42 results about "Meta heuristic" patented technology

The invention provides an intelligent inspection path planning method and system based on reinforcement learning, and relates to the technical field of path planning, comprising the steps of constructing a scene understanding model and a dynamic interaction model, and forming a cognitive enhanced digital twin system. Metaheuristic search and social learning mechanisms are fused, and the inspection efficiency, energy consumption and risk are balanced through a multi-target conflict coordinator. The group behavior emergence prediction model achieves dynamic prediction of a collaborative mode, the federated learning framework achieves aggregation of experience knowledge, a group evolution strategy with high adaptability is generated, and closed-loop optimization from task execution to cognitive enhancement is achieved through a robustness evaluation mechanism fed back in real time and a task allocation optimizer based on an attention mechanism. And the inspection efficiency and adaptability are improved.

The invention provides an electricity price-based permanent magnet motor energy storage and optimization operation method and system, and relates to the technical field of motor energy storage and optimization operation, and the method comprises the steps: predicting the power of a motor through a deep belief neural network, predicting the electricity price through a space-time diagram convolutional neural network, and quantifying a system error in combination with a Gaussian mixture model, based on a prediction result, performing global optimization by adopting a quantum genetic optimization algorithm to obtain an initial optimization strategy set, applying the initial optimization strategy set to a meta-heuristic hybrid optimization model, generating a time sequence association control strategy, performing system modeling through a hybrid Gaussian-hidden Markov model, and constructing a double-layer action evaluation control network to generate an operation control instruction; a distributed reinforcement learning framework is used for cooperative control, and an optimal control strategy is obtained based on adaptive dynamic programming and a meta-learning strategy migration mechanism.

The invention discloses a factory network river-lake integrated scheduling method and system based on a butterfly optimization algorithm, and solves the problems that resource scheduling is not systematic and the optimization effect is poor in urban water environment management. The method comprises the following steps: firstly, collecting and sorting data information such as a rain sewage pipe network, pump gate characteristics and a water system in a research area, constructing one-dimensional and two-dimensional hydrodynamic models, and simulating dynamic behaviors of a drainage system; on the basis, a meta-heuristic butterfly optimization algorithm is adopted to construct an optimization scheduling model, and an optimization scheme conforming to a scheduling target is generated through the steps of population initialization, fitness calculation, iterative search of an optimal solution and the like. In the optimization process, scheduling parameters are adjusted in combination with real-time monitoring data, and the effectiveness and practicability of the scheme are ensured.

The invention belongs to the technical field of engineering design, and discloses a new meta-heuristic method inspired by a piranha fierce hunting strategy, and the algorithm comprises the steps: initializing a numerical value, initializing the positions Xi of individuals in a piranha school, the number N of the individuals, and the maximum number tmax of iterations; s3, searching: performing multi-dimensional perception on all piranha according to a formula (1), a formula (3), a formula (4) and a formula (6), then performing greedy decision search movement according to a formula (8), and turning to S3; s4, interaction: each piranha randomly selects other piranha according to a formula (9) for interaction, the piranha preferentially moves, and the step S4 is executed; hunting and killing: hunting and killing each piranha to the position of the global optimal prey according to a formula (10), and updating the position of the individual and the current global optimal position Xbest; s2, judging whether to terminate or not, judging whether an optimization precision requirement or the maximum number of iterations tmax is met or not, if yes, outputting the optimal value of the target function, and if not, turning to S2. According to the invention, accurate and efficient target locking is realized. A closely cooperative optimization system is constructed by simulating a plurality of natural communication mechanisms such as sound signals, light guide, pheromone transmission, vibration response and the like.

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 discloses a partial discharge feature selection method based on a meta-heuristic swarm intelligence optimization algorithm, and the method comprises the steps: extracting a time domain feature, a frequency domain feature and a time-frequency domain feature based on a partial discharge signal sample, and constructing a feature vector and an original feature set; randomly dividing the original feature set into a training set and a verification set; selecting a classifier for partial discharge type classification and identification, training the selected classifier by using the training set to obtain a classifier model, and setting a fitness function as the classification accuracy of the currently trained classifier model on the verification set; a feature subset with the optimal classification performance is obtained by adopting a meta-heuristic swarm intelligence optimization algorithm and through multiple rounds of iterative operation; and re-training the classifier by using the selected optimal feature subset to obtain a classification model, and evaluating the classification model to ensure good performance of the model on test data. According to the method, irrelevant or interference features are effectively removed, and the accuracy and reliability of partial discharge type classification and identification are improved.

The present invention provides a permanent magnet motor energy storage and optimized operation method and system based on electricity price, which relates to the technical field of motor energy storage and optimized operation, including: predicting motor power through a deep belief neural network, predicting electricity price by using a spatiotemporal graph convolutional neural network, quantifying system errors in combination with a Gaussian mixture model, and based on the prediction results, using a quantum genetic optimization algorithm to perform global optimization, obtain an initial optimization strategy set and use it in a meta-heuristic hybrid optimization model to generate a timing association control strategy, perform system modeling through a mixed Gaussian-hidden Markov model, construct a two-layer action evaluation control network to generate operation control instructions, use a distributed reinforcement learning framework for collaborative control, and obtain an optimal control strategy based on an adaptive dynamic programming and meta-learning strategy migration mechanism.

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.

The present invention belongs to the field of communication technologies, and particularly relates to a three-dimensional deployment method of an unmanned aerial vehicle (UAV) base station in post-disaster communication restoration. Based on minimizing the required number of UAVs and maximizing the overall quality of service measured by the received power of all users, a problem of obtaining the optimal decision for UAV deployment is constructed. During the process of solving the problem of obtaining the optimal decision for UAV deployment, a greedy search algorithm is used to find a sub-optimal solution for the minimum number and positions of UAVs, and then an artificial electric field algorithm is used to optimize the problems of position and load balancing, so as to obtain an optimal UAV deployment scheme. The present invention combines a greedy search algorithm and a meta-heuristic artificial electric field algorithm, takes into account minimizing the number of UAV deployments and meeting QoS requirements, can be continuously deployed in three-dimensional space, can quickly adapt to autonomous positioning in a dynamic network, and has a low complexity.

The invention belongs to the field of parameter optimization algorithms, discloses a meta-heuristic parameter optimization algorithm based on variation chain search, and particularly relates to a meta-heuristic evolutionary parameter optimization algorithm for variation chain search based on individual characteristics of sample points and overall generality of a sample point set. The method comprises the following specific processing steps: (1) hydrological model parameter determination and hierarchical division; (2) collecting an initial sample group; (3) extracting the optimal evolution direction (acceleration) of the individual; (4) optimizing subgroup division; (5) generating an optimization speed vector; (6) determining a search direction; and (7) convergence conditions. According to the method, on the basis of the similarity and anisotropy between the sample points in the hydrological model parameter optimization process, the sample points are updated and iterated through similarity analysis and a spatial clustering method, and a new thought is provided for the optimization method of the hydrological model.

The application relates to a kind of semiconductor packaging workshop bonding process fast response scheduling method and system based on super heuristic algorithm, method includes establishing semiconductor packaging bonding process scheduling optimization model;Through the order splitting of large batch order by batch strategy;Through super heuristic algorithm adaptive generation scheduling scheme, wherein reinforcement learning algorithm is used as senior selection strategy, heuristic operator is used as the method of bottom problem domain;Output optimal scheduling scheme, arrange production.The scheduling method and system of the application, according to the characteristics of semiconductor packaging bonding process scheduling problem, design simple heuristic method, to build LLH method set, so as to realize the optimization solution of semiconductor packaging bonding process scheduling problem, both retains the good global optimization performance of meta heuristic algorithm, avoids the uncertainty caused by the adjustment of algorithm parameters in meta heuristic algorithm by artificial experience, can effectively improve the efficiency of algorithm design and bonding process scheduling efficiency.

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 present invention relates to a computational logic process that uses a meta-heuristic method divided into two blocks of processes based on PSO and ACO, interconnected in order to analyse and define more precise combined results between a database of professionals, available jobs, professional preparation pathways, and gender inclusion.

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.

This application provides a metaheuristic calculation-based control law optimization method for a combined ramjet engine, belonging to the field of aerospace engine control technology. The method includes establishing a combined ramjet engine model and determining optimization variables. For each combined ramjet engine control mode, constraints and an objective function based on the optimization variables are determined. The control modes include a steady-state control mode and an acceleration control mode. The steady-state control modes include a minimum fuel consumption control mode, a maximum thrust control mode, and a minimum turbine inlet temperature control mode. Based on the objective function and constraints, a metaheuristic calculation strategy is used to optimize the control law for each control mode to obtain the optimal control variables. The metaheuristic calculation strategy is set to be a reptile search algorithm fused with a multidimensional learning hunting strategy. This application's processing solution improves the convergence speed of combined ramjet engine control law optimization, improving the optimization effect and engine performance.

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 hybrid cloud deployment cluster Spark job scheduling method and device, equipment and a storage medium. The method comprises the following steps: acquiring current operation information, virtual machine information and stage information in real time; on the basis of the operation information, the virtual machine information and the stage information, adopting a particle swarm optimization algorithm, and taking minimization of the virtual machine use cost of the whole cluster as an optimization target to generate current scheduling information; and scheduling an executor for execution according to the scheduling information. According to the technical scheme provided by the invention, the job information, the virtual machine information and the stage information are combined, and the optimal scheduling scheme is accurately customized for the DAG of each Spark job in a multi-job environment through the iterative search of the particle swarm based on the meta-heuristic particle swarm optimization algorithm, so that the overall operation cost of a hybrid cloud deployment cluster is effectively reduced.

According to some aspects of the disclosure, a path generation apparatus for generating a movement path of at least one drone for inspection of an object, the path generation apparatus comprises a problem solving unit configured to output a solution by solving a vehicle routing problem model by using a metaheuristic technique with respect to a graph in which a connection relationship among a plurality of checkpoints included in the object is defined as a target and a path generation unit configured to generate the movement path of the at least one drone based on the outputted solution.

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 present invention provides a project scheduling rule mining method and system based on gene expression programming (GEP), which is used to solve the multi-objective optimization problem of multi-skill, resource-constrained project scheduling. During the solution process, previous project information is used as training set data. Project information and resource information are integrated to extract various feature attributes with decision-making value. These attributes are combined with several basic mathematical operators to form the genetic source of soft chromosomes. Each soft chromosome represents a hybrid scheduling rule, which is used as a decision-making method for the order of task execution to obtain a specific scheduling solution. The solution process uses an improved GEP algorithm, which designs a backward traversal decoding method, incorporates four neighborhood structure operators and a rule mining perturbation mechanism, and improves the ENS ranking method used in the solution evaluation process. This greatly improves the algorithm performance and the efficiency of exploring the frontier solution set. The resulting metaheuristic rule set can be easily applied to real projects and production environments.

The application discloses a plane feature detection method for a real scene three-dimensional model. The application first analyzes the real scene three-dimensional model into a primitive set, then comprehensively considers the spatial proximity feature, the surface feature and the color feature of the primitive to construct a primitive heterogeneity distance formula, and clusters the primitives into a series of clusters with uniform properties and regular boundaries, extracts the plane features based on the clusters by using the Euclidean space distance and the cosine measurement value, and uses a meta heuristic optimization algorithm to improve the automation degree of the method. The application can effectively detect large planes in the real scene three-dimensional model, has the advantages of high recognition rate, high efficiency and easy generation of regular boundary planes, and can provide support for real scene three-dimensional model building extraction, objectization, data compression, model simplification and roof extraction and the like.