21 results about "Meta heuristic" patented technology

The invention provides a performance optimization method and system for aggregate communication operation, electronic equipment and a medium, and the method comprises the steps: executing a closed-loop optimization process taking meta-heuristic search as a core through determining a multi-dimensional parameter space for describing a hierarchical protocol strategy of the aggregate communication operation, a complex mapping relation between different strategy configurations in a parameter space and the actual performance of the system is fully explored; the target code of the candidate strategy is automatically generated and actually measured based on the historical performance data, the actually measured performance feedback is obtained through continuous iteration, and the target hierarchical protocol strategy of the set communication operation is obtained. According to the method, the centralization bottleneck problem in a direct protocol mode is effectively solved, and the communication efficiency and the resource utilization efficiency in a large-scale parallel system are remarkably improved. Moreover, the closed-loop optimization process gets rid of dependence on artificial experience, reduces the complexity and cost of manual adjustment and optimization in a multi-factor coupling environment, and improves the adaptability, performance and transportability of integrated communication operation in different platforms and tasks.

The invention provides a full-focus imaging method based on a metasurface improved stacking plate buffer rod, and relates to the technical field of nondestructive detection.The full-focus imaging method combines a semi-empirical parameterization method with meta-heuristic search, parameterization design is conducted on a side wall structure through a sine function family, time domain finite element simulation and experimental verification are matched, and the full-focus imaging effect is achieved. Directional suppression and engineering manufacturable design of the trailing wave of the stacked plate are realized; according to the method, the limitation of trailing wave artifacts in traditional buffer rod full-focus imaging is overcome, the advantages of the acoustic metasurface technology and the meta-heuristic algorithm are combined, the physical interpretability of design can be kept, the metasurface structure parameter set which is optimal under the limitation of the actual working environment can be automatically searched out, the trailing waves are remarkably weakened, and the system performance is improved. Artifacts are almost completely eliminated, the signal-to-noise ratio and positioning reliability of imaging are remarkably improved, and a more feasible design scheme is provided for popularization and application of the stacking plate buffer rod in phased array nondestructive testing.

A processing unit carries out a process for performing a first solution search for an integer programming problem including a plurality of state variables using a metaheuristic method, and a second solution search using a branch and bound method for the integer programming problem relaxed linearly. In the process, the processing unit identifies, based on information on first solutions calculated for each of a plurality of subproblems obtained through branching operations in the second solution search, some of the plurality of state variables whose values are fixed in any of the plurality of subproblems; performs the first solution search while keeping fixed the values of the identified some of the plurality of state variables; and performs branch cutting in the second solution search using a first evaluation function value of a second solution obtained by the first solution search.

The application provides a full-focusing imaging method based on a super surface improved stack plate buffer rod, relates to the technical field of nondestructive testing, and combines a semi-empirical parameterization method with a meta-heuristic search, parameterizes a side wall structure through a sine function family, and cooperates with time domain finite element simulation and experimental verification to realize directional suppression and engineering manufacturability design of a stack plate trailing wave. The application overcomes the limitation of the trailing wave artifact in the traditional buffer rod full-focusing imaging, combines the advantages of the acoustic super surface technology and the meta-heuristic algorithm, can maintain the physical interpretability of the design, and can automatically search for the super surface structure parameter set that performs best under the actual working environment limitation, so that the trailing wave is significantly weakened, the artifact is almost completely eliminated, the signal-to-noise ratio and the positioning reliability of the imaging are significantly improved, and a more feasible design scheme is provided for the popularization and application of the stack plate buffer rod in the phased array nondestructive testing.

The invention provides a meta-heuristic wind turbine generator control parameter adaptive optimization method, and belongs to the technical field of wind turbine generator control. Comprising the following steps: step 1, establishing a mathematical model of a fan system; step 2, constructing an effective fitness function as a setting parameter optimization target; step 3, calling an LPO algorithm to optimize parameters; step 4, data processing; and 5, carrying out weighted fusion on multiple targets, and carrying out correlation and sensitivity analysis to determine weight parameters. According to the method, the meta-heuristic intelligent algorithm (LPO) is introduced to carry out multi-parameter self-adaptive setting, so that automatic optimization of fan control parameters is realized, and the efficiency and accuracy of parameter design are remarkably improved; and correlation and sensitivity analysis is carried out on multiple targets, and the stability and high efficiency of the fan under complex working conditions are ensured through weighted fusion optimization design indexes.

The application discloses an improved efficient dispatching optimization method for carrier aircraft sortie, and belongs to the field of carrier aircraft dispatching optimization, and comprises the following steps: constructing a carrier aircraft sortie dispatching model, wherein the dispatching model is a workshop dispatching model with multiple constraints; and solving the dispatching model by using an improved meta-heuristic recursive neural algorithm, calculating a new solution by using a job allocation matrix to represent the solution and updating the solution by using a disturbance updating strategy, and iteratively optimizing the solution to obtain an optimal dispatching scheme. The application solves the problems of low solving efficiency, early convergence and difficulty in guaranteeing that all constraints are met when the existing method faces the high dimension and strong coupling constraints of carrier aircraft dispatching.

The application discloses a kind of meta-heuristic test design optimization methods based on matrix transformation, belong to computer-aided test design and statistical modeling technical field, this method uses a two-stage optimization framework: first, in global exploration stage, with minimizing global uniformity index as target to carry out unconstrained optimization, to obtain well space coverage sample set;Subsequently in local development stage, with minimizing adjacent distance variance as target, optimization is carried out under the constraint of maintaining the good global coverage obtained, to fine adjustment sample point local spacing distribution, improve uniformity;The application encodes test design scheme into matrix form individual, is updated iteratively by meta-heuristic algorithm, and adopts global normalization strategy to ensure that sample is always located in design space;The application can systematically consider the global coverage of sample and local uniformity, significantly improve the sample distribution quality in high-dimensional space, generate comprehensive performance excellent test design scheme.

The invention discloses an underwater range gating dynamic imaging method based on meta-heuristic attitude estimation, and relates to the technical field of underwater imaging, and the method comprises the steps: building an underwater range gating laser imaging system, and obtaining a continuous space-time matched original scanning image sequence through synchronous control of laser emission, scanning and gating exposure; selecting a reference frame from an original scanning image sequence, determining an effective imaging area, modeling inter-frame rigid body motion by adopting a six-degree-of-freedom pose parameter, and constructing a global optimization objective function based on a gray mean square error; and designing a self-adaptive weighted pixel loss function, distributing weights for pixels through normalized gray and gradient information, and inhibiting the influence of a highlight area. According to the method, the meta-heuristic optimization thought is applied to the 6D pose estimation problem of underwater range gating laser imaging, a 6D pose optimization framework suitable for a high-sparseness and high-constraint scene is constructed based on PSO, and the fundamental challenges of scarcity of feature points and insufficient data sets are overcome.

An information processing apparatus for solving one or more online optimization problems, including at least one processor and a memory which is configured to store instructions, the at least one processor executing: an obtaining process of obtaining a problem setting; a generating process of generating a metaheuristic solver with reference to the problem setting; a solving process of solving a problem including one or more tasks using the metaheuristic solver so as to provide a solution to the problem including the one or more tasks; and an updating process of updating the metaheuristic solver with reference to a new task so as to provide a solution to the problem including the new task.

This invention discloses an underwater range-gated dynamic imaging method based on metaheuristic attitude estimation, belonging to the field of underwater imaging technology. The method includes: constructing an underwater range-gated laser imaging system; acquiring a continuous, spatiotemporally matched sequence of original scanned images by synchronously controlling laser emission, scanning, and gated exposure; selecting reference frames from the original scanned image sequence and determining the effective imaging region; modeling inter-frame rigid body motion using six-degree-of-freedom pose parameters; constructing a global optimization objective function based on gray-level mean square error; and designing an adaptive weighted pixel loss function, assigning weights to pixels by normalizing gray-level and gradient information to suppress the influence of bright areas. This invention applies metaheuristic optimization ideas to the 6D pose estimation problem of underwater range-gated laser imaging, constructing a 6D pose optimization framework suitable for highly sparse and highly constrained scenarios based on PSO, overcoming the fundamental challenges of feature point scarcity and insufficient datasets.

The invention discloses an improved meta-heuristic optimization algorithm-based parallel instrument dexterous performance enhancement method, which comprises the following steps of: establishing a basic optimization model of a parallel instrument, including a single-target or multi-target optimization model; splitting a complete task into a plurality of sub-tasks according to the characteristics of the task executed by the parallel machine, establishing a mapping relationship between the workspace sub-region and the optimization target according to the association between the sub-tasks and the workspace sub-region and the association between the sub-tasks and the optimization target, and establishing an improved optimization model of the basic optimization model based on the mapping relationship; performing target optimization solution on the improved optimization model by using a meta-heuristic intelligent optimization algorithm improved by chaotic mapping and variation, and screening candidate solutions based on a dexterous performance sensitivity index to obtain an optimal solution; and constructing a motion performance comprehensive evaluation index of the parallel instrument to evaluate the dexterous performance enhancement design effect of the parallel instrument applying the optimal solution. The design performance of the parallel instrument can be effectively improved, and the aim of improving the dexterous performance of the parallel instrument is achieved.

The invention discloses a floating type wind power plant regular layout and mooring system collaborative design method. The method comprises the steps of obtaining wind power plant basic data; further constructing a grid-based mixed integer linear programming model, approximately evaluating the mooring cost by introducing a water depth proxy function and applying a rule layout constraint, and quickly solving to obtain an initial discrete position of the fan; a continuous coordinate optimization model is constructed on the basis, a meta-heuristic search algorithm is adopted to carry out collaborative fine tuning on continuous coordinates of the wind turbine and parameters of the mooring line, and the length of the mooring line is accurately calculated on the basis of submarine topography. Therefore, the technical problems that the mooring system design and the fan layout are disjointed, the optimization calculation complexity is high and the engineering regularization layout requirement is difficult to meet due to the complex submarine topography in the deep and far sea area are solved. According to the method, the generating capacity is evaluated through the wake flow model, a collaborative optimization scheme considering regular layout, wake flow loss and mooring economy is finally output, and maximization of the net present value of the whole life cycle of the wind power plant is achieved.

The invention relates to a wireless sensor network clustering routing method and system based on hybrid intelligent optimization. The method comprises the following steps: constructing a clustering routing model; on the basis of a clustering routing model, considering the unbalance of node energy consumption, and constructing a network energy consumption model; constructing a joint optimization problem of cluster head selection and a routing path by taking maximization of network comprehensive efficiency as a target based on a network energy consumption model; converting a joint optimization problem into a Markov decision process problem according to the dynamic variability of the network state; a hybrid intelligent optimization framework fusing meta-heuristic and deep reinforcement learning is adopted to solve a Markov decision process problem, and an optimal solution of the problem is obtained, namely, an optimal clustering scheme and a data transmission path are obtained. According to the method, a hybrid algorithm framework fusing grey wolf optimization and the deep Q network is provided, after the joint optimization problem is solved, the life cycle of the wireless sensor network can be remarkably prolonged, the convergence speed is high, and the resource efficiency is high.

The invention discloses a ship organization method based on a compound channel. The scheduling efficiency and rationality are improved through a systematized process. The method comprises the following steps: firstly, classifying ships, and assigning corresponding channels according to different types of ships so as to guarantee the basic order of navigation; then, conflict points in ship navigation are identified, the priority of each ship is set, and the potential conflict problem is solved; then constructing an optimization model, and comprehensively considering various factors to realize the overall optimization of the scheduling scheme; and finally, quickly and effectively obtaining and outputting an optimal scheduling scheme by adopting a meta-heuristic solving method. The method can effectively reduce the ship waiting time, improves the channel utilization rate, reduces the conflict occurrence probability, provides a scientific and efficient solution for ship scheduling, is suitable for ship scheduling management of various ports and channels, and has wide application prospects and practical values.

This invention relates to the field of precipitation fusion technology and discloses a multi-source precipitation data fusion method based on metaheuristics and machine learning, comprising the following steps: collecting ground precipitation observation data, multi-source satellite precipitation product data, and auxiliary variable data, and performing data preprocessing; dividing the preprocessed ground precipitation observation data, multi-source satellite precipitation product data, and auxiliary variable data into training and validation sets according to a certain ratio; setting the initial value range of hyperparameters of a multilayer perceptron model, and inputting them along with the training set into the multilayer perceptron model for training; optimizing the hyperparameters of the multilayer perceptron model using a sparrow search method to obtain a trained multilayer perceptron model; inputting the validation set into the trained multilayer perceptron model to identify the precipitation observation values ​​of the validation set, thereby obtaining the predicted ground precipitation observation data, i.e., the fused precipitation data; this method improves the accuracy of fused precipitation data while avoiding reliance on assumptions that are not valid in reality.

The application discloses a kind of floating wind farm regular layout and mooring system collaborative design method, comprising: obtaining wind farm basic data;Further construct the mixed integer linear programming model based on grid, by introducing depth proxy function approximation evaluation mooring cost and impose regular layout constraint, quickly solve to obtain the initial discrete position of wind turbine;Based on this, construct continuous coordinate optimization model, adopt meta-heuristic search algorithm to the continuous coordinate of wind turbine and mooring line parameter are cooperated and fine-tuned, and mooring line length is accurately calculated based on seabed topography.Therefore, solve the technical problems that mooring system design and wind turbine layout are disconnected in deep sea area due to complex seabed topography, optimization calculation complexity is high and it is difficult to meet the requirements of engineering regular layout.The method evaluates power generation by wake model, finally outputs the collaborative optimization scheme considering regular layout, wake loss and mooring economy, realizes the maximization of wind farm life cycle net present value.

The present application relates to a kind of rock burst intensity prediction method based on weighted meta-heuristic combination model, belong to geotechnical engineering technical field.The present application determines the evaluation index of rock burst intensity prediction and rock burst intensity classification;Rock burst case is collected, rock burst intensity prediction sample data set is established, and is divided into training set and test set sample;Three meta-heuristic optimization algorithms are used to optimize hyperparameters in random forest (RF) model, and corresponding rock burst intensity prediction model is established;Training set and test set sample are respectively input into three rock burst intensity prediction models to obtain the predicted rock burst intensity classification, and the prediction accuracy of three rock burst intensity prediction models is calculated respectively;According to the prediction accuracy, the weight vector of corresponding rock burst intensity prediction model is calculated;According to the weight vector of rock burst intensity prediction model, the value of hyperparameter is determined, and the meta-heuristic combination model based on weighting is established;The rock burst intensity to be predicted in engineering case is predicted using the meta-heuristic combination model based on weighting.

An adaptive system is implemented for use with one or more numerical relays. A plurality of features associated with relay operational profile data is input into a plurality of candidate models, and each candidate model is evaluated for one or more performance metrics. A predictive model is selected for fault detection based on the sensitivity and specificity of the predictive model, and a metaheuristic process is used to evaluate various combinations of relay settings for the numerical relays in an electrical network and determine a first set of relay settings based on the evaluation. The electrical network is monitored to detect one or more relay state transitions indicative of one or more changes in relay performance or network conditions, and if one or more relay state transitions are detected, the first set of relay settings are adjusted to enhance performance of the electrical network.

This invention discloses a method for designing nested layout schemes in 3D space based on hybrid meta-heuristic evolution. The method includes the following steps: First, constructing an enclosing structure that completely encompasses the 3D space container, setting a safe distance for interference prevention of 3D components, and generating an initial 3D nesting conceptual design layout; next, constructing a set of meta-heuristic operator strategies, generating a group of meta-heuristic operators using the Markov chain Monte Carlo algorithm, and mixing four types of meta-heuristic operators; during the evolution process, solving for a better group of meta-heuristic operators, and using it to update the current 3D prototype layout scheme to obtain a 3D layout based on hybrid meta-heuristic evolution. The method disclosed in this invention can solve the layout problem in irregular 3D space, and also supports nested layouts of 3D components, making full use of the available space inside the components, overcoming the difficulty of existing methods in handling the arrangement of island-shaped containers or components, and significantly improving the utilization rate and layout efficiency of 3D space.

The invention discloses a real-time delivery task allocation method based on a two-stage meta-heuristic algorithm, and relates to the field of real-time delivery, and the method comprises the steps: constructing an order and rider mixed utility matrix, and generating a candidate edge set; performing utility parameter calibration based on the hybrid utility matrix, and determining a reference parameter; an MCMF model is constructed through a dynamic TopK pruning and time-phased soft capacity constraint method, and an MCMF initial solution is generated; and based on the MCMF initial solution, a final distribution task distribution result is obtained through a two-stage multi-target task distribution optimization method of NSGA-II and ALNS. According to the method, comprehensiveness, flexibility, global search capability and local optimization capability are shown, so that load unevenness between different riders is effectively reduced while the overall distribution effectiveness is improved by an order dispatching algorithm, the platform transport capacity utilization rate and the rider satisfaction degree are improved, and the service life of the riders is prolonged. The method is suitable for solving the task allocation problem of large supply and demand fluctuation in multiple peak scenes all day long.

This invention relates to the field of artificial intelligence technology, specifically to a method and system for groundwater level prediction using a large language model-assisted hyperheuristic optimization. The method includes: acquiring a groundwater level dataset, a metaheuristic algorithm library, a neural network model library, and a trained large language model; constructing an initial population, where each individual contains both a metaheuristic algorithm component and a neural network component; iteratively evolving the initial population, performing mutation, crossover, and selection operations in each iteration, where mutation operations include metaheuristic mutation, neural network mutation, and large language model evolution mutation; and applying the optimized algorithm and model configuration contained in the optimal individual obtained after iteration termination to the groundwater level dataset to generate prediction results. This invention achieves the co-evolution of the metaheuristic algorithm and the neural network model, improving the accuracy and robustness of groundwater level prediction.