41 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 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 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 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.

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 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 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.

The invention discloses an adversarial sample detection method based on a model statistical decision, and relates to an adversarial sample detection method. The invention aims to solve the problem that an automatic modulation identification network based on deep learning is easily attacked by an adversarial sample. The invention provides an efficient detection and low-overhead defense strategy for a modulation signal adversarial sample, and provides an adversarial sample detection method based on a model statistical decision to deal with the security problem that an automatic modulation recognition network based on deep learning is easily attacked by the adversarial sample. According to the method, innovative Gaussian blur, weight stochastic and other mutation operators are utilized to destroy the priori knowledge dependence of an attacker on a target model, so that the detection capability of LCR on an adversarial sample is remarkably improved. When the mutation model is generated, a front and back layer freezing strategy is used, so that the calculation overhead and the algorithm cost are reduced, and the classification accuracy of the original samples and the adversarial samples is improved by combining mutation model screening and threshold optimization mechanisms. The invention belongs to the technical field of wireless communication.

This invention provides a traffic scheduling method for TSN-5G high-speed rail onboard networks based on a reinforcement immune algorithm. The method includes: obtaining all possible routes for the set of traffic to be scheduled in the TSN-5G high-speed rail onboard network, and the latency of each route; setting various operators for the reinforcement immune algorithm, using a DQN network as the cloning and mutation operator for the reinforcement immune algorithm, and comprehensively considering latency and load as the optimization objective of the reinforcement immune algorithm; the reinforcement immune algorithm iteratively using various operators to obtain the transmission order and route of each traffic, as well as the transmission time of each traffic at the TSN switch; obtaining a gated scheduling list for the TSN switch based on the transmission time of each traffic; and the TSN switch performing end-to-end deterministic transmission of each traffic according to the gated scheduling list. This invention, by combining an immune algorithm and reinforcement learning, overcomes the shortcomings of existing scheduling methods, improves scheduling efficiency and flexibility, and achieves deterministic scheduling of critical traffic within the TSN-5G train onboard network.

The invention discloses a picture data deep learning model robustness enhancement method based on a mutation operator, and the method comprises the following steps: applying disturbance to an input image through a data layer mutation operator, and generating mutation data containing natural scene disturbance; applying structure or parameter disturbance to the original deep learning model by using a model layer mutation operator to generate a plurality of mutation models; respectively inputting the variation data into the original model and each variation model for cross validation, and screening out error-prone samples based on loss differences output by the models; adding the screened error-prone samples into a training set, and retraining the original model to obtain a robustness-enhanced deep learning model; according to the method, the model robustness is systematically improved through a collaborative disturbance mechanism of the data layer and the model layer, and meanwhile, the training resource consumption is reduced by adopting a dynamic screening strategy.

This invention provides a network attack detection method and apparatus based on a binary classification model, comprising the following steps: Step 1: Calculating network parameters and preprocessing them; Step 2: Genetically optimizing the weights of the binary classifier, wherein the weight vector of the binary classifier is evolved through crossover, mutation, or permutation operators, and the optimal weight configuration is selected; Step 3: Using the genetically optimized binary classifier to detect network attacks, wherein the preprocessed network parameters are used as input feature vectors and input into the trained binary classifier, and the network traffic is judged to be normal or abnormal based on the comparison result between the output of the binary classifier and a preset activation threshold. This technical solution can quickly and accurately detect abnormal traffic in the network, reducing false positives and false negatives.

The application discloses a remanufacturing system scheduling method of diversified reprocessing routes, and the remanufacturing system comprises a disassembly subsystem, a reprocessing subsystem and a recombination subsystem. In the establishment of a scheduling model, the coordination among the three subsystems is considered, and diversified reprocessing routes are also considered. Then, a hybrid meta-heuristic algorithm is used to solve the scheduling model, the number and sequence of products to be disassembled (recombined) on the disassembly (recombination) workstation are determined, and suitable reprocessing routes are selected to repair defective components with different damage types and damage degrees, and the performance of the solution is improved through the integration of adaptive parameters, efficient migration and mutation operators, local search strategies and restart strategies. The application can quickly obtain a better scheduling scheme, and improves the coordination and efficiency of the remanufacturing system.

The application discloses a kind of based on evolution algorithm and clustering grouping field optimization method, comprising: step one, initialization field energy deposition coefficient and minimization objective function;Step two: generate initial population, and set initial parameter value;Step three, by clustering, the minimization objective function is grouped dimension reduction using Spearman cooperation function, the objective function is reduced to low-dimensional space from original space, reduce algorithm cost;Step four, by evolution algorithm optimization field set, using crossover mutation operator generates offspring, and selects individual by environment selection iteration, finally obtains a set of optimal field set scheme.The application can effectively reduce the time and space consumed in large-scale field optimization problem, and ensure the high quality of the obtained field set scheme.

The invention discloses a virtual machine testing method and device, equipment, a storage medium and a program product, and belongs to the field of software testing. The method comprises the steps of obtaining a first test program, wherein the first test program comprises a target statement; determining a first mutation operator corresponding to the target statement from a plurality of mutation operators; based on the first mutation operator, performing mutation processing on the target statement in the first test program to obtain a second test program; and determining a test result of the to-be-tested JVM according to the first operation information when the to-be-tested JVM compiles and executes the second test program. The multiple mutation operators are determined based on the multiple compiling optimization behaviors of the instant compiler in the JVM to be tested, so that the second test program generated after the target statement is subjected to mutation processing through the mutation operators can cover at least one compiling optimization behavior. Therefore, when the to-be-tested JVM compiles and executes the second test program, the compiling defect of the to-be-tested JVM can be quickly and effectively detected.

The embodiment of the invention provides a unit test analysis method and device, equipment and a storage medium, relates to the technical field of software testing, and obtains a test report through optimized assertion verification and variation testing to determine the effectiveness of unit test codes. The method comprises the following steps: determining a unit test code corresponding to a tested code needing to be subjected to unit test in a code file in the aviation business field; the unit test code is used for testing whether the tested code is valid; performing assertion logic test on the unit test code; under the condition that the unit test code passes the assertion logic test, modifying a logic statement of the tested code according to the mutation operator library, and determining a mutation copy of the tested code; performing variation test on the unit test code based on the variation copy; based on the variation test and the assertion logic test, determining an effectiveness evaluation result of the unit test code; and outputting a unit test analysis report based on the effectiveness evaluation result of each unit test code.

The invention relates to an obstacle avoiding X structure bounded deflection wiring method under time sequence relaxation constraint, and belongs to the technical field of integrated circuit computer aided design. According to the method, an efficient preprocessing strategy is provided, and the relationship between four wiring methods between any two pins and all obstacles is recorded by constructing an edge obstacle table. A genetic algorithm is combined with a particle swarm optimization algorithm, and a mutation operator and a crossover operator are introduced as a discrete updating mode of a particle swarm. A local time sequence optimization strategy is provided, and the purpose of optimizing the worst negative relaxation value is achieved by optimizing the wiring radius. An effective obstacle avoiding strategy is provided, and the wire length is optimized as much as possible on the premise that wiring does not pass through obstacles. A path refining strategy is provided, the wiring structure with the highest wiring resource sharing degree is selected to replace the original wiring structure, and then the purpose of optimizing the wire length is achieved. According to the invention, the wire length and radius of wiring are optimized, and the stability of the circuit is greatly improved.

This invention relates to a dynamic air traffic control method based on a multi-tree evolutionary programming algorithm, belonging to the field of air traffic management technology. This invention constructs a dual-knowledge-tree collaborative architecture, independently encoding and co-evolving the two sub-tasks of route selection and flight priority ranking, thereby achieving overall optimization of the control strategy. It designs a structurally constrained mutation operator and a two-stage parent selection mechanism based on behavioral similarity to improve evolutionary efficiency and offspring quality while ensuring the semantic validity of the rules. Through a low-level heuristic simulation environment, the knowledge tree is mapped into executable control instructions, ultimately generating an efficient air traffic control scheme adapted to the dynamic airspace environment.