56 results about "Metaheuristic algorithms" patented technology

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 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 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.

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.

This invention belongs to the field of marine engineering antifouling material technology and artificial intelligence optimization technology. It discloses a collaborative optimization method for the antifouling stability and safety of antifouling materials used in debris-blocking nets, comprising the following steps: Step 1, collecting multi-dimensional data and preparing samples of the antifouling materials for the debris-blocking nets; Step 2, constructing and training an AI prediction model: selecting key feature parameters from the multi-dimensional data, using these key feature parameters as core feature vectors to construct a dual-objective prediction model, which outputs predicted values ​​for the material's antifouling stability index and environmental safety index; Step 3, collaborative optimization under multiple constraints: setting multiple constraints for the material; using the hybrid meta-heuristic algorithm ALO-KHO to solve the multi-objective optimization problem; selecting the optimal solution from the Pareto optimal solution to obtain the parameter combination of the antifouling materials for the debris-blocking nets with the highest score. The prediction model of this invention has high accuracy, strong generalization ability, and significant multi-objective collaborative optimization effect.

The invention provides a multi-unmanned aerial vehicle path planning method based on a communication network. The method comprises the following steps: based on path length evaluation, turning angle evaluation, climbing angle evaluation, obstacle avoidance evaluation and multi-unmanned-aerial-vehicle communication evaluation, setting a multi-unmanned-aerial-vehicle overall target function of the multi-unmanned-aerial-vehicle path planning method based on the communication network; performing mathematical modeling on the terrain and obstacles passed by the path of the unmanned aerial vehicle, and constructing a three-dimensional simulation terrain model; and based on the three-dimensional simulation terrain model, adopting a fuzzy ant colony algorithm based on an artificial potential field to solve the multi-unmanned aerial vehicle overall target function, and obtaining a path planning result of the unmanned aerial vehicle. According to the method, the fuzzy control thought is fused into a meta-heuristic algorithm, intelligent parameter adjustment is achieved, adaptive adaptation can be achieved according to the search stage and problem features, local and global search is balanced, the convergence speed and the solving quality are improved, and the multi-unmanned-aerial-vehicle cooperative path planning requirement based on the communication network is met.

This invention provides a method, apparatus, and electronic device for recommending playback layouts in a passenger information system. The method involves generating an initial layout population based on preset layout parameters; converting this initial layout population into multiple previewable and rating layout images according to layout decoding rules; and pushing these previewable and rating images to the user terminal. In response to the ratings of each layout image from the user terminal, a metaheuristic algorithm is used to iteratively optimize the initial layout population, generating at least one recommended layout, which is then recommended to the user terminal. This achieves intelligent layout generation, shortens the layout production and update process, saves labor costs, and can be continuously optimized based on user preferences, thereby improving the operational efficiency of the passenger information system.

The invention discloses an artificial intelligence logistics planning intelligent processing method based on data analysis, and mainly relates to the technical field of intelligent logistics planning. Comprising the following steps: collecting multi-source data from each link of a logistics supply chain, and constructing a feature vector set; constructing a deep learning neural network model, and training the deep learning neural network model by using historical logistics data to complete prediction of future logistics demands by the model; constructing an intelligent logistics planning model according to the predicted logistics demand; solving and optimizing the intelligent logistics planning model by adopting a mode of combining a heuristic algorithm and a meta-heuristic algorithm; the optimized logistics planning scheme is converted into a specific execution instruction, and whether execution abnormity exists or not is monitored; and starting an exception handling mechanism when monitoring that an exception occurs in the logistics execution process. The method has the beneficial effects that the optimal configuration of logistics resources is realized, the efficiency is improved, the cost is reduced, and the service quality is ensured.

The invention discloses a dynamic multi-target flexible job shop scheduling method based on deep reinforcement learning. According to the method, a dynamic multi-target flexible job shop scheduling problem is modeled as a Markov decision process, and a lightweight deep reinforcement learning network named as A2DC-Net is provided for solving. The A2DC-Net dynamically focuses on key scheduling features by introducing a feature extraction module of an attention mechanism; an improved Actor-Critic framework comprising double independent Critic networks is adopted, values of different optimization targets are evaluated respectively, and multi-target strategy optimization is guided accurately; and a novel multi-target reward function is combined for training. According to the method, light weight is strived on the network structure, the calculation burden is remarkably reduced while the scheduling quality is ensured, and efficient real-time response to dynamic events is realized. Experiments show that the method is superior to a traditional scheduling rule, a meta-heuristic algorithm and other advanced deep reinforcement learning methods in convergence speed, multi-objective optimization performance and generalization ability.

The application provides a flexible workshop scheduling optimization method based on a meta-heuristic algorithm, data sets of flexible workshop scheduling are acquired; a differential evolution algorithm based on a trigonometric function mutation is adopted to process the data sets of flexible workshop scheduling and preliminarily form a scheduling scheme by introducing a nonlinear disturbance and a dynamic adjustment mechanism of a companion population; the adaptive hill climbing algorithm is adopted to optimize a current optimal solution and process again to form an optimal scheduling scheme by combining a multiple disturbance mechanism and a disturbance intensity adjustment strategy; and the optimal scheduling scheme achieving the maximum completion time is visually presented; the flexible workshop scheduling optimization method based on the meta-heuristic algorithm can improve the ability to jump out of a local optimum, can significantly reduce the maximum completion time and can improve the solution efficiency of the flexible workshop scheduling problem.

The application provides a pairing method and device for non-fixed additional pseudo codes, wherein the method comprises the following steps: setting pairing indexes, the first index being the aligned cross-correlation value absolute value, and the second index being the absolute value of the difference between the leading and lagging correlation values; determining a target function and a cost matrix based on the first index and the second index; establishing an additional pseudo code allocation model based on the cost matrix and an allocation matrix; dividing the additional pseudo codes into two parts, determining an initial population based on the division result, and solving the model by using a JVC algorithm to determine the initial pairing scheme corresponding to the individuals in the initial population; performing iterative updating on the population based on a meta-heuristic algorithm or an improved meta-heuristic algorithm, and performing iterative updating on the pairing scheme corresponding to the individuals based on the JVC algorithm until a preset maximum number of iterations is reached, and determining the optimal pairing scheme obtained in the last iteration process as the optimal pairing scheme of the non-fixed additional pseudo codes; wherein the optimal pairing scheme obtained in the iteration process is determined based on the target function.

The application provides a stamping shape and performance synergistic control digital process design system, method, medium and equipment, belonging to the field of metal material plasticity processing and digital manufacturing. The application aims to solve the technical problem that the stamping process design is difficult to synergistically optimize the shape precision and use performance of the formed part. The system comprises: a multi-field coupled finite element simulation module for simulation using a coupled constitutive model considering the influence of temperature, strain and phase change; a shape and performance synergistic target decision module for evaluating the simulation results based on a multi-target evaluation system including shape precision and use performance indicators; a process parameter intelligent optimization module for using a hybrid optimization strategy combining machine learning and meta-heuristic algorithms to optimize the stamping process parameters based on the evaluation results. The application can realize shape and performance synergistic optimization, improve the overall quality of the product, and shorten the development cycle.

This invention discloses a vehicle routing optimization method for waste collection, comprising constructing a multi-objective cost function integrating fixed deployment, dynamic load-bearing fuel consumption, and processing station variance penalties; rigidly configuring a "fleet size reduction mechanism" as an external iterative global constraint; and relentlessly squeezing idle capacity through rolling probing. The underlying hybrid improved metaheuristic algorithm incorporates a load balancing heuristic factor and seamlessly coordinates with a variable neighborhood descent search operator for node temporal reconstruction. This invention utilizes a collaborative clustering algorithm with capacity overflow blocking to perform macroscopic cluster-level dimensionality reduction on discrete nodes, significantly reducing the overall fleet size, lowering comprehensive energy consumption, and achieving balanced distribution of throughput pressure on sanitation physical processing facilities. It can be applied to vehicle routing optimization in complex waste collection networks encompassing multiple vehicle depots and processing stations, solving the problems of redundant transport resources and unbalanced load on underlying facilities in large-scale road networks.

The invention provides an LLM-based mechanical system reliability design method and a related device, belongs to the field of reliability analysis and optimization design, and aims to quickly generate high-quality design points by dynamically feeding back key information of the design points to a large language model in combination with an iteration mechanism of a large model and a meta-heuristic algorithm. And the performance optimization is realized while the reliability constraint is satisfied. The LLM-RBDO optimizes the calculation process through software and hardware collaboratively, and efficient generative optimization search is achieved. The CPU is mainly responsible for data preprocessing, proxy model training and optimization search, the GPU is responsible for LLM reasoning calculation, the CPU and the GPU carry out data transmission through CUDA, and the response speed of LLM in an optimization task is accelerated. According to the method, the performance of the method in automobile structure design and aircraft engine jet pipe structure design cases is evaluated through a Deepseek-V3 model. Experimental results show that the method can find a solution meeting design requirements, and the convergence speed of the method is higher than that of a traditional genetic algorithm in a test case.

The invention discloses a method for predicting rock shear strength based on Bayesian method-meta-heuristic optimization. The method comprises the following steps: 1) preprocessing original data; 2) performing Bayesian optimization on the four models, outputting four improved models, and selecting an optimal improved model from the four improved models; 3) adjusting hyper-parameters of the optimal improved model by using six meta-heuristic algorithms; (4) predicting the training set and the test set by using the output model in the step (3), calculating evaluation indexes, and selecting one with the optimal comprehensive performance; and 5) predicting all the test samples by using the determined optimal SMA-RF model, and outputting predicted values of the cohesive force c and the internal friction angle Phi. The rock mechanics parameter prediction method belongs to the technical field of rock mechanics parameter prediction, and relatively fully considers different geological conditions, sampling differences and multi-scale structure effects, and prediction errors meet requirements.

A method for generating personalized learning path based on a metaheuristic algorithm is disclosed, comprising: initializing a candidate solution population; using a concept coverage function, a time penalty function and a style matching function, constructing a multi-objective fitness function to comprehensively score the candidate solutions; locally updating each candidate solution in the population and updating an expert age, followed by globally updating the candidate solution population by a dynamic control and a migration mechanism. When a set iteration count is reached, calculating and ranking a priority score of the learning material, and generating a personalized learning sequence by utilizing a weighted pooling method. Therefore, by adopting the method for generating a personalized learning path based on a metaheuristic algorithm as described above, issues of local optima and instability can be improved, and more efficient and accurate generation of personalized learning path within the multi-objective optimization can be achieved.

The present application relates to the PID controller self-tuning method and system based on combination fitness function optimization, the system includes controller module, system simulation module and parameter optimization module, the method includes constructing FNN-PID controller, the basic adjustment of FNN-PID controller output is respectively parameterized as a group of hyperparameter vectors through respective gain and bias;Based on FNN-PID controller and controlled object, a closed-loop system simulation model is established;Introduce the weighted error square integral-nonlinear penalty combination fitness function optimization artificial rabbit group meta-heuristic algorithm;With the hyperparameter vector as the optimization variable, the system simulation model is iteratively called to run, the fitness value is obtained and used as the optimization feedback;The optimal hyperparameters corresponding to the fitness value meeting the preset condition are output;The automatic and intelligent optimization of controller hyperparameters is realized, and the dynamic performance and robustness of the control system are significantly improved.

The invention provides a flexible workshop scheduling optimization method based on a meta-heuristic algorithm. The method comprises the following steps: acquiring a data set of flexible workshop scheduling; a nonlinear disturbance and concomitant population dynamic adjustment mechanism is introduced, a differential evolution algorithm based on trigonometric function variation is adopted, and a data set of flexible workshop scheduling is processed to preliminarily form a scheduling scheme; optimizing the current optimal solution for the formed scheduling scheme by adopting a self-adaptive hill-climbing algorithm in combination with a multi-disturbance mechanism and a disturbance intensity adjustment strategy, and processing again to form an optimal scheduling scheme; the optimal scheduling scheme for realizing the maximum completion time is visually presented; according to the flexible workshop scheduling optimization method based on the meta-heuristic algorithm, the capability of jumping out of local optimum can be improved, the maximum completion time can be remarkably shortened, and the solving efficiency of a flexible workshop scheduling problem can be improved.

The invention provides a hybrid optimization solution method and device based on generative adversarial network assistance, equipment and a storage medium. The method relates to the technical field of logistics optimization. The method comprises an off-line training stage and an on-line solving stage. In the offline training stage, a precise solver is used for solving a plurality of combinatorial optimization problem instances, and an optimal solution is obtained to form a data set; and constructing a conditional generative adversarial network, and performing adversarial training by taking the optimal solution as a real sample and taking the problem instance characteristics as conditional input. In the online solving stage, the features of the large-scale combinatorial optimization problem are input into the trained generator, and one or more high-quality initial solutions are generated; and by taking the initial solution as a starting point or an initial population, executing iterative search optimization of a meta-heuristic algorithm to obtain an optimal solution. According to the method, the knowledge obtained by the precise solver and the high efficiency of the meta-heuristic algorithm are combined, so that the problems of long solving time and low solution quality when a large-scale combinatorial optimization problem is processed in the prior art are solved.

The invention discloses an unmanned aerial vehicle flight path planning method based on a four-diagnosis collaborative optimization algorithm. The method comprises the following steps: determining a tth-round current flight path group corresponding to tth iteration; a heuristic search strategy of a four-diagnosis collaborative optimization algorithm is adopted, under the set multi-flight constraint condition, track points of each current track in the tth-round current track group are adjusted, and an optimized track group corresponding to the tth iteration is obtained; wherein the four-diagnosis collaborative optimization algorithm is a meta-heuristic algorithm which introduces a four-diagnosis co-reference thought and a monarch, minister, assistant and guide compatibility principle in traditional Chinese medicine diagnostics; taking the optimized track group corresponding to the t iteration as a (t + 1) current track group corresponding to the (t + 1) iteration, and repeatedly executing the steps until the number of iterations reaches the set maximum number of iterations to obtain a target track group; and taking the track corresponding to the highest fitness in the target track group as the optimal track of the unmanned aerial vehicle in the flight area. According to the invention, the flight path planning reliability can be improved.

The invention relates to the technical field of industrial robot motion control and intelligent optimization, in particular to a robot time optimal trajectory planning method and system based on hierarchical hybrid optimization. Comprising an input and modeling module, an upper-layer analysis guide module, a lower-layer intelligent precise search module, a collaborative verification module and a track output module, according to the method, through collaborative optimization of the layered architecture, on the premise that all physical constraints are strictly met, the total running time of the track can be shortened by 15%-25%, meanwhile, the peak value of a jerk index representing motion impact is predicted to be reduced by more than 45%, and faster and more stable collaborative improvement is achieved instead of tradeoff in the traditional sense; meanwhile, the layered architecture serves as a general framework, and a lower optimizer of the layered architecture has a plug-and-play characteristic and can be flexibly combined with various improved meta-heuristic algorithms (IWOA, IPSO, IGWO and the like), so that extremely high flexibility is provided for deployment in industrial scenes with different performance requirements.

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.