59 results about "Mutation operator" patented technology

The invention relates to the technical field of flexible job shop scheduling in intelligent manufacturing and production scheduling, in particular to a flexible job shop scheduling optimization method for solving and adjusting resource constraints. Comprising the steps of initializing parameters and randomly generating an initial population; sequentially using a crossover operator and a mutation operator to evolve the current population; executing a hybrid decoding strategy on the current population; sorting the individuals in the current population from small to large according to the maximum completion time to form an elite population, and updating the elite population through question specific local search; and judging whether an evolution condition is met or not, if so, executing CP-based mathematical evolution, and outputting a final solution when the running time reaches the total running time. The method has the positive effects of reducing the resource waiting time, improving the machine utilization rate and improving the resource utilization efficiency and the scheduling performance of the whole workshop production.

The invention relates to the technical field of intelligent manufacturing, in particular to an efficient and energy-saving flexible job shop scheduling method considering adjustment time. Comprising the steps of 1, initializing an initial population of an MTCP-SPEA algorithm; 2, executing a spatial awareness environment selection strategy; step 3, judging whether the current running time of the MTCP-SPEA algorithm reaches half of the total running time, if so, executing step 6, and otherwise, executing step 4; 4, a crossover operator and a mutation operator are adopted to evolve the current population, and current non-dominated individuals are selected to form a Pareto solution set; 5, executing local search operation and returning to the step 2; and step 6, executing a multi-thread-based constrained programming auxiliary optimization method to generate an improved elite set, screening out all non-dominated individuals from the improved elite set to form an optimal solution set, and outputting the optimal solution set. According to the method, the problem of efficient and energy-saving flexible job shop scheduling optimization considering time adjustment is effectively solved.

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 application discloses a kind of multi-star on-orbit cooperation observation task planning method and system, by designing heuristic initial solution generation strategy, roulette selection algorithm based on Boltzmann selection probability, adaptive crossover operator and mutation operator based on population dispersion degree, stop condition based on population convergence coefficient, from initial solution, selection operator, crossover operator, mutation operator, stop condition etc. Various angles improve the genetic algorithm for multi-star cooperative observation task planning, compared with the genetic algorithm based on circle next cross, variation, significantly improve the execution benefit of observation task and the observation number of target point, construct the scheduling system of constellation cloud computing system, reach the purpose of on-orbit cooperation multi-star efficient execution observation task.

A coverage-guided code generation and fuzzing method based on a large model is proposed to address the problems of existing large model fuzzers, which not only ignore the guiding role of coverage information in the fuzzing loop but also generate prompts too hastily. This invention comprises four stages: prompt generation, fuzzing loop, mutation operator selection, and vulnerability detection. This method proposes a prompt generation method based on expert templates and a fuzzing loop method guided by unequal interval coverage information. The former lowers the barrier to entry for users of CoverFuzz and improves the quality of initial prompts; the latter, guided by coverage information, better covers the object under test to detect vulnerabilities. This invention significantly improves the effectiveness of programs generated by fuzzing based on large models, solves the problem of existing large model-based fuzzers ignoring coverage information, and also achieves higher code coverage.

The invention discloses a feature-aware variation protocol fuzz testing method, which relates to the field of network protocol testing and comprises the following steps: acquiring a protocol message sequence of a protocol to be tested as an initial seed; extracting a structural feature, a behavior feature and a core field type of a to-be-tested protocol to form a protocol feature description; classifying mutation operators in the protocol fuzzy test tool to form a mutation operator category set; protocol fuzz test initialization is completed; and generating a variation message sequence, sending the variation message sequence to the server, collecting and updating server information, and returning to continue execution if the preset protocol fuzzy test duration is not reached. According to the method, the protocol features are automatically obtained by using the large language model, and the mutation operator configuration adaptive to the protocol features is selected to carry out the mutation fuzzy test on the protocol, so that the action time of a low-efficiency mutation operator is shortened, and the protocol fuzzy test efficiency and the defect discovery capability are improved.

This invention belongs to the field of power grid fault diagnosis technology and discloses a power grid fault diagnosis method and system based on differential evolutionary logic operations. The method includes: collecting the action information of protection devices and circuit breakers in the power system; filtering out a set of candidate elements that have experienced faults based on preset association rules between faulty elements and protection / circuit breaker actions; establishing a 0-1 integer programming model based on the determined protection action information, circuit breaker action information, and candidate element set; and solving the 0-1 integer programming model using an improved differential evolutionary algorithm based on logic operations to determine the fault state of the candidate elements. This invention directly encodes individuals using binary, eliminating the need for floating-point to binary conversion. It also constructs a binary mutation operator and an improved crossover operator based on logic operations and employs adaptive parameter adjustment to balance the population diversity and search efficiency of the algorithm.

The present invention discloses a flexible job shop batch scheduling method considering carbon emissions, and solves the flexible job shop batch scheduling problem with completion time and carbon emissions as targets by improving the NSGA-Ⅱ algorithm. First, a mathematical model for flexible job shop batch scheduling is established; the relevant parameters of the improved NSGA-Ⅱ algorithm are set, and the individuals of the population are encoded using a four-segment encoding method, thereby initializing the population. Then, crossover and mutation operators are designed to perform crossover and mutation operations on the four genes of batch division, sub-batch workpiece quantity division, process sorting, and machine selection, respectively. The concept of a dominant and recessive gene is proposed, and a method for distinguishing dominant and recessive genes is designed, by which individuals can be effectively decoded. Then, excellent individuals are selected and retained through non-dominated sorting and crowding. Finally, the Pareto optimal solution set is output when the number of iterations is reached.

The invention discloses a multi-target influence maximization method based on cost constraint in a hypergraph, and belongs to the technical field of social network analysis, and the method comprises the following steps: S1, selecting an independent cascade IC model as a basic model; s2, evaluating double targets by adopting a multi-target optimization function; s3, according to a three-mode initialization strategy, diversified initial individuals are generated in different areas of a Pareto frontier (PF) through an HCI-based initialization module, a unit collective influence-based initialization module and a random initialization module; and S4, operator selection: a fast non-dominated sorting algorithm, a two-point crossover operator, a mutation operator, and a self-adaptive crossover rate and mutation rate mechanism are selected. Through the mode, the core technical problems that the influence on diffusion and seed cost cannot be considered in a hypergraph scene, and the evaluation and search efficiency is low are solved, and an efficient and extensible evolutionary multi-target solution is provided.

The present application relates to the technical field of flexible job shop scheduling in intelligent manufacturing and production scheduling, and particularly relates to an optimization method for solving flexible job shop scheduling with adjusted resource constraints. The method comprises the following steps: initializing parameters and randomly generating an initial population; using a crossover operator and a mutation operator in sequence to evolve the current population; executing a hybrid decoding strategy on the current population; sorting the individuals in the current population in ascending order of maximum completion time to form an elite population, and updating the elite population through problem-specific local search; judging whether the evolution condition is met, and if yes, executing mathematical evolution based on CP, and outputting a final solution when the running time reaches the total running time. The present application has the positive effects of reducing resource waiting time, improving machine utilization, and improving the resource utilization efficiency and scheduling performance of the entire workshop production.

The invention discloses a causal analysis-driven neural network model low-energy-consumption hyper-parameter recommendation method. The method comprises the steps of extracting modifiable hyper-parameters, performance and energy consumption measurement from a to-be-tested model according to specific requirements; designing a hyper-parameter variation range according to development requirements, and designing a variation operator according to the variation range; introducing a mutation operator into the original model to obtain a hyper-parameter mutation model; running the variation model to collect performance and energy consumption metrics; performing causal analysis on the measured performance and energy consumption metrics; and evaluating a tradeoff relationship between energy consumption and performance according to a causal analysis result, and recommending hyper-parameters. The method solves the problem that which hyper-parameters are more suitable for being used for optimizing the hyper-parameter method to reduce the energy consumption of the model.

The present invention discloses an improved genetic algorithm for solving flexible job shop scheduling, comprising the following steps: S1, initializing algorithm parameters; S2, generating an initial generation of chromosomes using a divide-and-conquer greedy initialization strategy; S3, extracting individuals with random numbers less than cp as the parent population for the crossover operator, sequentially extracting two chromosomes from the population as crossover parents chro1 and chro2, and using an improved POX operator to generate crossover children child1 and child2; S4, extracting individuals with random numbers less than mp as the parent population for the mutation operator, and adding the mutated individuals to a child population list childList; S5, merging the chromosomes in the child population childList into a total population list chroList; S6, selecting the parent of the next generation according to a tournament selection method; S7, assigning temp data to the total population list chroList; and outputting a global optimal solution. The present invention has the beneficial effects of initializing the initial generation of individuals using a divide-and-conquer greedy initialization strategy to ensure the generation of a relatively optimal initial solution, and proposing an optimal matching crossover method to improve the traditional POX crossover operator, thereby accelerating the convergence speed of the algorithm and improving the solution-finding efficiency of the algorithm.

The present invention belongs to the field of big data, and specifically relates to a method, a system, and a device for selecting a problem-solving layer of a multi-modal data model. This method is used to optimize the problem-solving layer in the partitioned order product space. It includes a preselection stage and an optimization stage. The specific steps are as follows: S1: Represent the problem-solving layer as chromosome individuals to generate a quasi-initial population. S2: Define a fitness function. S3: Iteratively update the quasi-initial population and save the optimal individual in each round of iterative update. S4: Improve the selection operator, crossover operator, and mutation operator in the classical genetic algorithm to obtain a new adaptive genetic algorithm. S5: Combine the randomly generated chromosomes and the sub-optimal population as the initial population in the optimization stage. S6: Iteratively update the initial population and select the optimal problem-solving layer after the iteration ends. The present invention solves the problems such as difficult convergence and easy entrapment in local optima when the existing classical genetic algorithm is used to handle this problem.

The application discloses a method for identifying cancer collaborative driving pathways based on a weighted mutation matrix, and comprises the following steps: 1) setting a nonlinear CMDP model; 2) setting an MPGA: 2.1) chromosome coding and initial population; 2.2) setting a fitness function; 2.3) selecting an operator; 2.4) a mutation operator; and 2.5) an MPGA algorithm specific process. The method can identify more biological information, has strong expansibility and practicability, and can identify genes enriched on multiple important driving pathways.

The invention is applicable to the technical field of software testing, and provides a mutation testing method based on a co-location coverage relationship, which comprises the following steps of: applying a mutation operator to a variable original statement of a tested program to generate a mutation statement, and constructing a corresponding mutation branch, on the premise of keeping the semantics of the original program unchanged, all variation branches are integrated to obtain a new tested program, then the tested program is executed by using a test case set, the coverage information of the test case on the original statement and the variation branches is collected, and whether a co-location coverage relationship exists between the original statement and the variation branches is judged according to the coverage information; on the basis of the coverage relation, it can be directly determined that a test case covers a variable original statement and also covers a variable branch of the variable original statement, and for variants which cannot be judged according to the in-situ coverage relation, weak variation testing is executed, and a variation score is calculated in combination with the actual killing condition. According to the method, the variation test execution amount is remarkably reduced, the test efficiency is improved, the resource consumption is reduced, and meanwhile, high variation scores and test quality are kept.

The present invention relates to the field of software testing, and in particular to a mutation operator simplification method and system for a deep learning model. The present invention uses each mutation operator to mutate the original model, obtains the hidden state sequence of each test sample in the original model and each variant during the training process, and after dimensionality reduction of each hidden state vector, clusters all the reduced hidden state vectors, regards each cluster cluster as an abstract state, and obtains the abstract state trace of each test sample in the corresponding model; for each original abstract state trace in the set of original abstract state traces, calculates the average value of the distance between it and each mutated abstract state trace in the set of mutated abstract state traces of each variant, obtains the mean difference between the abstract state traces of the original deep learning model and each variant, and determines the mutation operator to be simplified. The present invention reduces the execution cost of mutation testing and improves the effectiveness of evaluating the quality of image or text test case sets.

The application discloses a deep learning framework mutation testing method based on developer experience, which adopts a mutation operator and a constraint condition generation model based on the professional knowledge of developers to simulate the common operation of developers in the development process and detect more diversified defects in multiple stages (such as model training and inference) of the life cycle of a deep learning model. The method comprises two parts: first, model mutation, which is used to generate mutants; second, defect detection, which is used to perform defect detection. The model mutation comprises the following steps: first, the application adopts two deep Q networks to select a mutation operator and a seed model; then, the seed model is mutated under the constraint of criteria, and the Q network is updated according to the reward calculated by the current target evaluation; then, it filters out the mutants that violate any constraint, and adds the legal mutants to the generated model pool. The process is iterated until the mutation is completed. The defect detection comprises the following steps: first, the application detects the defects exposed in the mutants and records the defect reports. Then, based on the reward, the application selects part of the models and further executes them to detect defects in resource scheduling, execution crash, model performance and output precision. Through the method of the application, meaningless models in defect detection can be filtered, diversified defects can be detected in the execution stage of the model, the effectiveness of defect detection is improved, and the method has important application value and popularization prospect.

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.

The invention discloses a petrochemical inspection and maintenance project safety supervision human resource optimal configuration method, and particularly relates to the technical field of safety engineering. According to the method, a petrochemical enterprise safety management area model is constructed, a petrochemical enterprise inspection and maintenance operation safety supervision comprehensive efficiency objective function is determined, and a crossover operator and a mutation operator of a genetic algorithm are optimized by adopting a self-adaptive crossover probability and a self-adaptive mutation probability based on the genetic algorithm. And calculating an optimal solution of the petrochemical enterprise inspection and maintenance operation safety supervision comprehensive efficiency objective function to obtain an optimal configuration scheme of petrochemical inspection and maintenance project safety supervision human resources. According to the method, the influence of the dynamic change of the safety risk of the inspection and maintenance operation and the difference of the safety supervision efficacy of various types of workers on the operation construction site on the human resource configuration is comprehensively considered, and the optimal configuration of the safety supervision human resources of the petrochemical inspection and maintenance project under the condition of limited safety supervision personnel is realized; and a foundation is laid for safe production of refinery enterprises.

This invention relates to an intelligent multi-objective optimization method for solving infeasible solutions in complex chemical process industrial applications. This method uses a classification model to screen infeasible solutions and performs targeted mutations to increase the likelihood of transforming infeasible solutions into feasible ones. The method uses classification as a data-driven model to accurately identify specific infeasible solutions and perform targeted mutations. Differentiated mutation operators are used for different types of solutions to ensure that the mutation direction matches the characteristics of the solution. For specific infeasible solutions, uniform mutation is used, constrained by the variable range of Pareto solutions, to correct the solution to the feasible region. This avoids the waste of traditional methods by generating high-quality Pareto solutions early on and avoids local convergence. Validated in dual-tower side-stream extractive distillation and four-tower extractive distillation systems, this method not only significantly improves computational efficiency, reducing optimization time by 35.3% and 20.8% respectively, but also outperforms widely used genetic methods.

The invention discloses a test method of a text sentiment analysis task model based on a genetic algorithm. The test method comprises the following steps: S1, randomly selecting an original seed test case from existing text data; s2, mutation operator design is carried out, wherein a sentiment analysis mutation operator and a general text mutation operator are included; s3, constructing a search framework, calling the search framework based on a genetic algorithm, selecting original seed text test cases according to the evaluation score of the dual-target fitness function to obtain candidate test cases, applying the mutation operator to the candidate test cases in a combined manner to search for an optimal solution of the test cases, and automatically generating a final test case; and S4, testing the sentiment analysis task model to be tested by using the test case to obtain a defect evaluation result based on the metamorphic relationship. According to the method, a test means for comprehensively and efficiently evaluating the defects of the sentiment analysis model can be provided, and the evaluation efficiency and accuracy of the reliability and robustness of the SA system are improved.

The application discloses a test case automatic generation method and system based on MoMuTUML, reduces and deletes original function overlapping mutation operators, and adds three new mutation operators which are not functionally overlapped with the original mutation operators, so that the generation of a syntax equivalent mutation model is reduced, and the test case generation time cost is reduced; test cases are generated according to a mutation model set and a model under test, and equivalent mutation models are deleted according to the test cases, so that effective mutation models are obtained, the problem of too many equivalent mutation models in MoMuTUML is avoided, and the test case generation efficiency is improved; through a feedback-based mutation method, the mutation operators corresponding to the effective mutation models are scored, an optimal mutation operator set is constructed, and the mutation operators in the set are selected to perform mutation operations on the model under test, so that the problem of low efficiency of the MoMuTUML mutation method is solved, and the test case generation efficiency is further improved.

The invention provides a method for renaming an identifier in a mutation operator on the first aspect, which comprises the following steps of: acquiring a name and a position of the identifier in the mutation operator, and acquiring an available identifier; sorting the available identifiers according to the names and the positions; calculating the relationship strength of each sequence according to a set rule; traversing and calculating the maximum value of the sum of the relation strength to obtain a sorting result corresponding to the maximum value, and taking the sorting result as a renaming result. The invention also provides a compiler test program generation method, which comprises the following steps of: acquiring a mutation operator capable of inserting a seed program, and renaming an identifier of the mutation operator to obtain an insertion operator; determining insertion information according to the seed program; and inserting the insertion operator into the seed program according to the insertion information so as to generate the compiler test program.

The application provides a sensor network deployment method based on scene analysis for operator selection, based on a scene analysis technology for describing characteristics of a solution space where a population is located, states of multiple deployment schemes in the solution space are extracted, including Euclidean distances between all individuals in the population P and an optimal individual, differences in coverage rates, Euclidean distances between the top 10% individuals in the population P, and position differences of all individuals in the population P, and then a mutation operator is adaptively selected according to the extracted states to perform mutation on the population, a strategy mapping from solution space state information to a suitable mutation operator is realized, a user obtains a deployment scheme with higher coverage rate in limited time, the solving efficiency of the deployment optimization problem is greatly improved, and the sensor network deployment optimization problem is effectively solved.

The invention discloses a data processing method, device and equipment and a computer readable storage medium, and the method comprises the steps: constructing an initial prompt according to a grammar analysis file of a test database and database information of the test database, inputting the initial prompt into a pre-training model, carrying out the recognition processing of the initial prompt through the pre-training model, and obtaining a recognition result of the initial prompt; obtaining a first test case; obtaining a mutation operator, and performing mutation processing on the first test case through the pre-training model and the mutation operator to obtain a second test case different from the first test case; executing the test cases in the test case library through the test database to obtain a first execution result output by the test database, and determining a test report of the test database according to the first execution result; the test case library comprises a first test case and a second test case. By adopting the method and the device, the expandability and the coverage rate of database testing are improved.

This invention discloses a fuzzy testing method and system for aero-engine compilers based on a large language model. In the mutation hint generation stage, a seed program library is constructed using embedded programs with various structures and compiler test suite programs. Mutation operators are selected through diversified guidance strategies, and mutation hints are constructed based on hint templates. In the test generation stage, variant programs with complex data and control flows are generated using the large language model to enhance compiler test coverage. Test outputs are inserted into the program to monitor global and local variables, enabling the program to effectively detect the most harmful silent compilation errors. Furthermore, a front-end error feedback strategy is designed to improve the effectiveness of test programs generated by the large language model. In the differential testing stage, random differential testing and testing under different optimization levels are combined to detect back-end defects in the compiler. This invention can improve compiler test coverage and detect silent compilation errors.

This invention discloses a path optimization method based on cooperative aggregation and branch deviation, comprising the following steps: (1) Initialize the population and variables, and divide the population into two subpopulations s1 and s2; (2) Calculate the fitness of individuals in subpopulations s1 and s2 according to the fitness function; sort the fitness of individuals in the two subpopulations from smallest to largest, and select the optimal individual; (3) Keep the i1 and i2 individuals with the highest fitness in subpopulations s1 and s2 respectively, and take the remaining individuals as clustered individuals; (4) Apply the cooperative aggregation strategy to the clustered individuals; (5) Take the top m1% and m2% of s1 and s2 respectively according to fitness to synthesize a new population s3; (6) Update the individuals in s3 randomly using the replacement strategy, and update the global optimal individual; (7) Determine whether the termination condition has been met. If not, increase the population diversity using the crossover operator and mutation operator; (8) Iterate according to steps (2)-(7) until stopping, and output the optimal path.

The invention discloses an unmanned cluster cooperative task allocation method, and the method comprises the steps: constructing a multi-unmanned-aerial-vehicle cooperative task allocation model through constructing a three-objective optimization function which comprehensively considers the task execution efficiency, the resource consumption cost and the task comprehensive income, and combining a task resource demand and a single-machine single-task constraint; a dynamic task importance evaluation mechanism is introduced, and task priorities are updated in real time based on a distance attenuation model and target motion vector analysis; an improved genetic algorithm which introduces multi-modal initialization, a hybrid selection strategy, a self-adaptive crossover mutation operator and a constraint repair mechanism is adopted to carry out iterative solution on the allocation model, and an optimal task allocation scheme which meets multi-target and multi-constraint even conditions is efficiently generated. According to the method, an efficient, feasible and stable cooperative task allocation scheme can be provided for the unmanned cluster under the conditions of limited resources and dynamic task change.

This invention provides a fuzzy testing mutation scheduling optimization method based on the mayfly algorithm, belonging to the field of fuzzy testing. It includes: Step 1, constructing a lightweight mayfly population for each seed in the fuzzy testing process, where individuals in the population represent a probability distribution of a set of mutation operators; Step 2, selecting mutation operators for each seed in the queue according to a uniform probability distribution, performing mutation, and simultaneously collecting path coverage and crash detection as feedback information, and calculating fitness scores through normalization and weighting; Step 3, simulating diverse behavioral patterns of the mayfly population based on individual fitness scores, dynamically updating the probability distribution of mutation operators, and optimizing the probability distribution; Step 4, finally applying the optimized probability distribution to the next round of seed mutation to generate more effective test cases, and repeatedly executing the above steps to achieve continuous optimization of the mutation scheduling strategy, improving fuzzy testing coverage and vulnerability discovery capabilities.

This invention discloses an optimization method for emergency resource scheduling, belonging to the field of emergency resource scheduling. The invention transforms the emergency resource scheduling problem into a problem that can be handled by an optimization method through natural number encoding and decoding. For the optimization objective, nearest neighbor rules and time rules are used to ensure the diversity of the initial population and expand the search space of the optimization method. By improving global and local mutation operators, the diversity of the population during the search process is guaranteed. Based on the changing characteristics of the population's demand for global and local search capabilities during evolution, the population evolution process is divided into three stages, employing an adaptive parameter adjustment strategy. In each stage, a crossover rate and mutation rate more consistent with evolutionary characteristics are used to improve the search efficiency and convergence of the optimization method. This invention is applicable to the field of emergency resource scheduling, improving the efficiency and reliability of emergency resource scheduling.