to the transmission power decision of a user; designing a fitness function F (x); selecting an elite individual with F (x) closest to 0 in the genetic algorithm population; and updating a local search radius by adopting a cosine annealing algorithm, performing optimization comparison on elite individuals in combination with a particle swarm optimization algorithm, forming a new generation of population in combination with new individuals obtained by selection, crossover and variation, and outputting an individual with F (x) closest to 0 after iteration, namely a Nash equilibrium solution as a final power control decision. According to the method, a game problem is converted into an optimization problem by designing a specific fitness function, so that the calculation complexity and the implementation threshold are remarkably reduced; and the method has low requirements on the property of a cost function, does not need to be continuous or differentiable, has relatively high robustness and universality, and can efficiently solve a continuous game problem.

The invention discloses a limited transportation resource flexible job shop scheduling system and scheduling method based on a novel evolutionary algorithm, and relates to the technical field of optimal scheduling, and the method comprises the steps: calculating the process completion time of a new scheduling scheme, and generating an optimal scheduling plan; performing non-dominated sorting on the optimal scheduling plan, calculating a congestion distance of a solution, and performing solution set screening to generate an optimal Pareto solution set; and integrating the optimized objective functions, generating comprehensive objective functions, and selecting an optimal scheduling scheme corresponding to the minimum comprehensive objective function based on the optimized Pareto solution set. According to the invention, a self-adaptive crossover and mutation probability adjustment mechanism is introduced through the genetic scheduling module, the global search capability of the evolutionary algorithm is improved, an optimization objective function is constructed through operation data, and the equipment utilization rate and the production efficiency are improved in combination with a self-adaptive evolutionary strategy and multi-objective optimization.

The invention provides a cooperative path planning method for searching and tracking an underwater target by a multi-USV unmanned cluster, and the method comprises the steps: introducing a pose-control quantity-time to carry out the three-dimensional double-chain coding of a population in an improved genetic algorithm, enabling a control instruction and an accurate execution timestamp to serve as a genetic gene, and enabling the control instruction and the precise execution timestamp to serve as a genetic gene in the crossover and mutation operation, timestamps, control quantities and poses are transmitted synchronously, and it is ensured that offspring individuals can inherit an excellent cooperation mode of a parent; during population initialization, USV is guided to preferentially cover a high-value region through region division and weighted Gaussian distribution, so that blindness caused by random initialization of a traditional genetic algorithm is overcome, and invalid search is avoided; a target for avoiding secondary search is designed in a fitness function, and repeated access to recently searched areas is quantified by introducing a concept of confidence time intervals, so that an algorithm actively explores unexplored water areas, waste of search resources is avoided, and accuracy and planning efficiency of multi-USV collaborative path planning are improved.

The invention discloses an inverse kinematics method for a redundant seven-degree-of-freedom robot. The method specifically comprises the following steps: step 1, establishing forward kinematics modeling of the seven-degree-of-freedom robot; step 2, inputting a target pose, and defining a tail end pose error of the seven-degree-of-freedom robot as a Riemannian distance on a three-dimensional special Euclidean group manifold; 3, adopting a three-stage hybrid optimization strategy; 4, carrying out constraint processing between joints; and 5, verifying convergence and robustness. The adaptive differential evolution algorithm is used as a core optimizer, parameters of the adaptive differential evolution algorithm can be dynamically adjusted along with an iteration process, and global exploration and local development capabilities are balanced; and a dynamic parameter adjustment strategy of nonlinear progressive increase of a scaling factor and linear progressive decrease of a crossover probability is designed, and a DE / current-to-pbest variation strategy is adopted, so that the algorithm pays attention to global exploration in the initial stage of iteration and is biased to fine development in the later stage, and the convergence speed and the global search capability are effectively improved.

The application belongs to the field of workshop scheduling, and aims at solving the problem that the existing production scheduling method is only applicable to the homogeneous factory model.The method comprises the following steps: S1, establishing a distributed heterogeneous flow shop scheduling model; S2, initializing the basic parameters of the meme algorithm, and generating an initial population according to a hybrid initialization strategy; S3, evaluating the fitness value of the population individuals, and performing a Pareto non-dominated sorting; S4, performing a crossover operation on the individuals in the population with a certain probability; S5, performing a collaborative search on the population by using a search operator; S6, merging the offspring population and the parent population, and selecting half of the individuals as local search targets; S7, performing a local search on the selected target individuals; S8, updating the population; S9, if the algorithm meets the stopping condition, ending the algorithm process, and outputting a non-dominated solution set; otherwise, the algorithm goes to S4 and continues iteration.

The application provides a three-period minimal surface intervertebral fusion cage design method, relates to the technical field of porous structure design, and determines a design domain of a three-period minimal surface TPMS porous structure region, combines individual vectors of modeling parameters of corresponding TPMS of each region, randomly generates a plurality of individual vectors, and forms an initial population; finite element simulation is carried out on the TPMS porous model corresponding to each individual vector, so as to divide the initial population into a load advantage population and a pore advantage population; the individual vectors are selected from the load advantage population and the pore advantage population respectively to perform improved neighborhood directional crossover operation; an optimal three-period minimal surface TPMS porous structure region is obtained through iterative genetic algorithm; through improved crossover operation and neighborhood directional crossover between the load advantage population and the pore advantage population, the new generation of individuals can consider both load performance and pore performance, and optimization is avoided around a single performance.

The present application relates to a kind of based on hydraulic model and WLS-GA fusion's water supply network multi-leakage node positioning method, belong to water supply network leakage detection technical field.The technical scheme is: firstly, using weighted least squares WLS to each node of pipe network is carried out single-leakage-point leakage estimation, based on residual and sensitivity matrix, filter out the candidate leak point with higher leakage possibility, subsequently, candidate leak point is combined, finally, the optimized combination solution is used as initial population, using improved genetic algorithm GA to carry out multi-leakage-point accurate positioning, through adaptive crossover probability and elite reservation strategy iteration optimization, determine the final multi-leakage-point position and leakage quantity.The beneficial effects of the present application: weighted least squares pre-screening reduces the search space of genetic algorithm, improves the calculation efficiency, through the improved adaptive crossover strategy and elite reservation strategy iteration optimization, avoid algorithm to fall into local optimum, improve the convergence speed of genetic algorithm.

The invention belongs to the technical field of job shop scheduling, and particularly relates to a self-learning job shop scheduling method meeting waiting time constraint, which comprises the following steps: S0, constructing a job shop scheduling problem model with waiting time constraint; the method comprises the following steps: S1, acquiring job shop scheduling problem data, a configuration algorithm and operation parameters; s2, constructing chromosome individuals, generating chromosomes and initializing a population; s3, calculating the fitness value of each chromosome individual; s4, forming a new generation of population; s5, combining the fitness information of the current population, dynamically selecting a crossover rate Pc through a Q learning algorithm, and performing crossover operation on the population; s6, dynamically determining a mutation rate Pm in the same parameter combination space by using a Q learning algorithm, and performing mutation operation on the crossover progeny to generate an updated population; and S7, judging whether an iteration termination condition is met or not. According to the method, a feasible and near-optimal scheduling scheme can be efficiently generated on the premise of ensuring that the inter-process waiting time constraint is met.

The application provides a mixed-band controlled SDN controller deployment method and device, wherein the method comprises the following steps: taking network delay, load difference and bootstrap time of an SDN system as optimization targets, taking cost as a constraint condition, and determining a target function; based on a third generation non-dominated sorting genetic algorithm, a feasible solution construction algorithm, a crossover mutation algorithm, a removal of isolated node algorithm, and a random intervention and update reference point algorithm, the target function is solved to determine an optimal solution of a Pareto frontier; and the optimal solution of the Pareto frontier is taken as a controller deployment scheme of the SDN system, and the controller of the SDN system is deployed. Thus, the SDN controller deployment problem under the condition of multi-objective optimization can be effectively optimized, and the requirements of low delay, load balancing and the like can be met.

The invention discloses a dynamic path planning and task allocation method and system for wheat harvesters and grain transport vehicles. The method comprises the steps of obtaining basic information of a plurality of to-be-planned farmlands, performance parameters of a plurality of harvesters and performance parameters of the grain transport vehicles; an improved genetic algorithm model for dynamically adjusting a crossover mode and a crossover probability based on a self-adaptive strategy is adopted, iterative optimization is carried out by taking the minimum operation time as a fitness function, and a full-coverage path planning and task allocation result of the harvester on the farmland is generated; according to the real-time states and path information of the harvesters and the performance parameters and real-time states of the grain transporting vehicles, idle grain transporting vehicles are dynamically allocated to the harvesters in the full warehouse state through a distributed auction algorithm, and when the harvesters break down, uncompleted tasks are input into the improved genetic algorithm model again for dynamic reallocation and path planning. According to the method, the problems of dynamic path planning and task allocation of cooperative operation of the harvester and the grain transport vehicle group during large-scale production of wheat are solved.

The invention relates to a reinforcement learning-based river-lake-underground water combined dispatching optimization method, which specifically comprises the following steps of: performing space-time normalization on river-lake-underground water combined dispatching data, and defining a space-time feature mapping function to complete mapping of the data to a new feature space; constructing a joint scheduling model based on the mapping output, and defining a state space, an action space and a reward function to obtain a state vector, an action vector and a reward value; optimizing a genetic algorithm, constructing a reinforcement learning agent in combination with an actor-commentator architecture, initializing a network weight and a population by using state space clustering information, screening elite individuals by calculating individual fitness, fusing multiple features to realize adaptive crossover variation to update the population, synchronously updating an actor network and an action vector, and setting a re-learning mechanism; and after strategy fusion update and action execution are completed, final optimization of joint scheduling is realized based on the optimal strategy parameter and the network update weight. According to the invention, the optimization performance and adaptability of the scheduling strategy are improved.

The application provides a high-dimensional multi-objective evolutionary method based on an improved dominance criterion, which is used for optimizing the wiring design of a very large scale integrated circuit physical design and comprises the following steps: step one, convergence of a non-dominated solution set is ensured according to a defined convergence index, and an adaptive parameter based on a genetic algorithm niche is combined to control the diversity of the solution set, the radius target and the line length target of the wiring design are optimized by minimizing MOP, and the dominance criterion is improved; step two, a convergence index and a diversity index are designed, the two indexes jointly constitute a dynamic fitness function, and individuals with better convergence and diversity are adaptively reserved to perform MaOEA-IDR environment selection; and step three, an adaptive t distribution crossover operator is provided, which can balance the global search capability of a Cauchy operator and the local exploration capability of a Gaussian operator in a high-dimensional space, and the ASDX adaptive distribution crossover operator is adaptively adjusted. t The application can effectively solve the wiring problem of the very large scale integrated circuit physical design.