102 results about "Metaheuristic algorithms" patented technology

The invention provides an intelligent path planning method and system based on a large language model, and relates to the field of path planning. According to the invention, an LLM-based multi-level collaborative optimization framework optimizes an original mathematical model and an algorithm strategy for a new demand described by a natural language by integrating the semantic understanding ability of the LLM, so that the algorithm strategy is optimized on the basis of a constructed first mathematical model and a constructed first genetic algorithm. The optimized second mathematical model and the optimized second genetic algorithm are generated in combination with changing new requirements, so that the problems of expressive limitation of engineering modeling by directly using LLM cue words and insufficient field adaptability of generating a meta-heuristic algorithm can be solved, and the scene adaptability of a traditional framework can be improved. Besides, the code proxy based on the MCP is constructed, and a hierarchical progressive self-correction mechanism of the code proxy is combined with standardized scheduling of the MCP, so that omnibearing automatic processing from a surface code error to a deep mathematical model problem is realized.

The invention relates to an air coupling ultrasonic signal processing method based on a COA-VMD combined improvement wavelet threshold, and the method specifically comprises the following steps: 1, employing a meta-heuristic algorithm Coati, and introducing a minimum envelope entropy as a fitness function of the algorithm, and taking the minimum envelope entropy as an evaluation criterion of an optimal solution; 2, decomposing a noisy part in the air coupling ultrasonic signal into a plurality of intrinsic mode function components by using the optimized VMD decomposition parameters; and 3, on the basis of the step 2, distinguishing an effective ultrasonic signal component f1 and a noise component f2 in the detected IMF components by adopting a correlation coefficient method. And step 4, processing the noise component f2 by adopting an improved wavelet threshold function algorithm to obtain a noise-reduced air coupling ultrasonic signal. According to the method, COA-VMD and an improved wavelet threshold noise reduction method are combined, the signal-to-noise ratio is effectively improved, and a technical basis is provided for high-precision detection of defects in the honeycomb sandwich structure.

The invention discloses a closed-loop supply chain network optimization method for improving a non-dominated sorting genetic algorithm, and relates to the technical field of calculation methods. The method comprises the following steps: establishing an optimization model; setting parameters; generating an initial solution; a fitness function value is calculated, and rapid non-dominated sorting is carried out; updating the population; judging whether the current generation is greater than the maximum number of iterations; calculating a comprehensive state value; calculating a reward function value; performing action selection and parameter adjustment; and outputting a result. Aiming at the problems that a traditional meta-heuristic algorithm is insufficient in solution set diversity, prone to falling into a local optimal solution, sensitive in parameter and complex in adjustment when solving a multi-objective optimization problem of a closed-loop supply chain network, the method is improved on the basis of an NSGA-II algorithm, an intelligent learning mechanism is introduced to enhance the adaptability and dynamic response capability of the algorithm, the optimization process is accelerated, and the optimization efficiency is improved. Therefore, the overall solution quality is improved, the local optimal risk is reduced, and the calculation efficiency is improved.

The invention relates to the technical field of refrigeration system control, in particular to a refrigeration system cooperative control method and system, a medium and a terminal. The control method comprises the steps that running state parameters of the refrigerating system are obtained in real time; predicting the temperature and energy efficiency of the refrigeration system based on the constructed and initialized physical prediction model; performing global optimization on the operation state parameters and the predicted temperature and energy efficiency based on a constructed and initialized meta-heuristic algorithm to generate optimization control parameters; generating a real-time adjusting instruction according to the current operation state parameter by utilizing a PID controller; and the optimization control parameters and the real-time adjustment instruction are fused to generate a dynamic cooperative control signal, and the dynamic cooperative control signal serves as an output control signal of the refrigeration system. Through the arrangement, the calculation burden can be reduced, the real-time performance can be improved, the method can adapt to high-precision control of a complex multi-physics field coupling scene, and the energy efficiency, the dynamic response speed and the multi-working-condition adaptability of the refrigeration system are remarkably improved.

The invention belongs to the technical field of production scheduling, and discloses a hybrid flow shop scheduling method based on a double-agent neighborhood search algorithm, and the method comprises the following specific steps: 1, researching a real production scene of a hybrid flow shop, mining constraint conditions, and analyzing a main scheduling problem which mainly affects a production cycle; the invention integrates the advantages of a deep Q network (DQN) and a meta-heuristic algorithm, provides a double-agent neighborhood search algorithm, can quickly respond to the change of a workshop environment and dynamically adjust a scheduling strategy, and aims to provide a new efficient solution scheme for hybrid flow shop scheduling and derivative problems thereof. Two intelligent agents capable of interacting with the workshop environment in real time are constructed, wherein the first intelligent agent can determine the machining sequence of workpieces in each stage, and a promising initial solution is rapidly generated; and the second agent can select an optimal neighborhood search strategy, and dynamically adjust the search direction to accelerate convergence of the algorithm.

The invention discloses a mechanical arm excitation trajectory optimization-based kinetic parameter identification method, which comprises the following steps of: constructing a corresponding Newton-Euler kinetic model based on a target mechanical arm, and obtaining a linear kinetic model through linear transformation and parameter recombination; based on the linear dynamics model, a staged periodic Fourier series excitation track based on all joint positions of the target mechanical arm is constructed; a quantum behavior and Levy flight hybrid particle swarm optimization algorithm is adopted to optimize the excitation trajectory, and constraint conditions of a regression matrix in the linear dynamic model are obtained; and based on the constraint conditions, adopting an improved hyperbolic sine and cosine optimization algorithm, and identifying the minimum parameter set and the joint torque of the linear dynamics model. According to the method, continuous excitation of the kinetic parameters in robot system recognition is achieved by improving the meta-heuristic algorithm, the decoupling problem of the kinetic parameters is solved, and therefore the precision of the mechanical arm kinetic model is improved.

The invention discloses a building cold load cycle prediction method based on boundary feature protection and an HOA-LightGBM model, and belongs to the field of building intellectualization, and the method comprises the following steps: S1, data collection and cleaning; s2, carrying out smoothing processing on the GCMWSG filtering; s3, selecting evaluation indexes; and S4, an HOA-LightGBM hybrid model is constructed, and comprehensive evaluation is carried out. According to the building cold load period prediction method based on boundary feature protection and the HOA-LightGBM model, a periodic boundary protection mechanism is introduced, and the problem of boundary distortion occurring when periodic air conditioner load data are processed through traditional MWSG filtering is effectively solved. Experimental results show that the performance of the prediction model is remarkably improved while the load characteristics of the data processed by the GCMWSG at the start and stop moments are reserved; according to the HOA-LightGBM hybrid model provided by the invention, through deep fusion of a meta-heuristic algorithm and a gradient lifting framework, the problems of low parameter tuning efficiency and insufficient prediction precision of the shallow model are solved, and an efficient and reliable technical scheme is provided for real-time prediction of the building cold load.

The invention belongs to the field of green port planning and operation, and discloses a resource-saving type spraying operation intelligent control method considering dynamic change of stacking of a storage yard in a dry bulk port district, which comprises the following steps of: calculating spraying operation resource consumption, including power consumption of a water supply pump station and total water consumption of spraying operation of all material piles in the storage yard; and establishing a storage yard distribution model, wherein an objective function comprises the storage delay time cost of the material pile, the power consumption cost of the spraying operation and the water resource consumption cost of the spraying operation. The model is linearized, and part of constraints are linearized. The method comprises the following steps: firstly generating a material pile problem instance for a subsequent experiment, then introducing three rule-based scheduling methods as references, and determining the material pile distribution efficiency by testing a storage yard distribution model, a rule-based method, different stacking densities and different material piling and taking efficiency conditions and comparing with a meta-heuristic algorithm. The effectiveness and adaptability of the storage yard space distribution model are verified.

Aspects described herein relate to the automatic generation of floorplan layouts based on a floorplan image. Image data comprising an image of an area may be accessed. Based on the image data, a floorplan of the area may be determined. A determination of whether constraints associated with occupancy are met may be made. Based on the constraints being met, and based on the floorplan, a light zones map may be generated. Based on the light zones map, spatial zones corresponding to the light zones may be determined. Based on the spatial zones, candidate floorplan layouts may be generated. Based on application of metaheuristic algorithms or machine-learning models to the candidate floorplan layouts, a subset of candidate floorplan layouts may be selected from the plurality of candidate floorplan layouts. Furthermore, floorplan layout data comprising a subset of floorplan layouts for use by a design application may be generated.

The present invention provides an intelligent path planning method and system based on a large language model, which relates to the field of path planning. In the present invention, a multi-level collaborative optimization framework based on LLM optimizes the original mathematical model and algorithm strategy for the new requirements described in natural language by integrating the semantic understanding ability of LLM, and realizes that on the basis of the first mathematical model and the first genetic algorithm that have been constructed, the optimized second mathematical model and the second genetic algorithm are generated in combination with the new requirements of the changes. This can not only overcome the expressive limitations of engineering modeling directly using LLM prompts and the insufficient domain adaptability of generating metaheuristic algorithms, but also improve the adaptability of traditional frameworks to scenarios. In addition, a code agent based on MCP is constructed, and its hierarchical and progressive self-correction mechanism is combined with the standardized scheduling of the MCP protocol to achieve all-round automated processing from surface code errors to deep mathematical model problems.

Aspects of the disclosure relate to s computing system that is configured to use heuristic and / or metaheuristic algorithms based on swarm learning (SL) intelligence frameworks and combine SL with blockchain and edge computing frameworks to provide a technologically efficient, responsive, and / or adaptable solution to detecting and preventing defects in software applications.

The invention discloses an urban rail transit peak period train departure interval optimization method based on multi-line cooperative scheduling, and relates to the technical field of rail transit operation management. The method comprises the following steps: firstly, constructing a network topology containing key transfer stations and a passenger flow OD matrix; establishing a passenger travel space-time network model to accurately describe a transfer waiting process; further constructing an optimization model with the goal of minimizing the total cost of the system, wherein the model comprehensively considers the passenger waiting time, the in-vehicle crowding degree, the operation cost and the transfer collaboration degree; and finally, designing a mixed intelligent algorithm combining Lagrangian relaxation and a meta-heuristic algorithm for solving, and generating a multi-line cooperative departure interval scheme. According to the method, the limitation of single-line independent scheduling is broken through, resource allocation can be optimized from the overall perspective of a line network, the total travel time of passengers and transfer station congestion are effectively reduced, and the overall transportation efficiency and service quality of a rail transit system in peak periods are remarkably improved.

The invention provides a dynamic pricing method, system and device for a cloud service provider, and a storage medium, and relates to the field of network resource evaluation. According to the characteristics of different cloud services in a cloud federation computing platform, a meta-heuristic algorithm and a pricing strategy of deep reinforcement learning are provided for universal cloud services and special cloud services respectively. According to the method, multiple factors influencing cloud service pricing are fully considered, time sequence features, continuous features and classification features are involved, and the optimal pricing strategy is dynamically adjusted according to historical data and real-time environment states of services. According to the method, a proper pricing strategy is adopted for different types of cloud services, a distribution and profit balance point is sought, the service quality of the cloud federation is improved, resource use is optimized, and the profit of a cloud service provider in the federation is improved to the maximum extent.

The present invention provides a multi-period freight pricing and logistics network planning method, which relates to the technical field of logistics pricing and network planning. This method obtains relevant business information from a logistics planning database; preprocesses the business information to obtain model input data; determines a multi-period freight pricing and logistics network planning model based on the model input data; obtains a hybrid metaheuristic algorithm according to the particle swarm algorithm and the differential evolution algorithm; uses the hybrid metaheuristic algorithm to solve the multi-period freight pricing and logistics network planning model to obtain a logistics planning scheme; and determines the final freight pricing and the implementation scheme of the logistics network planning according to the logistics planning scheme. The present invention can determine the freight price according to the behavior decisions of different types of customers in the logistics network planning, and take into account the selection of logistics carriers, improving the practicability of the logistics network planning; and can improve the planning efficiency on the premise of ensuring the solution quality, increasing the profits of enterprises.

The invention provides an airborne infrared image ship detection method and system based on feature fusion and FVIM-HLOA, and relates to the technical field of target detection. According to the technical key points, the method comprises the following steps: obtaining an airborne infrared image containing a marine ship, and carrying out preprocessing on the airborne infrared image; extracting a multi-dimensional feature map from the preprocessed image; adopting a fuzzy variable index meta-heuristic algorithm to perform adaptive clustering on the multi-dimensional feature map to generate a region-of-interest mask; and obtaining an optimal segmentation threshold value by using an improved angular skill optimization algorithm, and carrying out multi-threshold adaptive optimal segmentation on the mask of the region of interest by using the optimal segmentation threshold value to obtain a final segmentation map. The method has the advantages of being high in feature discrimination capability, efficient and accurate in detection framework, high in optimization capability of an optimization algorithm, high in environmental adaptability and the like, and has good generalization capability and practical value.

The present invention discloses a three-dimensional space robot path planning method based on a fusion metaheuristic algorithm. This method drives a robot in three-dimensional space from a starting point to an end point and searches for the optimal movement direction during the movement. The core of this method is the fusion metaheuristic algorithm. The fusion metaheuristic algorithm is an improvement on the conventional gray wolf optimization algorithm, which suffers from the drawback of prematurely falling into local optimality. Therefore, a nonlinear convergence factor and adaptive weight coefficient are designed to balance global and local search, and then integrated into the balanced optimization algorithm. By designing an iterative competition mechanism, the algorithm convergence speed is increased, thereby improving the operational efficiency of the robot path planning.

The application discloses a building cold load cycle prediction method based on boundary feature protection and an HOA-LightGBM model, and belongs to the field of building intelligence, and comprises the following steps: S1, data acquisition and cleaning; S2, GCMWSG filtering for smoothing processing; S3, selecting evaluation indexes; and S4, constructing an HOA-LightGBM hybrid model and comprehensive evaluation. The application adopts the building cold load cycle prediction method based on boundary feature protection and the HOA-LightGBM model, and by introducing a periodic boundary protection mechanism, effectively solves the boundary distortion problem of traditional MWSG filtering when processing periodic air conditioning load data. Experimental results show that the data processed by GCMWSG retains the load characteristics of start and stop moments, and significantly improves the performance of the prediction model; and the HOA-LightGBM hybrid model provided by the application solves the problems of low parameter optimization efficiency and insufficient prediction accuracy of a shallow model by deeply fusing meta-heuristic algorithms and gradient boosting frameworks, and provides an efficient and reliable technical scheme for real-time prediction of building cold load.

The invention relates to the field of intelligent manufacturing systems of aluminum alloy casting workshops, in particular to a molten aluminum supply production line for aluminum alloy low-pressure casting and a control method of the molten aluminum supply production line for aluminum alloy low-pressure casting. And the optimization model is solved in combination with the operation process and logic of the molten aluminum supply production line, so that the optimal solution of the operation parameters of the multiple devices is obtained. Besides, when the optimization model is solved, a pause probability model is introduced, and the optimal solution of the operation parameters under the target of achieving the minimum cost in the operation process of the molten aluminum supply process is obtained under the condition that pause is introduced. According to the aluminum liquid supply process proactive optimization method provided by the invention, the optimization model and the solving method based on the multi-agent simulation and meta-heuristic algorithm are constructed, so that the efficient optimization of the operation parameters is realized, and the purposes of reducing cost and improving efficiency are remarkably achieved.

The invention discloses a fishery oxygenation control optimization method based on a meta-heuristic algorithm, and belongs to the technical field of control optimization methods, and the method mainly comprises the following steps: S1, constructing a fishery oxygenation PID control system; s2, constructing an improved white sheep seat element heuristic algorithm module; s3, optimizing and setting parameters; and S4, parameter setting and effect optimization. According to the method, by introducing strategies such as interstellar distant symptoms, emotional outbreak and nebula aggregation, the global optimization and convergence capacity is remarkably enhanced, nearly-zero overshoot of dissolved oxygen adjustment is achieved through the PID controller set through the improved white sheep seat element heuristic algorithm, the response time can be effectively shortened, the steady-state error can approach zero, and the method is suitable for large-scale popularization and application. In this way, energy consumption and accumulative errors can be effectively reduced while rapid and accurate control is guaranteed, and the robustness of a fishery oxygenation PID control system can be remarkably improved.

Aspects of the disclosure relate to s computing system that is configured to use heuristic and / or metaheuristic algorithms based on swarm learning (SL) intelligence frameworks and combine SL with blockchain and edge computing frameworks to provide a technologically efficient, responsive, and / or adaptable solution to detecting and preventing defects in software applications.

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.

The invention provides an unmanned aerial vehicle three-dimensional path planning method and system based on a meta-heuristic algorithm of a competition search strategy, and aims to solve the problem of efficient and safe path planning of an unmanned aerial vehicle in a complex environment. According to the method, a flight starting point and a flight ending point are determined by constructing an unmanned aerial vehicle flight environment, and constraint conditions of a path planning problem are constructed based on the determination. The constraint conditions comprise the flight path length, the flight height fluctuation, the turning angle and obstacle and terrain constraints of the unmanned aerial vehicle, and it is ensured that path planning meets the safety and feasibility requirements of actual flight. The cost function is minimized through an optimized meta-heuristic algorithm based on a competitive search strategy to obtain an optimal unmanned aerial vehicle path. Experimental results show that the method provided by the invention can quickly generate a flight path with short flight distance and high smoothness.

This invention belongs to the field of production scheduling technology and discloses a hybrid assembly line workshop scheduling method based on a dual-agent neighborhood search algorithm. The specific steps are as follows: Step 1: Study the real-world production scenario of the hybrid assembly line workshop, identify constraints, and analyze the main scheduling problems affecting the production cycle. This invention integrates the advantages of Deep Q-Network (DQN) and metaheuristic algorithms, and proposes a dual-agent neighborhood search algorithm. This algorithm can quickly respond to changes in the workshop environment and dynamically adjust the scheduling strategy, aiming to provide a new and efficient solution for hybrid assembly line workshop scheduling and its derivative problems. This invention constructs two agents that can interact with the workshop environment in real time: the first agent can determine the processing sequence of workpieces at each stage and quickly generate promising initial solutions; the second agent can select the optimal neighborhood search strategy and dynamically adjust the search direction to accelerate the convergence of the algorithm.

The invention discloses a multi-scale grid-based middle and low altitude space resource matching method, which comprises the following steps of: firstly, constructing a 5D (5-dimensional) universal grid system comprising a geographic space dimension, a time dimension and a frequency dimension, and distributing a unique multi-dimensional code identifier for each grid unit; therefore, unified and standardized digital description of the urban low-altitude environment is realized. On this basis, a multi-scale grid is constructed through a dynamic grid granularity adjustment mechanism, and in an open airspace, coarse-grained grid nodes are selected to quickly guide a global track; and in a dense airspace, switching to a fine-grained grid node to carry out accurate obstacle avoidance and local track optimization. Then, based on the 5D multi-scale grid, the planned route of the detection platform and the working platform is mapped into a route grid changing along with time; and the feasibility and conflict risk of the resource matching link are judged by accurately calculating the intersection of the air line grids in the space-time and frequency dimensions. And finally, constructing an optimization model which aims at maximizing the overall efficiency of air resource utilization, and dynamically allocating a detection platform and an operation platform by using a meta-heuristic algorithm to realize intelligent planning and real-time optimization of an air route. According to the invention, digital and intelligent management of the middle-low-altitude airspace is realized, and the resource utilization rate and cooperation efficiency of the middle-low-altitude airspace are effectively improved.

The application discloses a data center computing power scheduling management method and system, and relates to the technical field of computing resource management. Through obtaining virtual machine computing power use data and demand prediction, prediction data is obtained, and the load state of a physical machine is judged; a to-be-migrated virtual machine set is determined according to the load state of each physical machine; in combination with the physical machine specifications, a meta-heuristic algorithm is used to optimize to-be-migrated virtual machine distribution, and a virtual machine placement scheme is generated. Compared with a traditional passive response strategy, the scheme can plan migration according to future load states, reduce unnecessary migration operations and migration overhead, improve physical machine resource utilization at the same time, and realize efficient scheduling and resource allocation optimization of virtual machine migration.

This invention discloses a hybrid power allocation method for an IRS-assisted VLC downlink NOMA system, belonging to the technical field of power allocation methods. Addressing the problems of easy occlusion of line-of-sight links, low efficiency of fixed power allocation, and high complexity of pure metaphysical heuristic algorithms in existing technologies, this invention jointly optimizes the reflection angle of intelligent reflective surfaces and the three-dimensional position of the UAV to construct a composite line-of-sight and non-line-of-sight communication link, enhancing anti-occlusion capabilities. Simultaneously, it proposes a modified fixed power allocation method, introducing a dynamic allocation factor based on user channel gain, achieving adaptive power allocation with the weakest user as the benchmark, improving fairness and efficiency. Furthermore, it integrates the modified fixed power allocation with the Ocean Predator algorithm to form a hybrid power allocation framework. This framework reduces the search space with low-complexity structured allocation, and then uses the Ocean Predator algorithm to globally and jointly optimize the UAV position, reflection angle, and dynamic allocation factor, balancing high system summation rate and fast convergence performance.

Aspects of the disclosure relate to s computing system that is configured to use heuristic and / or metaheuristic algorithms based on swarm learning (SL) intelligence frameworks and combine SL with blockchain and edge computing frameworks to provide a technologically efficient, responsive, and / or adaptable solution to detecting and preventing defects in software applications.

The invention discloses a composite material hot press molding production scheduling method based on a man-hour uncertainty dynamic transmission mechanism. In order to solve the problems that in the hot press molding process of the composite material, the working hours have uncertainty and are easily transmitted and amplified among multiple batches and multiple devices, a working hour uncertainty dynamic transmission mechanism is constructed, and the propagation rule of the workpiece machining time uncertainty between a batch layer and a machine layer is described; on this basis, constraints including an equipment capacity constraint and a workpiece and equipment matching constraint are comprehensively considered, a scheduling optimization model with minimization of the maximum completion time as an optimization target is established, and an optimization adjustment strategy is deduced; and finally, designing a mixed meta-heuristic algorithm to solve the scheduling optimization model in combination with an optimization adjustment strategy, thereby realizing efficient search of an optimal scheduling scheme. According to the method provided by the invention, the influence of uncertainty on the design of the production scheduling scheme can be effectively reduced, and the scheduling efficiency and stability of the composite material hot press molding process are improved.

The invention discloses a rockburst disaster prediction method based on an optimized extreme gradient lifting classification model, and relates to the technical field of environment monitoring, and the method comprises the steps: obtaining multi-shift monitoring data of a coal mine working face, carrying out the preprocessing, dividing the data into a training set and a test set, and taking an initial hyper-parameter extreme gradient lifting classifier as a basic model, and using at least one meta-heuristic optimization algorithm to optimize the hyper-parameters, training and constructing a hybrid prediction model, inputting real-time monitoring data into the hybrid prediction model to carry out rockburst disaster prediction, and outputting a coal mine rock burst disaster risk prediction result. According to the method, the technical problems of low disaster prediction accuracy and dangerous state recall rate and high missing report risk caused by insufficient sensitivity of an existing coal mine rock burst disaster prediction model to unbalanced monitoring data are solved, and the purposes of optimizing an extreme gradient lifting classifier through a meta-heuristic algorithm and improving the prediction accuracy of the coal mine rock burst disaster prediction model are achieved. The disaster prediction accuracy and the dangerous state recall rate are improved, and the missing report risk is reduced.

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.