145 results about "Mutation (genetic algorithm)" patented technology

The invention belongs to the technical field of indoor positioning, and discloses a Bluetooth path loss model parameter adaptive calibration method and system. According to the method, the historical learning mechanism and the hybrid optimization strategy are fused, so that the global search capability and the local optimization precision are considered while the dynamic updating of the path loss model parameters is realized. Compared with an existing calibration method of a fixed parameter or a single optimization algorithm, the method can automatically correct the model parameters according to the environment change, and solves the problem that the precision of a traditional model is reduced under the conditions of multipath effect, shielding interference and environment sudden change. A hybrid optimization framework of a genetic algorithm and a particle swarm algorithm is introduced, so that a parameter optimization process obtains a high-quality initial value in a global search stage, rapid convergence is realized in a local fine adjustment stage, and the calibration efficiency and precision are remarkably improved. And meanwhile, a historical learning mechanism is added, so that the model has a time memory characteristic, smooth mutation of historical parameter information can be fused, and the stability and robustness of the method in a complex dynamic environment are improved.

The method comprises the following steps: obtaining a power quality signal containing noise; selecting permutation entropy as an adaptive function of genetic algorithm, calling variational mode decomposition through genetic algorithm, and iteratively optimizing a penalty factor α and a decomposition mode number k of the variational mode decomposition to determine optimal parameters; decomposing signal data into k mode components through the variational mode decomposition, and determining effective mode components and noise mode components through a correlation coefficient; for improved wavelet threshold, a parameter-adjustable threshold function is proposed, and the concept of wavelet energy entropy is introduced into the threshold function; the noise mode components are denoised through the improved wavelet threshold, and the effective mode components and the denoised noise mode components are reconstructed to obtain a denoised power quality disturbance signal. The method can effectively remove noise interference while retaining singular information of mutation points of the collected signal, and provides help for subsequent analysis and treatment of the power quality disturbance signal.

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 relates to an islet repair ICK mode oral polypeptide optimization system taking VDAC1 as a target based on ESM2 and a genetic algorithm, and the system comprises a mutation module which is used for carrying out mutation and recombination by adopting the genetic algorithm according to original protein sequence data to generate an initial population; the prediction module is used for respectively inputting the protein sequence data of the initial population into a protein dimer dissociation energy prediction model, a protein toxicity prediction model and a protein cell penetrability prediction model to obtain a dissociation energy prediction value delta G, a toxicity probability value T and a cell membrane passing transmission probability Ed; and the optimization module is used for acquiring the optimal individual performance of the highest fitness score to output the Pareto optimal sequence index, and recording the hotspot residue distribution and the site mutation frequency at the same time. On the basis of keeping the biological activity of the original sequence, the key drug properties are improved in a breakthrough manner.

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 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 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.

This invention discloses a method, apparatus, medium, and device for parameter optimization of analog circuits, relating to the field of analog circuit technology. The invention automatically identifies current paths through a search algorithm to deeply analyze the circuit topology, determine the symmetry structure in the analog circuit, and constructs symmetry constraints based on the identical optimization parameters of electrical components within the symmetry structure. Simultaneously, it transforms the product of the transistor's gate width and the number of parallel connections into the total width and sets the optimization boundary. This combination reduces the dimensionality of the optimization variables while ensuring that the parameter optimization process conforms to the layout design of the analog circuit. Furthermore, it combines a population mutation algorithm and a non-dominated sorting genetic algorithm with an elitist strategy to perform iterative optimization within the constructed efficient search space, significantly improving the convergence speed of analog circuit parameter optimization, reducing computational costs, and enhancing the practicality of the analog circuit parameter optimization method.

The application relates to the technical field of power detection, in particular to a power inspection robot detection method and system based on machine vision. The application constructs a target region three-dimensional model through pre-acquisition of region basic data, and obtains historical defect data to mark a detection key region, then uses a genetic algorithm to optimize an initial path and key detection points, and removes redundant acquisition points, so that the accuracy of a visual detection path is improved; meanwhile, a path acquisition collaborative analysis module is used to analyze visual data in real time, generate an optimization instruction to dynamically adjust the path, and avoid the low efficiency and missing detection problems caused by independent operation of the visual and navigation; the application collects dynamic data of detection such as definition, vibration acceleration, environmental wind speed and environmental light through a visual perception module, analyzes and outputs a comprehensive fluctuation value through an environmental adaptability module, obtains a corresponding fluctuation value interval, generates corresponding environmental adaptability regulation instructions, and avoids the influence caused by motion blur and environmental mutation.

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.

This invention discloses an automatic calibration method for airborne lidar placement deviation based on voxel sharpness. The method includes: selecting a natural calibration site containing building facades, linear markers, and point targets; collecting point cloud and POS data via a UAV flying in multiple headings; modeling the placement deviation to be calibrated using six parameters; optimizing the parameters using a genetic algorithm; using voxel sharpness as the fitness function; voxelizing the point cloud in the region of interest; counting the number of points within each voxel and calculating the sharpness score; a higher sharpness score indicates a more pronounced clustering of point clouds in the feature region; and iteratively optimizing the method through selection, crossover, and mutation operations to output the optimal placement deviation parameters. This invention uses an ordinary basketball court as a natural calibration site, eliminating the need for artificial targets. After calibration, the point cloud sharpness is improved by more than 150%, and the positioning accuracy reaches the centimeter level.

The invention relates to the technical field of centrifugal pumps, in particular to an ultra-low specific speed unit transient pressure fluctuation suppression system and method.The method comprises the steps that a segmented broken line type guide vane closing rule is optimized, and decision variables are determined; determining a unit rotating speed water head ratio item in the target function of the multi-target optimization of the guide vane closing rule; according to the method, the'segmented broken line law 'of closing of guide vanes is optimized through a multi-target genetic algorithm, the'rotating speed water head ratio' is taken as a key regulation and control index, transient pressure fluctuation and runner hydraulic thrust are balanced, and the transient pressure fluctuation and the runner hydraulic thrust are controlled. The method is suitable for controlling transition processes such as starting, stopping or sudden load change of an ultralow-specific-speed unit, vibration and noise in the operation process of equipment can be reduced, the service life of the equipment is prolonged, the production efficiency is improved, equipment failures and safety accidents caused by pressure fluctuation are avoided, the production risk is reduced, meanwhile, energy waste is reduced, and the production cost is reduced.

The invention belongs to the technical field of drilling fluid deironing equipment, and provides a drilling fluid demagnetizing device parameter optimization method based on a genetic algorithm and a related device.The method comprises the steps that structure and process parameters of a drilling fluid demagnetizing device are obtained and converted into individual codes of the genetic algorithm, design parameters of the drilling fluid demagnetizing device are obtained and serve as gene codes, and the genetic algorithm is used as the individual codes of the drilling fluid demagnetizing device; each individual represents a device configuration parameter; constructing a fitness function based on the adsorption efficiency, the processing capacity and the cost of the drilling fluid demagnetizing device; and based on the fitness function, evaluating the fitness of each individual, performing selection, crossover and mutation operation on the population to generate a new generation of individuals, and when a predetermined evolution algebra is reached or the fitness reaches a predetermined threshold, completing optimization. Various parameters are allowed to be adjusted according to actual conditions by adopting a parameter optimization algorithm so as to adapt to different types of fluids containing magnetic substances, the flexibility is high, and the application field is wider.

The application 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. The method comprises the following steps: acquiring real-time operation parameters of a ship power system; constructing an initial population of the genetic algorithm based on the operation parameters; calculating a comprehensive fitness value of each individual based on energy consumption characteristic indexes and emission characteristic indexes; performing selection operation, crossover operation and mutation operation on the initial population to generate a new population; repeatedly performing population iteration optimization until a preset condition is met; outputting a combination of ship speed adjustment parameters, a generator set start-stop decision sequence and a power distribution scheme; and generating a ship power system control instruction set. The ship power system optimization scheduling method and system based on the genetic algorithm improve the operation efficiency of the ship power system, reduce energy consumption and reduce carbon emissions.

The application relates to the technical field of aluminum alloy structure design, and discloses an aluminum alloy net shell structure design method and system based on a genetic algorithm. The method receives design requirement parameter input by a user, including span, height and load conditions of the net shell structure; divides the net shell structure into multiple design units corresponding to nodes or rod pieces; obtains historical performance data of aluminum alloy material, such as strength, rigidity and durability, from a database; determines the importance level and constraint level of each design unit based on the above data; generates an initial population containing multiple design schemes by using a genetic algorithm; iteratively executes selection, crossover and mutation operations to optimize the design individual; collects structure response data such as stress, strain and displacement in real time in the iteration; confirms whether a preset constraint condition is violated based on the data, and adjusts the genetic algorithm parameter if the constraint condition is violated; and outputs an optimal design result and generates a structure analysis image for the user to view.

The present application relates to the technical field of data processing, and more particularly to a dimension reduction and feature extraction method based on mutual information and genetic algorithm, comprising using an improved mutual information formula to calculate mutual information to measure the role of each dimension for each class; using the mutual information value as the fitness value of the feature dimension, first using the roulette method in the genetic algorithm to generate multiple information-carrying feature subsets; then using mutual information to optimize the generated feature subsets in dimension; optimizing the feature subsets, controlling the mutation and difference degree of the optimized feature subsets, and generating new feature subsets; and fusing the evaluation results of the multi-source feature subsets. The present application considers the fixity of the dimensionality after transformation, the high efficiency and small influence of feature extraction, and the neglect of small features; the feature subset is mutated, the dimensionality of the reduced feature subset is changed, and the effective difference degree between the feature subsets is effectively controlled.

The application discloses a layout planning method based on reinforcement learning and a genetic algorithm, and comprises the following steps: 1, generating an original population of the genetic algorithm through perturbation B*‑tree; 2, calculating the fitness of all individuals, and selecting an individual with the highest fitness value as the optimal individual in the population; 3, according to the probability, selecting whether to use a crossover operator in an iteration, if the crossover operator is selected, selecting two parent individuals from the population, and generating offspring individuals through the crossover; 4, according to the probability, selecting whether to use a mutation operator in the iteration, if the mutation operator is selected, selecting a parent individual from the population, and generating an offspring individual through the mutation; 5, optimizing the offspring individual by using a reinforcement learning intelligent agent, and then determining whether to replace the parent individual according to the fitness of the offspring individual; and 6, the iteration is ended, and the optimal individual is obtained. The application efficiently reduces the area and line length of layout planning.

This invention relates to the field of structural design in building engineering, specifically to a method for optimizing the arrangement of pier-type energy dissipators based on genetic algorithms, including S1: establishing a non-vibration-damping structural model, calculating the frame node displacement results and the initial deformation [δ] of each span of each floor of the structure. 0 S2: Based on the genetic algorithm, a certain number of energy dissipator arrangement schemes are randomly generated, and each scheme is encoded to form a [sign] matrix; S3: Considering the additional damping ratio correction, the additional damping ratio of each energy dissipator arrangement scheme is calculated; S4: Genetic algorithm optimization is performed, different fitness functions are constructed for different optimal arrangements, individuals are selected for crossover and mutation, and genetic iteration is carried out to obtain the optimal energy dissipator arrangement scheme. This invention calculates based on the analysis results of the non-damping model, and uses a genetic algorithm to obtain the optimal energy dissipator arrangement scheme for each floor and each span of the structure based on the target additional damping ratio or the target number of energy dissipators.

The invention provides an inductance workshop scheduling method capable of reentering an outsourcing process, and the method comprises the steps: S1, arranging a machinable machine for the process of each workpiece based on an inductance process flow and a machine condition; s2, formulating a mathematical model of a reentrant inductance job shop scheduling problem according to actual production, wherein the mathematical model comprises an optimization target and constraint conditions; s3, based on the reentrant inductance job shop scheduling problem mathematical model, constructing a PPO sudden change controller and an improved GNN critical path predictor; s4, generating an initial scheduling plan by adopting an enhanced genetic algorithm fusing a PPO sudden change controller and an improved GNN critical path predictor; s5, performing key path analysis and optimization on the initial scheduling plan by utilizing topological sorting; s6, according to an outsourcing process completion event and a machine fault event, marking state update of an associated process or a machine, identifying an affected subsequent process set, extracting uncompleted processes to construct a temporary scheduling problem model, and re-planning arrangement of the uncompleted processes by adopting an enhanced genetic algorithm; and S7, repeating the steps S5-S6, and after an outsourcing process completion event or a machine fault event is processed, carrying out key path optimization again until the last process, so as to realize continuous dynamic adjustment of the scheduling plan.

The invention relates to the technical field of fluid conveying, in particular to a collaborative design optimization method for a guide vane type mixed-flow pump. Preliminarily designing the impeller according to the operating parameters of the design points, acquiring main geometric parameters, parameterizing the axial surface projection shapes of the impeller and the guide vane by adopting two constraints of geometry and size, and associating the axial surface projection parameters of the guide vane with the design parameters of the outlet of the impeller; according to the axial surface projection boundaries of an impeller and a guide vane, in combination with the structural characteristics and constraint conditions of the pumping chamber of the guide vane type mixed-flow pump, two constraints of geometry and size are adopted to carry out collaborative design on the pumping chamber; taking the initial design parameters and the geometric constraint parameters as optimization variables, determining a variable range, sampling by utilizing Latin hypercube sampling, and screening out key parameters by utilizing sensitivity to construct an error back-propagation neural network agent model; and a non-dominated sorting genetic algorithm with a penalty mechanism and an adaptive crossover variation attenuation strategy is introduced to carry out multi-target optimization on design parameters.

The invention relates to the technical field of legal artificial intelligence, and discloses a legal contract term AI examination method, which comprises the following steps: acquiring a contract to be examined, historical contract data and user game parameters, analyzing a contract text into a structured graph containing legal functional units, generating vectorized representation, and calculating the evolution distance between historical contracts to obtain the legal contract term AI. The method comprises the following steps: constructing a historical contract evolutionary tree structure, combining an evolutionary tree, a commercial risk scene and user game parameters, performing simulation deduction by applying a genetic algorithm, evaluating advantages and disadvantages of various strategy combinations through a payment function, guiding a mutation direction by utilizing evolutionary fitness scores of historical contracts, and searching an optimal strategy combination. And by comparing the difference between the optimal strategy combination and the original contract, an accurate modification suggestion or a complete contract draft is generated. According to the method, evolutionary intelligence of historical contracts is combined with game theory simulation, the limitation of static and template review in the prior art is overcome, and the strategic and personalized level of review is improved.

The invention discloses a heating ventilation air conditioning system intelligent control method and system based on reinforcement learning and genetic algorithm hierarchical collaboration. The method comprises the steps that an intelligent agent based on reinforcement learning is established, and an optimizer based on a genetic algorithm is established; acquiring a state vector of the HVAC system including historical, current and future prediction information in each decision cycle; the intelligent agent of reinforcement learning obtains strategic actions including equipment operation combination selection and macroscopic regulation and control targets according to the state vector; and taking the strategic action as a hard constraint condition, and carrying out iterative optimization on a genetic algorithm optimizer of the tactical optimization layer within a constraint range by utilizing an agent model to obtain an optimal real-time operation parameter combination so as to control the HVAC system. According to the method, the complex control problem is decomposed, the decision-making efficiency and the energy-saving effect of the control system are remarkably improved, unpredicted load sudden change can be effectively dealt with, and efficient and stable operation of the HVAC system is achieved.

The application discloses a kind of dairy cow cold stress multistage fuzzy comprehensive evaluation method, comprising the following steps: step (1): using analytic hierarchy process, from warm environment (temperature, relative humidity, wind speed and illumination), physiological factors (respiratory rate, body surface temperature) and air quality (NH3, CO2, PM 10 ) 3 dimensions construct dairy cow cold stress evaluation index system;Step (2): establish the judgment matrix of each index layer, and make consistency check, if meet the consistency requirement, then determine the weight of each index layer;Step (3): if it does not meet the consistency check, use genetic algorithm for testing and correcting the judgment matrix, the weight of each index layer is calculated by crossing, mutation and other operations;Step (4): the degree of cold stress of dairy cow is obtained by multistage fuzzy comprehensive evaluation;The application combines environmental parameters with dairy cow physiological parameters, and can realize comprehensive and accurate evaluation of dairy cow cold stress.

The invention relates to the field of production scheduling, in particular to an injection molding product production scheduling optimization method and system based on swarm intelligence, and the method comprises the steps: firstly analyzing the relevance and use frequency of a production order and a needed mold, constructing an adjacent fitness matrix according to the relevance and use frequency, and rewarding the order connection which continuously uses the same high-frequency mold through the matrix; and punishing the die change operation. Secondly, a structural integrity index is defined to assess the superiority of the order sequence fragment. Finally, the indexes are applied to an improved genetic algorithm, excellent gene blocks are intelligently protected and bad sequences are directionally repaired through self-adaptive crossover and mutation operation, and therefore rapid convergence is achieved in iterative optimization, an optimal production scheduling scheme with the shortest total mold changing time is generated, and the production efficiency is effectively improved.

The application discloses a genetic algorithm optimization method for a stacker picking sequence, comprising: obtaining a set of goods to be delivered out of a warehouse, the goods having a goods identifier, a goods location coordinate, a segment number and a priority; determining a single loading constraint based on the segment number and a loading segment number of a loading platform to limit the total segment number of the single loading goods to be less than the loading segment number; taking the goods identifier as a gene, initializing a population and setting a priority segment at the front end of an initial chromosome to preferentially arrange the first priority gene; in an iteration process, constructing an adaptability function based on a path length and a loading segment utilization rate, performing selection and performing cross and / or mutation to update the population, while keeping the first priority gene from participating in the cross and / or mutation; outputting an optimal solution when the iteration terminates, and performing a pass-by-pass split on the optimal solution according to the single loading constraint, and performing priority checking and adjustment pass by pass to ensure that the first priority gene is divided into the previous pass, thereby improving the executability and comprehensive efficiency of the delivery operation.

The invention provides a production scheduling optimization method based on deep reinforcement learning and genetic algorithm fusion. The method comprises a state sensing module, an initial scheduling generation module, a genetic algorithm optimization engine and a hierarchical DRL model. The initial scheduling generation module constructs an initial population, inputs the initial population into a genetic algorithm optimization engine to carry out multi-target fitness evaluation, executes selection and crossover operations, and generates an optimized population; and the state sensing module extracts scheduling state features of the optimized population in real time, inputs the scheduling state features into the layered DRL model, and outputs high-value variation actions. According to the method, a hierarchical DRL model is constructed, and an intelligent variation strategy of data driving and environment perception is provided in a GA iteration process; the search efficiency and the solution quality are obviously improved, and efficient and high-quality production scheduling solution is realized; according to the method, the intellectualization and autonomous evolution capability of scheduling optimization are realized, the dependence on expert experience and rules is reduced, the universality is high, and the method is suitable for different production line configurations and task fluctuations and can be migrated to other evolutionary algorithms.

The invention relates to the technical field of optical fiber transmission, and particularly discloses a low-crosstalk few-mode optical fiber structure optimization design method based on a genetic algorithm, and the method comprises the steps: determining a fixed structure parameter of a few-mode optical fiber, and setting an optimization range of a to-be-optimized groove structure parameter; within the optimization range, randomly generating N groups of trench structure parameters to form an initial population; aiming at each group of groove structure parameters in the initial population, based on a coupling mode theory, calculating a corresponding inter-mode crosstalk value under a preset bending condition; screening out M groups of parameters with high fitness from the current population as excellent individuals according to the crosstalk value; performing crossover and mutation operation on the excellent individuals to generate a new generation of population; outputting a trench structure parameter corresponding to the lowest crosstalk value in the previous population; according to the method, the deformation-crosstalk mapping relation can be quantified, and the optical fiber structure can be guided to meet the mechanical reliability requirement of benchmarking with the single-mode optical fiber while keeping low crosstalk.

The invention belongs to the field of production and manufacturing optimization, and particularly relates to a production scheme optimization method and device, equipment and a medium, and the method comprises the steps: generating a filial generation production scheme through the crossover and mutation operation of a genetic algorithm, so as to increase the diversity; dividing the combined candidate schemes into non-dominated layers with different quality levels according to the dominated relationship of the plurality of cost parameters; the production schemes are selected according to the priorities of the non-dominated layers from high to low, the distributivity of a solution set is guaranteed through crowding degree calculation in the same layer, the population is made to continuously approach the real optimal leading edge through iterative evolution, and finally the optimal production scheme is output. According to the method, under the condition that multiple targets are not artificially weighted into a single target, a batch of optimization schemes capable of achieving good balance among multiple competitive targets can be automatically found. Compared with a traditional optimization method, the method not only can ensure that the quality of the solution is quickly converged to the Pareto frontier, but also can maintain the diversity of the solution set through a crowding degree mechanism, and effectively avoids falling into local optimum.

The invention discloses a steel plate stack position distribution optimization method based on LOT combination and splitting and recombination, and the method comprises the following steps: S1, collecting steel plate parameters, including the LOT to which a steel plate belongs, the size specification and the ex-warehouse date, defining an LOT distribution unit, and determining the parameters of a storage yard; s2, designing a steel plate stack position distribution rule and an optimization model; s3, genetic algorithm parameters are set, the mapping relation between LOT and stack positions is represented through chromosome coding, and a preliminary stack position allocation scheme is solved through selection, intersection and mutation operation; s4, designing a genetic algorithm for simulated annealing optimization; and S5, carrying out second-stage LOT splitting and recombination operation through a hybrid algorithm to obtain a final stack position distribution scheme. According to the method, information such as LOT (machining batch) and ex-warehouse date of the steel plates is fully considered, the stacking position of the steel plates is reasonably planned, the number of occupied stacking positions is reduced on the premise that zero invalid plate turning operation is guaranteed, and the space utilization rate is increased.