46 results about "Evolutionary computation" patented technology

The invention relates to the technical field of data processing, in particular to a multi-modal sensor data real-time fusion processing method for a smart community. According to the method, a sensor network topological graph is constructed, a connection weight is optimized, distributed clock synchronization is realized by using a graph Laplacian matrix, and clock drift prediction and compensation are performed in combination with a fractional Brownian motion model; performing wavelet transform decomposition on the sensor data after time sequence alignment, calculating each scale Hurst index, predicting a load trend through a fractal prediction model, and outputting an optimal resource allocation scheme through hybrid evolution calculation; the method comprises the following steps: constructing multi-modal sensor data into a graph structure, extracting node features by using a graph convolutional neural network, obtaining global feature representation by using a self-attention mechanism, and performing anomaly detection classification in combination with a resource utilization rate and a prediction error; an anomaly detection feedback mechanism is established, and Laplacian matrix eigenvalues and weight parameters are dynamically adjusted; the real-time performance, the accuracy and the robustness of data fusion processing are improved.

The invention discloses an AI business process automatic generation method based on AI model combinatorial optimization, and the method comprises the following steps: constructing a multi-level AI capability decoupling and reconstruction module, and carrying out the bottom-up hierarchical modeling and top-down modular decoupling, dynamic mapping of an AI atomic power layer, an AI modular production capacity layer, an AI general capability layer and an AI application business layer is realized; an elastic AI capability combinatorial optimization module is constructed, AI capability combinatorial optimization oriented to three dimensions of data, features and models is carried out based on service quality requirements, and multiplexing, combination and arrangement of AI capabilities are realized through evolutionary computation optimization driven by an agent model; and an AI business process automatic generation module is constructed, an AI service containerization deployment scheme is generated and optimized based on data-driven process mining and a hyper-heuristic algorithm, and AI business process automatic generation is realized. According to the invention, the adaptability and execution efficiency of the AI technology in a complex scene can be improved.

The invention discloses an automatic algorithm configuration method based on deep reinforcement learning in evolutionary computation. The method comprises the following steps: determining a problem set; obtaining initial data for constructing a Markov decision process; defining a state vector of each individual in a time step in an evolutionary computation algorithm by using an individual state vector according to the initial data; constructing feature vectors of population features, individual features and development features according to the initial data; defining a continuous action space according to the initial data, and jointly controlling the selection of all individuals on hyper-parameters; based on whether a better solution is found or not, a reward mechanism is set, and maximization of expected benefits is achieved; and the deep reinforcement learning agent uses an attention mechanism to control dynamic hyper-parameters of the algorithm, so that a Markov decision process is realized, and automatic algorithm configuration is completed. According to the method, direct mapping from high-dimensional sensing information to continuous action space output is realized, configuration of individual exploration and development tradeoff is dynamically adjusted, and a balance mechanism of exploration and development is optimized.

The invention discloses an abnormal sample detection and identification method and system based on evolutionary computation and multi-modal consistency constraint, and belongs to the field of network security and artificial intelligence security. The method comprises the following steps: S1, acquiring and preprocessing input data; s2, multi-modal feature extraction and unified characterization are carried out; s3, optimization feature selection and weight self-adaption are carried out; s4, constructing a cross-modal fusion detection model; s5, abnormal sample risk identification; and S6, result judgment output: generating a final detection label according to a threshold function. The method shows high accuracy and strong robustness in counterfeit image and video detection and abnormal sample elimination, and can be widely applied to the fields of multimedia authentic identification, intelligent security and protection and AI content traceability.

The invention discloses a constrained multi-task optimization method and system based on task cooperation and resource self-adaption, and belongs to the technical field of evolutionary computation and multi-task optimization. The method comprises the steps that two populations are established based on each task; a feasibility priority strategy is adopted to evolve one population, and a domain adaptation strategy based on constraint relaxation is adopted to evolve the other population; after each evolution cycle, individuals in a preset proportion are selected for evolution based on the two populations of the same task, and progenies generated by evolution are included in a progeny set; later generations generated by evolution are evaluated through environment selection, and a roulette algorithm is adopted to carry out resource adaptive allocation on an evaluation result until an evaluation counter reaches the maximum function evaluation frequency; mating is carried out based on the populations corresponding to different tasks, and generated offspring is used for randomly replacing individuals in the offspring set. According to the method, different optimization strategies are adopted to evolve and optimize different populations, the adaptability of the algorithm is enhanced, and resource waste is reduced.

This invention belongs to the field of multi-objective optimization technology, specifically involving a trend-guided dynamic multi-objective optimization evolutionary method, including: S1. Trend modeling and direction construction in intelligent scenarios; S2. Construction of perturbation and search mechanisms under trend guidance; S3. Multi-objective knee point identification and feedback reinforcement mechanism; S4. Evolutionary computation parameter control mechanism; S5. Elite sparse resampling and trend collaborative scheduling optimization. The advantages of this invention are: it constructs a closed-loop optimization system that integrates trend prediction, knee point reinforcement, perturbation generation, and adaptive feedback control, and achieves a comprehensive improvement in the convergence, directionality, diversity, and stability of the solution set in a dynamic environment, significantly enhancing the search directionality and global convergence speed, avoiding the inefficiency problem caused by relying on random perturbations, and helping to quickly locate high-quality optimal solution sets.

The application provides a multi-unmanned agent-oriented cooperative firepower attack strategy generation method, and belongs to the field of intelligent game strategy generation. The method comprises the following steps: constructing an adversarial environment, generating an initial strategy population, performing parallel calculation on the fitness of the strategy population, performing crossover and mutation on the strategy population, and iteratively evolving the strategy population. The application solves the problems of large calculation amount and long training time of the intelligent game strategy generation method, represents the strategy as an action sequence, constructs fitness based on game winning rate, uses parallel multi-opening evolutionary calculation method, and intelligently, automatically and efficiently generates a cooperative firepower attack strategy with the fitness as the optimization target.

The invention discloses an emergency multi-target material scheduling method and device based on a multi-task evolutionary framework. The method comprises the following steps: acquiring initial information required by emergency material scheduling; constructing a multi-task evolution framework comprising an original emergency material scheduling task and a simplified auxiliary task based on the initial information; in the multi-task evolution framework, population iteration optimization based on evolutionary computation is executed on the original emergency material scheduling task and the simplified auxiliary task, knowledge migration operation from the simplified auxiliary task to the original emergency material scheduling task is executed in the evolution process, and an optimized population of the original emergency material scheduling task is obtained; and according to a preset decision rule, determining a final emergency material scheduling scheme from the optimized population of the original emergency material scheduling task. A multi-task evolution framework is constructed, directed knowledge migration from an auxiliary task to an original task is executed, and the defects that single-task optimization is slow in convergence and direct multi-task parallel is prone to generating negative migration are overcome.

The application discloses a wireless communication signal remote interference discrimination method and device, a storage medium and a terminal, wherein the method comprises the following steps: acquiring parameter information of a to-be-detected signal, and performing normalization processing on the parameter information of the to-be-detected signal to obtain to-be-detected parameter information; inputting the to-be-detected parameter information into all interference discrimination neural networks in an interference discrimination neural network group respectively, so as to acquire interference labels output by each interference discrimination neural network; performing voting on all possible discrimination results based on the interference labels output by each interference discrimination neural network and the weights of the interference discrimination neural networks, and taking the possible discrimination result with the most votes as an interference discrimination result of the to-be-detected signal. The method of the application combines a neural network with evolutionary calculation, and realizes fast and efficient wireless communication signal remote interference discrimination with relatively high accuracy and reliability.

The invention provides a three-dimensional stratigraphic structure modeling method based on grey wolf optimization inverse distance weighted interpolation, and relates to the crossing field of geological data processing and evolutionary computation.The method comprises the steps that drilling data are analyzed, and a three-dimensional grid is constructed; constructing an IDW interpolation function, and carrying out preliminary efficiency verification on the standard parameters of the three-dimensional grid; solving a mean value by adopting a one-hole reserving normal form, and taking the mean value as a core measure of a grey wolf optimization algorithm; and obtaining refined lithologic mapping through interpolation parameter adaptive optimization driven by a grey wolf optimization algorithm, and outputting a final three-dimensional stratum model. According to the method, the three-dimensional grids are efficiently condensed from the discrete drill holes, classification loads are simplified through ordinal number mapping, KD tree injection speed-up retrieval is carried out, an optimization foundation stone is laid, and the calculation overhead is compressed.

The application discloses an abnormal sample detection and identification method and system based on evolutionary calculation and multi-modal consistency constraint, and belongs to the field of network security and artificial intelligence security. The method comprises the following steps: S1, input data collection and preprocessing; S2, multi-modal feature extraction and unified representation; S3, optimized feature selection and weight self-adaptation; S4, cross-modal fusion detection model construction; S5, abnormal sample risk identification; S6, result judgment output: generating a final detection label according to a threshold function. The application has high accuracy and strong robustness in counterfeit image and video detection and abnormal sample elimination, and can be widely applied to the fields of multimedia authentication, intelligent security and AI content tracing.

The invention relates to the technical field of electronic file anti-counterfeiting, and particularly discloses a non-inductive watermark electronic file anti-counterfeiting method based on evolutionary computation, which comprises the following steps of: S1, converting each page of document of an electronic file into a grayscale image, and generating an image sequence; s2, finding and recording optimal embedding positions, optimal rotation angles and position sequence information corresponding to a plurality of watermark images for each page of document by using an evolutionary computation method, and generating an optimal insertion position sequence of the non-inductive watermark; s3, the optimal insertion position sequence is packaged and encrypted to generate a secret key; s4, during anti-counterfeiting verification, information is inversely solved through the secret key, and whether the watermark exists or not and whether the position of the watermark is consistent or not are checked page by page by using configuration information in the secret key, so that the authenticity and integrity of the file are judged; the optimal fusion position of the watermark and the file content can be intelligently found, the imperceptible watermark is generated, verification is carried out through the encryption key, and the problems that the electronic file is easy to tamper and counterfeit, and verification is difficult are effectively solved.

A star flash encryption transmission method, apparatus and device, and a storage medium are applied to the technical field of data security, and the method applied to a first device comprises the steps of initializing a population of evolutionary computation; performing iterative optimization of a preset number of iterations on the population, and obtaining a first password set of a nonlinear iteration function value corresponding to each individual in the population of each iteration and an exponential first population fitness value corresponding to the population; according to a first password set corresponding to the first maximum population fitness value and a preset encryption function, encrypting the to-be-encrypted plaintext to obtain a ciphertext; sending the initialized population and the first maximum population fitness value to at least one second device for security verification; and if response information which is fed back by the target second equipment and indicates that the verification is passed is received, transmitting the ciphertext to the target second equipment. Data encryption is carried out by adopting evolutionary computation, so that the security and the quantum attack resistance of the encryption process are enhanced; and meanwhile, the adaptability of the encryption process is improved.

The invention discloses a self-evolutionary computing system based on dynamic logic and recursive tensor, which defines a dynamic logic system comprising an uncertainty state (U-State) and an abnormal evolutionary state (A-State), and a logic value set of the dynamic logic system is {-1, 0, + 1, U, A}. The system generates a high-dimensional solution space through iteration of a recursive tensor, when system-level contradiction is caused by exploration of a U state, an A state is triggered, then a Meta-System Receiving mechanism is started, a new rule is searched in the solution space and permanently integrated into the system, and dimension transition of system functions is achieved. According to the method, normal form transformation of a computing system from'preset instruction execution 'to'autonomous knowledge creation' is realized, and a fundamental solution is provided for solving'unknown problems' in the fields of general artificial intelligence, adaptive control systems and the like.

The invention relates to the technical field of artificial intelligence and evolutionary computing, and particularly discloses a memory annual ring learning-based evolutionary method, which comprises the following steps of: locking elite groups, extracting core declusters, coding task constraint fingerprints, backtracking efficient operators, drawing performance operator portraits, constructing memory annual ring units and storing the memory annual ring units in a memory annual ring library. When facing a new task, analyzing and generating a to-be-matched task fingerprint, matching a historical memory annual ring unit, extracting a core declustering and efficiency operator portrait, remodeling a population basis, generating a dynamic operator weight table, and executing dual-channel collaborative guide evolution; according to the method, through collaborative optimization of the solution space and the strategy space, the convergence speed and the solving performance of the algorithm in a new environment are remarkably improved, conversion from blind exploration to experience guidance is achieved, and the challenges of knowledge migration and experience reuse in dynamic change or staged tasks of a traditional evolutionary algorithm are solved.

The invention provides a sub-model weight optimization method for ensemble learning of a large language model, and relates to the technical field of data processing, and the method comprises the steps: generating an initial sub-model weight group, combining and coding the initial sub-model weight group into a binary gene character string, and taking the binary gene character string as an initial population of evolutionary computation; constructing a proxy experiment environment, generating a proxy corpus by randomly sampling the original pre-training corpus, and selecting at least three representative natural language processing tasks as proxy tasks for evaluating the adaptive value of the gene character string; based on the evaluation result of the proxy task, selecting a high-fitness gene character string by adopting a roulette method, and generating a next-generation gene character string through crossover and mutation operations; and S2 and S3 are iteratively executed until convergence, an optimal sub-model weight combination is obtained, and task-independent meta-learner fine tuning training is carried out on the original full-amount corpus to obtain a final integrated model.

This invention discloses a method, apparatus, and storage medium for automotive structure optimization. The method includes: acquiring historical data for automotive structure optimization; wherein the historical data includes structural parameters and corresponding performance simulation test results; mathematically modeling the automotive structure optimization problem based on the historical data to determine the optimization objective of the model; wherein the optimization objective is to minimize vehicle weight and maximize safety; and using a hierarchical particle swarm optimization algorithm assisted by a classification model to optimize the mathematical model and obtain structural design parameters that minimize vehicle weight and maximize safety. This invention utilizes classification model prediction to replace most of the automotive structure performance simulation during the evolution process, and uses the classification results to drive hierarchical particle swarm evolution, solving the problems of poor automotive structure optimization results and low search efficiency caused by large search space and long evaluation time in existing technologies. This invention can be widely applied in two major fields: evolutionary computation and industrial automotive structure design.

The invention relates to the technical field of resonance sound absorption regulation and control, in particular to an air layer thickness and resonance frequency correlation control method and system in a sound insulation structure. According to the method and system, a corresponding thickness range can be reversely deduced from sound pressure and structure response deviation through a Gauss-Newton inversion algorithm on the basis of the residual error minimization principle; precise positioning of a resonance interval and continuous reverse solving of thickness parameters are achieved, screening and evolutionary calculation are carried out on a multi-parameter objective function by taking a thickness correction sequence as a population through a multi-objective genetic algorithm, adaptive thickness compensation amount is generated through population iteration, and continuous integral processing is carried out on thickness difference, so that the thickness difference is obtained. The quantitative balance between the macroscopic thickness and the disturbance compensation amount is established, so that the thickness adjustment has the traceability and convergence in the time sequence, the judgment of the response slope and the full-band scanning mechanism form a feedback closed loop, and the formant position keeps stable distribution in the dynamic adjustment.

This invention relates to the field of neural network architecture search and industrial intelligent processing technology, and discloses a structure search method for multi-output dendritic neuron models for industrial classification tasks. The method includes: initializing the synaptic connection weights and dendritic threshold parameters of the multi-input multi-output dendritic neuron model to generate an initial population; uniformly dividing the initial population into several subpopulations of equal size; calculating the temporal and spatial criteria for each subpopulation after task allocation and optimization, and dynamically allocating evolutionary computational resources for each subpopulation based on the fitness change rate represented by the temporal criterion and the population distribution state represented by the spatial criterion; implementing an accelerated sharing penalty mechanism to adjust fitness values ​​according to the crowding degree among individuals to maintain solution set diversity; and updating each subpopulation. This invention solves the problem of low convergence efficiency caused by uniform resource allocation in traditional large-scale multi-objective evolutionary algorithms during neural network architecture search.

The invention discloses a self-evolution computing system and a self-evolution computing method, which realize millisecond-level online optimization and minute-level continuous evolution through a sensing-diagnosis-decision-execution-control five-layer closed-loop architecture (as shown in attached drawings of the abstract). The system has causal diagnosis, strategy self-learning and microcode level reconfiguration capabilities, the actually measured energy consumption is reduced by more than or equal to 18%, the delay is reduced by more than or equal to 25%, the decision process is completely explained, the maintenance cost of the system is remarkably reduced, and the comprehensive performance is improved.

The invention discloses a flexible manufacturing structured process multi-objective optimization method based on evolutionary computation, and belongs to the technical field of intelligent manufacturing and production scheduling. The method comprises the following steps: modeling a production problem, and taking the workload balance degree of each vehicle type as an independent objective function; performing parallel optimization on process allocation by adopting a multi-objective evolutionary algorithm to generate a Pareto optimal solution set; and visualizing the Pareto frontier through an interactive decision support module, and carrying out multi-criterion decision recommendation based on the user preference weight. According to the method, the defects that a traditional single-target optimization method cannot reflect a multi-target tradeoff relation and depends on prior weight setting are overcome, a series of high-quality scheduling schemes can be provided for multi-variety mixed-line production, and the flexibility, transparency and overall efficiency of a production system are remarkably improved.

The invention provides a shelter equipment operation control method and device based on data acquisition, and relates to the field of intelligent control. The method comprises the following steps: acquiring shelter environment data and mushroom growth data; performing time sequence alignment and fusion processing on the square cabin environment data and the mushroom growth data, and extracting square cabin microorganism behavior characteristics by using a depth time sequence model to obtain behavior characteristic vectors; transmitting the behavior feature vector to a reinforcement learning control model, outputting a control instruction, and recording a growth result after the instruction is operated; the optimized shelter parameters and operation results are uploaded to a cloud end, evolutionary computation is carried out on data from different shelters through a group learning algorithm, and an optimal parameter template is formed; and the optimal parameter template is returned to the local part of each cabin for fine adjustment, and adaptive optimization of the parameters is controlled. The method is used in a shelter equipment operation control process based on data acquisition, and solves the technical problem that an existing mushroom shelter is lack of dynamic regulation and control of operation parameters.

The invention discloses an unsupervised three-dimensional acoustic logging reflector imaging interpretation method based on evolutionary computation. The unsupervised three-dimensional acoustic logging reflector imaging interpretation method sequentially comprises the steps that underground acoustic wave full wave train data collected by a multi-pole sub-array acoustic logging instrument is obtained; wave field separation and reflected wave extraction; multidirectional migration imaging is carried out; adaptive image enhancement driven by evolutionary computation; constructing a three-dimensional point cloud; adaptive spatial clustering driven by evolutionary computation and geometric fitting and interpretation of a three-dimensional structure are carried out; according to the scheme, the processing parameters are adaptively optimized through an evolutionary computation algorithm, so that unsupervised three-dimensional imaging of the geological reflector around the well, adaptive optimization of the automatic segmentation and processing parameters and true three-dimensional quantitative interpretation of the geological structure are realized; the technical problems that parameter adjustment depends on artificial experience and cannot adapt to a dynamic noise environment, geometric distortion exists in two-dimensional interpretation, and supervised learning is difficult to use due to lack of labeled data in an existing sound wave remote detection technology are solved.

The invention belongs to the technical field of dynamic multi-objective optimization and evolutionary computing, and relates to a dynamic multi-objective optimization method based on an improved Wasserstein distance and adaptive transfer learning, and the method comprises the steps: S1, outputting a unified scheduling signal; s2, performing affine migration of covariance shrinkage in a decision space, performing manifold projection / boundary reflection, and quickly pulling a population back to a high-quality feasible region; s3, carrying out nondimensionalization on various constraints into a unified residual error, so that a sudden change period is relaxed, and a stationary period is tightened; s4, suppressing oscillation through smoothing and the upper limit of the change rate; s5, the coverage degree and uniformity of the Pareto front are maintained; the method has the advantages that the environment change intensity can be measured in a dimensionless and steady manner, and the migration step length, the constraint weight and the evolution parameter scheduling are uniformly driven according to the environment change intensity; trend-guided reliable migration and self-adaptive disturbance, fast feasible region return to dynamic constraints, self-adaptive tradeoff between exploration and development and balance maintenance in sparse / dense directions are realized.

The application discloses a kind of attention mechanism LSTM fan blade fault diagnosis method, device, electronic equipment and medium based on differential evolution optimization.The method can include: collecting SCADA data, determining feature dataset;LSTM neural network is trained by feature dataset, and output state is obtained;Output state is input to attention mechanism model, and output result is obtained;Output result is obtained by output module to obtain prediction result, and prediction result is compared with fan actual state, and output error is calculated;According to output error, differential evolution calculation is carried out, and the parameters of LSTM neural network and attention mechanism network model are updated;Fan data to be tested is input into the model after training, and the diagnosis result of fan blade fault is obtained.The application has higher comprehensive performance and generalization ability, and has the advantages of improving the accuracy and speed of fan blade fault diagnosis.

A non-transitory computer-readable recording medium that stores a program causing a computer to execute a process is provided. The process includes when repeatedly searching for a solution using evolutionary computation based on an evaluation function that evaluates multiple objective functions, controlling a search direction for the solution according to a distribution of Pareto solutions obtained, and searching for a next generation of solutions.

The present application relates to the medical health information technology field, and discloses a personalized anti-inflammatory diet recipe recommendation system and method based on artificial intelligence; the present application constructs a user health portrait through collecting multi-source data such as wearable devices, electronic health records, gene sequencing, combines a dynamically updated anti-inflammatory knowledge graph, evaluates the user's quantitative inflammation load index and nutrient intervention target by using multi-modal deep learning based on the attention mechanism, adopts a hybrid intelligent optimization algorithm combining constraint satisfaction and evolutionary computing to generate the Pareto optimal anti-inflammatory recipe under the constraints of taste taboo, cost, cooking time and the like, and optimizes the model online through user physiological and subjective feedback; personalized, precise and dynamically adaptive anti-inflammatory diet recommendation is realized, and the scientificity and compliance are improved from group guidelines to individual intervention.

The application discloses an evolutionary computing-based dynamic path multi-AGV charging pile site selection optimization method and system. The method comprises the following steps: determining a plurality of workstation positions as candidate charging pile arrangement points, and representing whether each position is arranged with a charging pile as a binary decision variable; based on a multi-objective optimization model, taking the minimization of the number of charging piles, the maximization of charging pile coverage and utilization as the target, under the constraint conditions of meeting at least one charging pile arrangement, energy accessibility and full coverage, an evolutionary algorithm is used to iteratively optimize the initial population, and a charging pile layout scheme suitable for multiple sets of AGV operation routes is generated; through multi-scenario energy constraint verification and a self-adaptive repair mechanism, it is ensured that the layout scheme still meets the AGV charging demand under the route change; finally, the scheme quality is further improved through local optimization and simulated annealing strategy. The application can realize the comprehensive optimization of the economy, coverage balance and system robustness of the charging pile layout.

The invention provides an air route planning method based on multi-dimensional information representation and evolution calculation, and belongs to the technical field of unmanned aerial vehicle air route planning. Firstly, task demand and scene analysis is carried out, multi-dimensional information is processed, threat prediction is carried out, elevation information is complemented through neighborhood interpolation based on topographic data, and a three-dimensional image is obtained; smoothing the terrain to construct a minimum safe flight curved surface, determining a detection blind area based on a terrain shielding effect, delimiting an initial task feasible area, and dynamically predicting a threat target position to generate a dynamic threat situation map; the method comprises the following steps: constructing a multi-target air route optimization model, performing model solution based on an improved ant colony algorithm, designing and differentially maintaining composite environment pheromones, generating an initial feasible air route through a feasible air route increment construction method considering space-time constraints, and dynamically updating the pheromones to complete final air route planning; according to the method, the problems of poor air route collaboration, low concealment, insufficient feasibility and the like caused by space-time and performance constraints in a threat environment can be solved.

The invention provides an automatic operator selection method based on deep reinforcement learning in evolutionary computation, which comprises the following steps of: firstly, constructing an operator pool, and obtaining initial candidate solution population data of a bottom optimizer; the feature information of an optimization problem is extracted through population landscape analysis, a deep reinforcement learning strategy is adopted based on the features, the most appropriate operator is dynamically selected in the optimization process, a bottom optimizer is guided to efficiently search an optimal solution, a reward mechanism is introduced, the operator selection process and the bottom optimizer are co-evolved, and the optimization efficiency is improved. Compared with a traditional static operator selection method, the method has the advantages that the operator selection strategy can be flexibly adjusted in the optimization process, the adaptability and generalization ability of operators are improved, and better optimization performance is obtained in the aspect of complex and high-dimensional optimization problems. The method is suitable for multiple fields of automatic machine learning, engineering optimization, bioinformatics and the like, and has a wide application prospect.