85 results about "Mutation probability" patented technology

The present invention relates to the technical field of artificial intelligence for antenna array deployment, in particular to an intelligent arrangement method for array antenna elements and a clustering epigenetic optimization method. The intelligent arrangement method for array antenna elements comprises: performing encoding for a J_K array, and initializing an epigenetic algorithm so as to generate J+K genes. The present invention uses a clustering algorithm in machine learning to divide a population into sub-populations having different characteristics. Adaptive learning is performed on a portion of individuals in each type, and an epigenetics-based inheritance operation is performed on the basis of the fitness probability of population individuals. Then, minor random mutation operations are performed to generate a new sub-population. Algorithm hyperparameters such as population size and mutation probability participate in the evolution of individual genes, and adaptively change on the basis of evolutionary states. The invention solves the problems of low convergence speed and poor obtained results in optimization processes of current L-shaped array antenna system arrangement algorithms.

The invention discloses a big language model security vulnerability test method and device based on a genetic algorithm and attention distribution, and a medium. The method comprises the following steps: selecting an AdvBench data set as a vulnerability test problem; performing malicious vocabulary extraction and lexical element decomposition on a test problem sample in the AdvBench data set; randomly selecting a plurality of lexical elements as an initial population, and calculating and amplifying attention scores corresponding to the selected lexical elements; performing coding flipping on the initial population according to the mutation probability so as to optimize the population; inputting a test problem corresponding to the optimized population into a large language model, and judging model response; when the model response is refusal, determining that the large language model does not have security vulnerabilities; and when the model response is compliant, determining that the large language model has security vulnerabilities.

The invention provides a rotor-bearing system fault diagnosis method and system based on virtual-real fusion, and relates to the technical field of signal detection, and the method comprises the steps: deducing a mixed eccentric unbalanced magnetic pull expression, and building a rotor-bearing system twinborn model in combination with a Hertz contact theory; introducing rotor eccentricity and bearing inner / outer ring faults, and constructing a multi-fault working condition twinborn model; performing multi-parameter identification on the model through an improved genetic algorithm (introducing a feature sensitivity evaluation factor, a self-adaptive crossover mutation probability and simulated annealing) to obtain a corrected twin model; generating a twin fault sample based on the correction model, and training by using a one-dimensional cyclic generative adversarial network (designing a comprehensive loss function) with a classifier to generate a sample close to real distribution; and virtual and real fusion samples form a balanced data set, and fault classification is realized through one-dimensional convolutional neural network training. According to the method, the problems of fault data shortage, large difference between a twin model and real data and the like are solved, and the fault diagnosis precision and reliability are improved.

The invention discloses a temperature and humidity sensor data high-dimensional feature compression method based on quantum approximate optimization, and the method comprises the steps: inputting a temperature and humidity sensor time sequence data matrix, carrying out the quantum entanglement coding, and generating a quantum state; constructing a variable component sub-circuit of a quantum approximate optimization algorithm based on the quantum state, and dynamically updating circuit parameters through a genetic algorithm to obtain an output quantum state; performing projection measurement on the output quantum state to generate a candidate feature subset, updating a genetic algorithm population through quantum interference, and executing a dynamic crossover operation to generate a new feature subset; calculating a mutation probability by using a quantum gradient, executing an adaptive mutation operation, and optimizing a feature subset; the nonlinear relation between temperature and humidity data features is captured through quantum entanglement coding and quantum interference, dynamic parameter optimization and quantum gradient variation are combined, robust feature selection in a high-noise environment is achieved, feature compression efficiency and precision are improved, and the method is suitable for high-dimensional data processing in an Internet of Things scene.

The invention relates to the technical field of bioinformatics, in particular to a background noise mutation filtering method for detecting ctDNA through ultra-deep sequencing, a computer medium and a computer system. According to the method, on non-target areas in a section of area extending towards the left side and the right side of all target sites, according to mutation direction classification, the probability that the corresponding mutation direction is background mutation is counted as a baseline, and a model is constructed based on the target site sequencing depth and the background mutation probability; and calculating the maximum mutation times when the mutation is the background mutation as a threshold value for distinguishing the real mutation and the background mutation. The method is different from a traditional background noise filtering mode which depends on a normal sample to construct a baseline, the baseline can be constructed only by using the to-be-detected sample, the filtering model is constructed, and real mutation and background mutation are well distinguished; meanwhile, according to the method, performance indexes of different sensitivity and specificity can be realized through specific parameter adjustment, so that the requirements of different application scenes are met.

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

The invention discloses a network protocol fuzzy test method based on refined mutation probability learning, which comprises the following steps: 1) a multi-arm machine model is created based on a state transition edge in a state transition diagram of a target network protocol, and each arm represents an optional state transition path; 2) the fuzz tester sends the fuzz test case to a server program of a target network protocol through the target network protocol; based on the state transition diagram and the feedback of the server-side program, utilizing state transition edges to subdivide the protocol state in the server-side program to obtain a state transition edge set; 3) aiming at each state transition edge, selecting one arm from the multi-arm tiger machine model as a state transition path of the edge; 4) according to the test result of each fuzzy test, updating the sampling probability of a corresponding arm in the multi-arm machine model: if the state transition path i successfully explores a new state or path, increasing the sampling probability of the state transition path i; otherwise, the sampling probability is reduced.

The invention provides an instruction data matching experiment method, system and device and a storage medium, and belongs to the technical field of artificial intelligence, and the method comprises the steps: determining each data set participating in the fine tuning of a large model, setting the parameters of a genetic algorithm, and randomly generating an initial population; each individual in the initial population is used for fine tuning training of a large model; evaluating the large model after fine tuning training to obtain a fitness value corresponding to each individual; selecting an individual with the highest fitness value for generating a next-generation population; pairing the selected individuals, and performing crossover operation according to a set crossover probability; performing gene variation on newly generated individuals according to a set variation probability to generate a new generation of population; and replacing the current population with a new generation of population generated through genetic manipulation, repeatedly executing the steps until a set termination condition is reached, and outputting an individual with the highest fitness value in the new generation of population to obtain an optimal matching scheme. The data matching efficiency is improved, and the labor cost is reduced.

The invention discloses a flexible job shop scheduling method considering limited processing machine resources and multiple AGVs. The method comprises the following steps: S1, setting problem data and algorithm parameters; s2, performing three-section type coding to generate an initial population; s3, evaluating each chromosome by using an improved decoding strategy; s4, judging whether the maximum iteration number I of the algorithm is reached or not, and if so, skipping to S11; otherwise, if s = s + 1, skipping to S5; s5, calculating a fitness value and sorting the population; s6, selecting (m + 1) chromosomes through double strategies of elite retention and champion selection; s7, alternately selecting and grouping; s8, respectively selecting parent chromosomes P1 and P2 from the set X1 and the set X2 according to the crossover probability Pc; s9, selecting a parent chromosome P from the chromosome population according to the mutation probability Pm, and obtaining a filial generation C through random number optimization mutation and selection mutation; s10, updating the population, replacing a part in the initial population Xinit with the population obtained by variation, replacing the final (m + 1) chromosomes in population sorting with (m + 1) chromosomes selected by double strategies of elite retention and champion selection to obtain an evolved new population, and skipping to S3; and S11, outputting an optimal factory logistics scheduling scheme, calculating fitness values of all populations, and selecting the chromosome individual with the minimum fitness value for decoding to obtain the optimal factory logistics scheduling scheme.

The invention provides a remanufacturing workshop scheduling method based on reinforcement learning and evolutionary algorithm fusion, relates to the technical field of production scheduling optimization, and solves the problem of remanufacturing system scheduling in three stages of disassembly, reprocessing and assembly. A four-dimensional decision model covering process sorting, factory distribution, machine selection and speed gear scheduling is constructed, and an initial population with diversity and high-quality characteristics is generated by adopting a multi-strategy hybrid initialization method. A reinforcement learning decision module based on Q-learning is introduced, an optimal combination is dynamically selected from various crossover operators, mutation operators and neighborhood search strategies, the crossover probability and the mutation probability are dynamically corrected according to the distribution characteristics of the Pareto leading edge, and a dual escape mechanism is integrated to enhance the capability of the algorithm to jump out of local optimum. According to the scheme, the group search advantage of the evolutionary algorithm is exerted, the dynamic optimization of the search process is realized, and an efficient and accurate scheduling solution is provided for a complex remanufacturing system.

The embodiment of the invention discloses a motor fault diagnosis method and system based on multi-sensor data fusion and a medium. The method comprises the following steps: acquiring initial signal data of each sensor; performing noise reduction processing on the initial signal data through singular value decomposition; extracting an intrinsic mode function component of the denoised initial signal data, and obtaining reconstruction data according to the intrinsic mode function component; constructing a back-propagation neural network based on the reconstructed data, introducing crossover probability and mutation probability in a genetic algorithm to correct the weight and threshold of the back-propagation neural network, and determining a hybrid network; obtaining the fault diagnosis probability of each sensor according to the hybrid network; determining an evidence reliability factor according to the inherent reliability coefficient of each sensor and the inherent reliability coefficient of the hybrid network; the fault diagnosis probability of each sensor is corrected according to the evidence reliability factor, the evidence probability is determined, motor fault prediction is carried out through the evidence probability, and the accuracy and credibility of overall diagnosis are improved.

The invention relates to a multi-protocol self-adaptive multifunctional data acquisition system and method based on an FPGA (Field Programmable Gate Array) technology, in particular to the field of data acquisition, and is characterized in that electrical and time sequence characteristics of an original electrical signal accessed to a physical channel are analyzed in real time and are matched with a protocol characteristic library pre-stored on an FPGA chip to identify a protocol type and load corresponding analysis logic; performing numerical difference processing on the analyzed single sensor data sequence, calculating a physical quantity change rate and an acceleration, and inputting the physical quantity change rate and the acceleration into an FPGA built-in Bayesian network model to obtain a data point mutation probability; a risk state vector is formed by combining the sudden change probability and the spatial position coordinate of each sensor channel, the risk state vector and a preset catastrophe mode weight vector are subjected to Hadamard product operation to obtain a weight coefficient of each channel, and data acquisition frequency and processing resource allocation are adjusted; and performing tensor contraction operation on the multi-channel data with the aligned timestamps and a preset multi-dimensional failure mode feature kernel, and generating and preferentially sending a fusion early warning data packet when a result exceeds a corresponding risk threshold.

The invention relates to a planar array beamforming design method based on a genetic algorithm, and the method comprises the steps: simulating a planar phased-array antenna through employing an HFSS after a beamforming design target of the phased-array antenna is determined, and exporting an initial normalized directional diagram as an optimization object of an optimization algorithm; constructing constraint conditions, sketching an expected antenna directional diagram according to the constraint conditions, establishing an optimization problem, and judging whether the antenna directional diagram reaches a shaping design target or not in an iteration process by utilizing a fitness function; determining the mutation probability and the number of iterations, determining the number of individuals in an initial population, randomly initializing the gene length of the individuals in the population according to the number of the individuals, and carrying out iterative optimization on the antenna initial normalization directional diagram; and inputting an optimized result into an HFSS simulation model, and finally meeting a beamforming design target through judgment and comparison. The method has the advantages that the beamforming design is carried out through the weighting of the phase weighting bit, the sidelobe level of the directional diagram can be reduced, and the design of the flat-topped beam of the directional diagram is realized.

The invention provides an RPA script minimum increment repairing method based on evolutionary swarm intelligence and reinforcement learning, and relates to the technical field of RPA process automation, and the method comprises the steps: obtaining an event, and generating a rule JSON set; analyzing and mapping condition keys of the rule JSON set, and extracting a decision chain; performing rule coverage vector locking encoding on the decision chain, setting a mutation probability according to a structure influence factor to generate candidate individuals, crossing the individuals with similar structures, and sequentially completing grammar, variable and interface permission verification to obtain an initial patch candidate group with complete coverage; converting the initial patch script into a chain graph, inputting the chain graph into a local-global double-flow enhanced network for script optimization, outputting optimized patch candidates with composite scores, sorting according to the composite scores and the number of node changes, selecting a dominant pedigree to execute controlled micro mutation and block crossing, and obtaining a final patch script meeting a convergence condition; and carrying out full regression test on the final patch script, and after the test is passed, carrying out deployment and archiving a running log.

The invention discloses a hybrid learning-based flexible job shop energy-saving batch scheduling method, which relates to the technical field of shop scheduling and comprises the following steps of: establishing a flexible job shop energy-saving scheduling model by taking minimization of maximum completion time and total energy consumption of a machine as optimization objectives; the method comprises the following steps of: constructing a mapping relationship between an operation process and machine selection by adopting a three-layer coding mode, initializing a population through multiple initialization strategies, decoding a coding part, and generating an initial solution; constructing a neighborhood structure, performing population evolution through an adaptive crossover and mutation operator, and adjusting the crossover and mutation probability according to the population evolution degree; searching an optimal solution through a plurality of search strategies; traversing the population to perform non-dominated sorting, and adjusting the crossover mutation rate according to a non-dominated sorting result; and judging whether an iteration termination condition is met or not, if not, continuing iteration, and if so, outputting the optimal Pareto frontier solution. According to the invention, the energy consumption is reduced while the processing time is minimized.

The application relates to a house interior design selection method, a terminal device and a storage medium, and the method comprises the following steps: setting a chromosome code composed of a plurality of room codes and a room position code, setting the value interval of each room code and room position code; setting the maximum value and minimum value of each gene on all individual chromosomes; randomly generating an initial population; according to the input mutation probability and the crossover probability, the population is iteratively updated through the mutation rule and the crossover rule; drawing the corresponding house plan of each individual in the kth generation population before and after the crossover; when the selection result of the better house plan is received, the individual chromosome corresponding to the selected better house plan is used as the individual in the k+1th generation population. The application can enable the user to participate in the design process of the indoor plan in a timely manner, and improve the design efficiency and the user satisfaction.

The invention provides an online examination intelligent test paper composition method based on an improved genetic algorithm, and relates to the technical field of online education and information processing, and the method comprises the following steps: S1, designing a rule configuration module, randomly extracting test paper individuals as an initial population, S2, improving self-adaptive fitness function calculation, adjusting mutation probability parameters in genetic operation, and obtaining an initial population; and S3, optimizing a genetic manipulation process, dynamically adjusting gene diversity and ensuring an optimal solution. According to the online examination intelligent test paper composition method based on the improved genetic algorithm, by designing a rule configuration strategy, a user can conveniently define and design a test question layout and randomly draw test paper individuals as an initial population, the generated initial population has excellent individuals, and under rule limitation, test questions can be quickly divided from a large number of test questions, so that the test paper composition efficiency is improved. Therefore, optimization is achieved more quickly in the genetic manipulation process, the test paper generation speed is increased, and the topic coverage degree between different individuals in the same population is calculated through genetic diversity measurement.

This application discloses a sliding mode control method, device, equipment, and storage medium for a permanent magnet synchronous motor, belonging to the field of control technology. It includes: determining the objective function corresponding to M sets of optimized parameters included in the first-generation optimized parameters; determining the target set of optimized parameters included in the first-generation optimized parameters; determining the fitness of the M sets of optimized parameters based on the objective function corresponding to the M sets of optimized parameters; determining the crossover probability and mutation probability corresponding to each of the M sets of optimized parameters based on the fitness of the M sets of optimized parameters; generating the next-generation optimized parameters including M sets of optimized parameters based on the crossover probability and mutation probability corresponding to each of the M sets of optimized parameters; and so on, until the target set of optimized parameters including G-generation optimized parameters is determined; and controlling the permanent magnet synchronous motor through the sliding mode controller based on the target set of optimized parameters including G-generation optimized parameters. This application can improve the control effect of permanent magnet synchronous motors.

The present invention discloses a method, system, and medium for economic load distribution of a new energy power system. The method includes: constructing an objective function of the new energy power system; initializing a population according to preset constraints; calculating the fitness value of each particle according to the objective function and the position of each particle; calculating the individual optimal value of each particle and the global optimal value of the population according to the fitness value of each particle; updating the speed and position of each particle in the population according to a particle swarm algorithm; performing selection, crossover, and mutation operations on the population using a genetic algorithm to obtain a new population, and updating the crossover probability and mutation probability; when an end condition is met, outputting the particle corresponding to the global optimal value, and using the economic load parameter of each generator set at the position of the particle as the optimal distribution result. The present invention is planned according to the actual situation of the new energy power system, is more adaptable to the actual situation of the new energy power system, and can achieve optimal economic load distribution.

The invention relates to the technical field of artificial intelligence, in particular to a cue word automatic optimization method based on an evolutionary algorithm, and the method comprises the steps: generating an initial cue word population through analyzing a business demand, and verifying the validity; performing mutation operation of dynamic mutation probability adjustment on the current population to generate a filial generation candidate set; a standardized failure case set is used as an evaluation sample, and the combined population is subjected to test sorting; executing an elitism and tournament selection strategy based on a sorting result to screen high-quality individuals to obtain a next-generation population; judging whether a termination condition is met or not, and if yes, outputting a candidate optimal cue word; and carrying out automatic business verification on the candidate prompt words, and if the verification is not passed, automatically adjusting parameters based on failure reasons and triggering secondary iteration until the verification is passed. According to the method, directional optimization can be carried out on the business failure case, the evolution pressure is dynamically adjusted, the efficiency and accuracy of cue word optimization are improved, and automatic closed-loop iterative optimization of cue words is realized.

The invention discloses a single-target amino acid sequence optimization system based on comprehensive scoring and bit-by-bit iterative optimization. The system comprises the following modules: a mutation probability evaluation module, a target affinity scoring module and a comprehensive scoring module. The comprehensive scoring module and the bit-by-bit iterative optimization strategy of the system significantly improve the optimization efficiency, can quickly screen out high-quality sequences with high affinity and functional rationality in a complex sequence space, reduces the calculation complexity, and ensures that the screening result has scientific rationality and application value. The technology can accelerate optimization design of antibody drugs, target screening of polypeptide vaccines and functional improvement of protein engineering, and provides a reliable tool for drug research and development, precision medicine, personalized treatment and industrial biotechnology.

The application provides a high-efficiency fuzzy test method based on a score matrix, only normal basic blocks with a block score greater than 0 are subjected to a plug-in operation, and branches with a reaching probability of 0 are deleted through a pruning mode, which can prevent the fuzzy test of the application from generating new irrelevant test case inputs; therefore, it can be seen that the application avoids the fuzzy test tool of the application from exploring paths to irrelevant codes through the optimization mode; meanwhile, test cases in the seed pool of the application can trigger new paths reaching the target basic block or trigger vulnerabilities in the code to be tested, and the application selects a test case according to the final score and the mutation probability corresponding to each test case in the seed pool to perform mutation, and continues to test the vulnerabilities of the code to be tested according to the test case after mutation, so that the test case can reach the target basic block as much as possible through a high-score path, and the vulnerability mining efficiency of the software system to be tested is improved.

This invention provides a rotor-bearing system fault diagnosis method and system based on virtual-real fusion, relating to the field of signal detection technology. The method comprises: deriving an expression for the magnetic pull of mixed eccentricity and imbalance, and establishing a rotor-bearing system twin model based on Hertzian contact theory; introducing rotor eccentricity and bearing inner / outer race faults to construct a twin model for multiple fault conditions; identifying multiple model parameters using an improved genetic algorithm (introducing feature sensitivity evaluation factors, adaptive crossover mutation probability, and simulated annealing) to obtain a modified twin model; generating twin fault samples based on the modified model, and training a one-dimensional cyclic generative adversarial network with a classifier (designed with a comprehensive loss function) to generate samples that closely resemble the true distribution; and finally, forming a balanced dataset from the virtual-real fusion samples, which is then trained using a one-dimensional convolutional neural network to achieve fault classification. This invention addresses the problems of insufficient fault data and significant discrepancies between the twin model and the real data, thereby improving the accuracy and reliability of fault diagnosis.

The application discloses an outlet SO2 mutation early prediction network and a feedforward probability fusion method, relates to the technical field of industrial process control and intelligent prediction, and comprises the following steps: obtaining outlet SO2 and related measuring point historical sequence data collected by a distributed control system, performing sliding window organization processing on the historical sequence data, forming a historical data matrix containing multivariate time sequences, and synchronously generating corresponding observation identification information. The application realizes early discrimination of mutation probability by introducing a feedforward variable and a working condition self-adaptive time lag alignment mechanism, drives prediction fusion of a smooth path and a mutation path, completes integrated output of prediction and early warning, improves adaptability by using physical time lag modeling and multi-scale alignment, enhances mutation response capability by rolling self-fusion and control plan injection, and realizes the transformation of prediction from history dependence to causal driving.

The invention discloses a mechanical arm trajectory planning method and system based on a hybrid particle swarm optimization, and the method comprises the steps: carrying out the interpolation of a multi-degree-of-freedom mechanical arm through a 3-5-3-degree polynomial, setting four key nodes, and obtaining the interpolation angle of each joint of the mechanical arm at the key nodes through inverse kinematics analysis; and taking the duration time of each section of track as particles, obtaining an optimal time distribution scheme by using a particle swarm algorithm, and generating a motion track of each joint on the mechanical arm based on the optimal time distribution scheme. In the speed updating process of the particle swarm algorithm, performing three-stage updating on the particle speed; in the first stage, an initial update speed is obtained according to the speed and position of particles; the second-stage speed obtains an intermediate update speed based on the adaptive crossover probability updated with the number of iterations; and in the third stage, obtaining the final updating speed based on the mutation probability.

The invention relates to a low-carbon scheduling parameter determination method and device of a micro-grid and computer equipment. The method comprises the following steps: randomly initializing scheduling parameter values of multiple groups of target microgrids to generate an initial current fox population in a fox optimization algorithm; according to the fitness function, calculating the individual fitness of each fox individual in the current fox population; according to the individual fitness, a scheduling parameter value corresponding to each fox individual in the current fox population is updated, and a candidate fox population is obtained; and based on a preset mutation probability, performing mutation operation on each fox individual in the candidate fox population to obtain a new current fox population, and returning to the step of calculating the individual fitness of each fox individual in the current fox population according to the fitness function until the current fox population meets a preset iteration condition, and if yes, obtaining a target low-carbon scheduling parameter value of the target microgrid according to the current fox population meeting the preset iteration condition. By adopting the method, the scheduling effect of the micro-grid can be improved.

The invention provides an airborne flight control system-oriented sparse coverage edge feedback-based efficient fuzzy test method, which comprises the following steps that: a fuzzy test system initializes a seed queue and constructs an initial probability distribution model for each seed, and the mutation probabilities of all byte positions are equal; the fuzzy test system tracks an execution path of a test case in real time in a variation process, and dynamically updates an enhanced control flow diagram to record edge hit times and path information; the fuzzy test system calculates a path fitness score based on the edge hit times and the path depth in the enhanced control flow diagram, adjusts a probability distribution model of seeds according to increments, and improves the mutation probability of high-contribution byte positions; and the fuzzy test system selects byte positions and mutation operators according to the adjusted probability distribution model to generate a new test case, and detects whether a new path is triggered to update the seed queue or not until a termination condition is met. According to the method, the abnormal condition of the detected program is efficiently, accurately and reliably detected, and it is guaranteed that security holes and defects of the airborne flight control system are found in time.

This invention discloses a mapping-guided fuzzing method for loop code, applied in the field of software testing. The method includes: continuously mutating test cases, inputting them into a test case queue, sending them to the program under test (CUPT) with instrumented loop code structures, and outputting a coverage statistics table when the fuzzing termination condition is met; training a deep learning model and back-calculating the weighted distance sum to construct and output a mapping relationship between the byte sequences of test cases and the coverage of the loop code structure; based on the mapping relationship, assigning mutation probabilities to the corresponding byte sequences in the test cases corresponding to the loop code structure coverage and performing mutations to generate child test cases, which are then used as input to the CUPT for fuzzing until the fuzzing termination condition is met, and outputting a fuzzing report. This method mutates the byte sequences of test cases based on mapping relationships, enabling the targeted generation of test cases that improve the coverage of loop code results.

The invention relates to a rectification system multi-objective optimization method based on dynamic NSGA-II, and belongs to the technical field of chemical process system optimization. The method comprises: determining a decision variable and a target function according to a rectification system model; a diversity index is obtained by calculating an invalid solution proportion of each generation of population and diversity of a target and decision space; and the crossover probability, the mutation probability and the population size are dynamically adjusted, a dynamic NSGA-II algorithm is constructed, the rectification system is optimized, and an optimal solution is obtained. According to the method, dynamic parameter adjustment is achieved by introducing the invalid solution proportion and the population diversity index, the number of non-convergent solutions can be effectively reduced, the population diversity is enhanced, premature convergence is avoided, the calculation cost is reduced while the optimization efficiency is improved, and an efficient and practical solution is provided for multi-objective optimization of a complex chemical system.

The application relates to a distributed cache parameter optimization method and device, computer equipment and a storage medium. The method comprises the following steps: randomly generating multiple groups of distributed cache parameters as an initial population; inputting the initial population into multiple learners of a cache performance prediction model respectively, predicting the cache performance of the initial population, and determining the health value of the initial population according to the cache performance of the initial population; performing evolution processing on the initial population according to the health value of the initial population, a mutation probability threshold and a crossover probability threshold to generate a new population; updating the new population as the initial population, returning to the step of inputting the initial population into the multiple learners of the cache performance prediction model respectively, and iteratively performing evolution processing to obtain a group of distributed cache parameters with optimal cache performance. The method can improve the efficiency and accuracy of distributed cache parameter optimization.