7 results about "Mutation operator" patented technology

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.

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.

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.

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.

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.

This invention relates to the field of de-icing prediction technology, specifically a method for predicting the de-icing jump height of transmission lines based on WOA-GA-GRNN, comprising: S1, acquiring prediction data of the de-icing jump height of transmission lines, including structural parameters, load parameters, and material parameters; S2, constructing a GRNN network, determining the number of neurons in the input layer based on the number of parameter variables in the structural, load, and material parameters; S3, using the smoothing factor used in the GRNN network as the individual to be optimized in the WOA population, performing crossover and mutation operations on the WOA population using the crossover and mutation operators of the GA algorithm, with the minimum fitness function value as the optimization objective, and iteratively optimizing to obtain the optimized smoothing factor; S4, configuring the optimized smoothing factor into the GRNN network to obtain a WOA-GA-GRNN hybrid prediction model, inputting the structural parameters, load parameters, and material parameters of the transmission line to be predicted into the WOA-GA-GRNN hybrid prediction model to output the de-icing jump height, thus achieving a more accurate and stable prediction of the de-icing jump height of transmission lines.