22 results about "Algorithm robustness" patented technology

The invention discloses a competitive evolution multi-task optimization method, system and equipment, and aims to solve the problem that an existing method is difficult to carry out collaborative optimization on fuel cost and gas emission under the condition that power balance and unit capacity constraint conditions are met. The method comprises the following steps: constructing main and auxiliary double-population parallel search; state indexes representing population convergence, diversity and feasibility are calculated in real time; based on the current state, a cooperation mode and evolution operator combined action is adaptively selected through a deep reinforcement learning agent; rewards are calculated according to improvement of the population on cost, emission and constraint satisfaction after action execution; and training the intelligent agent by using the state, the action and the reward, and iteratively optimizing the decision strategy until the Pareto optimal scheduling scheme meeting the constraint is output. According to the method, adaptive intelligent guidance of the evolutionary process is realized, and the optimization quality, the convergence speed and the algorithm robustness of the economic emission scheduling scheme of the power system are remarkably improved.

The invention discloses a virtual assembly method and system based on improved point cloud registration and precision feature extraction, relates to the technical field of virtual assembly, and aims to solve the technical problem that a conventional ICP algorithm is insufficient in robustness and precision in a point cloud registration link in a current virtual assembly technology based on point cloud data. Comprising a file processing module, a preprocessing module, a point cloud registration module and a many-to-many component matching scheme solving module. According to the method, the RICP algorithm is designed, and a general adaptive robust function is introduced, so that the problem that the traditional ICP algorithm is sensitive to noise and outliers is effectively solved. The robust function can dynamically give a weight according to a point pair distance, stable and high-precision registration can still be realized even in a scene of large point cloud initial pose difference, low overlapping degree or unobvious surface features, and the problem of insufficient robustness and precision of a traditional ICP algorithm in a point cloud registration link of a current virtual assembly technology based on point cloud data is solved.

The invention provides a hybrid optimization algorithm-based steel plate stack transfer sorting system and method and a storage medium. The method comprises the steps of firstly, obtaining a to-be-sorted steel plate set, the number of available temporary stack positions and a target sequence; generating an initial stack transfer strategy through a heuristic rule; secondly, performing iterative optimization on the initial stack transfer strategy by using a genetic algorithm to generate an optimal stack transfer strategy by taking the minimum operation time length and the number of temporary stack positions used for stack transfer sorting as targets; and finally, stack transferring equipment is controlled to execute the stack transferring step in the optimal stack transferring strategy, and moving operation of the steel plates between the stack positions is achieved till stack transferring sequencing is completed. According to the method, the heuristic rule, the GA and the DQN are fused, so that the ternary relationship among the time constraint, the equipment resource and the temporary stack position can be coordinated, and the idling rate of the equipment is reduced, so that the production process of a shipyard is optimized, the production efficiency is improved, and meanwhile, the production cost is reduced; according to the method, the limitation of a traditional single method is broken through, the algorithm robustness is higher, and the industrial application value is prominent.

The invention discloses an LMI-based intelligent suspension robust control and state observation method, and relates to the technical field of suspension control, and the method comprises the following steps: S1, splitting a control system into a non-matching disturbance subsystem and a matching disturbance subsystem; s2, determining state feedback control of an LMID algorithm according to the weight matrix of state control; determining disturbance compensation of a disturbance observer part in an LMID algorithm based on a weight matrix of a matching disturbance subsystem and disturbance estimation, and realizing the LMID algorithm by using an LMIob observer; and S3, taking the sprung acceleration based on the IMU in the suspension system as the input quantity of the LMIob observer, and obtaining state estimation of the control system. The method is designed based on an LMID algorithm of LMI, the algorithm has disturbance observation properties, the robustness of the algorithm can be improved, a weight matrix is introduced, and flexible adjustment of state control and disturbance estimation is achieved.

The invention discloses a mining subsidence prediction parameter solving method based on an improved center collision optimization algorithm, and belongs to the field of mine deformation monitoring data processing. Aiming at the defects that a basic center collision optimization algorithm is prone to premature convergence and insufficient in optimization precision when solving a mining subsidence parameter inversion problem, a Huber loss function is adopted to construct a fitness evaluation model to enhance the robust capability of the algorithm, and Cubic mapping is utilized to generate a random number to improve the stability of a search process, so that the method is suitable for the mining subsidence parameter inversion problem. And a self-adaptive elite-guided Cauchy variation mechanism is introduced to enhance the global exploration capability. According to the method, firstly, a probability integral method parameter inversion problem is constructed into an optimization model, and a population is initialized; performing double-space collaborative search in an original space and a decorrelation space constructed based on principal component analysis, and iteratively updating a population in combination with a dynamic space allocation strategy; and finally outputting an optimal parameter solution. According to the method, the convergence precision, stability and robustness of the algorithm in complex nonlinear parameter inversion are effectively improved.

The application provides an improved tabu search topology optimization method of a marine wind farm power collection system, which is based on the positions of wind turbines to intelligently preliminarily group and sort, to form an initial connection scheme, to connect all the wind turbines in each group with the minimum cost, to calculate the group cable cost, to check whether the lines cross, to impose a penalty in the total cost if the lines cross, to ensure that the scheme is feasible, to compare the current scheme with the historical best, to update the record and intelligently exchange the wind turbine groups to explore a new scheme, to repeat the optimization process until the number of iterations is satisfied, and to finally output a globally optimal topology scheme. The application has the advantages of better optimization effect, significantly improved economy, higher calculation efficiency, stronger engineering practicability, better algorithm robustness, better scheme reliability, high design automation degree and strong auxiliary decision-making capacity.

The invention relates to the technical field of ship control and simulation, in particular to a physical isolation-based ship control algorithm robustness verification method, which comprises the following steps of: constructing a ship model at an embedded end, and combining and establishing the ship model based on an MMG ship operation model for determining an inertia matrix parameter and a linear damping coefficient; the embedded terminal is connected with an upper computer, the upper computer issues a motion instruction to the ship model, the ship model outputs ship motion state data based on the motion instruction, ship type parameters and a sea condition disturbance scene, and the embedded terminal transmits the ship motion state data to the upper computer; and the upper computer compares the ship motion state data with a preset track, and evaluates the robustness of the ship control algorithm under different disturbances. According to the method, the execution effect of the control strategy on resource-limited hardware can be accurately reflected, and a reliable experimental foundation and engineering support are provided for testing the robustness of ship path tracking and attitude control research under complex sea conditions.

The present application relates to the technical field of cloud computing and distributed computing scheduling, and particularly belongs to a kind of serverless MapReduce job scheduling optimization method, comprising: initializing the operating parameter of bayesian genetic algorithm;Form initial population;Current population executes variable neighborhood search to generate new solution, update population;Bayesian probability model is constructed;New solution is generated by bayesian probability sampling and genetic operation, and population is updated;Global optimal solution is updated;Whether to reach time limit is judged, if reach, then the global optimal solution and its corresponding maximum completion time are output, if not reach, then continue iteration.The application has the positive effect of realizing the minimum maximum completion time of job and improving the robustness of algorithm and scheduling efficiency.

The invention relates to an intelligent algorithm application range analysis method based on region growth, and belongs to the technical field of intelligent algorithm robustness evaluation. According to the method, the capability boundary of an intelligent algorithm is iteratively generated by expanding a region growing and automatic searching method, firstly, a seed set is formed based on a targeted evaluation sample for preliminary evaluation, secondly, the capability boundary is iteratively described continuously by adopting a region growing method based on sample disturbance, and finally, contour lines are formed based on regression methods such as Kernel and the like. The safe distance is obtained through calculation, and support and guidance are provided for optimization iteration and landing application of an intelligent algorithm.

The invention discloses a well track automatic obstacle avoiding method for guiding type fence searching. The method is characterized by comprising the following steps that S1, a searching fence environment is built; s2, initializing an algorithm; and S3, node searching: S4, smoothing a search path to obtain an optimal and smooth path. By building a multi-layer fence search environment, parallel operation of an algorithm (multiple layers search paths at the same time) is facilitated, so that the overall search process is accelerated; the provided guiding type heuristic function is beneficial to guiding search to be carried out to areas not occupied by obstacles, the search space is reduced, the decision-making time in the search process is shorter, and the algorithm robustness is higher.

The application discloses a kind of competitive evolution multitask optimization method, system and equipment, to solve the problem that existing method is difficult to carry out collaborative optimization to fuel cost and gas emission under meeting power balance and unit capacity constraint condition.The method comprises the following steps: constructing main, auxiliary double population parallel search;Real-time calculation state index representing population convergence, diversity and feasibility;Based on the current state, through deep reinforcement learning agent self-adapting selection cooperation mode and evolution operator combination action;According to the improvement of population after action execution in cost, emission and constraint satisfaction, calculate reward;State, action and reward are used to train agent, iteratively optimize decision strategy, until the pareto optimal scheduling scheme that meets the constraint is output.The application realizes the adaptive intelligent guidance of evolution process, significantly improves the optimization quality, convergence speed and algorithm robustness of power system economic emission scheduling scheme.

The present application relates to the technical field of micro-grid optimization, and discloses a micro-grid optimization scheduling method and system based on an improved electric eel foraging optimization algorithm, which comprises constructing a target function with the minimum total operation cost of a micro-grid as the target, and establishing constraint conditions that the micro-grid needs to satisfy during operation; the micro-grid optimization scheduling model composed of the target function and the constraint conditions is solved based on the improved electric eel foraging optimization algorithm to generate an optimal scheduling scheme; the algorithm of the present application restructures the optimization mechanism in depth: in the initialization stage, an elite reverse learning strategy is adopted to mine potential high-quality solutions, significantly enhancing population diversity; in the migration and hunting stage of core evolution, a Cauchy mutation operator is introduced in combination with a tournament selection mechanism to improve the robustness of the algorithm while effectively avoiding premature convergence; in addition, a vertical and horizontal crossover strategy is fused to dynamically coordinate the algorithm behavior, achieving a precise balance between local search precision and global development capability.

The invention discloses a beam focusing method and system based on a liquid neural network and meta learning, and belongs to the technical field of wireless communication, and the method comprises the following steps: determining a system model; determining an optimization target; and joint optimization. According to the invention, the LNNM-BF algorithm with strong robustness and unsupervised learning is provided, the LNNM-BF algorithm designs a phase shift matrix through an index matrix and a phase compensation matrix, then an analog precoding matrix and a digital precoding matrix are designed at a BS end, and the problem of a near-field dual-beam splitting effect can be well relieved without introducing an extra true time delayer. Besides, the LNNM-BF algorithm enhances the adaptability of beam focusing to imperfect CSI through a meta-learning framework and improves the robustness of the algorithm, and the embedded LNN dynamically adjusts the topological structure and the connection weight between neurons through the currently input and previously implied iteration information to realize the maximization of SE.

The invention discloses a wavelet noise reduction method based on two-parameter optimization, relates to the technical field of digital signal processing, and solves the technical problems that in a traditional wavelet noise reduction method, selection of a threshold value and the number of decomposition layers depends on experience, and the noise reduction effect is unstable. According to the technical scheme, the method is characterized by comprising the steps of collecting noisy signals, performing multilayer wavelet decomposition, constructing a signal-to-noise ratio and root-mean-square error two-parameter evaluation index, performing joint optimization on a threshold value and the number of decomposition layers by adopting a self-adaptive optimization algorithm, performing wavelet threshold value noise reduction according to optimized parameters, and reconstructing the signals. According to the method, the optimal noise reduction parameter can be adaptively determined, the signal noise reduction quality and the algorithm robustness are remarkably improved, and the method is suitable for signal preprocessing in the fields of power system monitoring, mechanical vibration analysis, voice processing and the like.

The invention provides a metasurface off-grid DOA (direction of arrival) estimation method based on joint orthogonal matching pursuit. The metasurface off-grid DOA estimation method comprises the following steps: S1, constructing a joint over-complete dictionary containing a grid item matrix and a derivative item matrix; s2, setting initial parameters: a residual error and a support set; s3, calculating the inner product of the residual error and the joint over-complete dictionary to obtain a quantized value of the correlation between each column of the joint over-complete dictionary and the current residual error, and selecting a grid point index with the maximum joint correlation to be added into a support set; s4, utilizing a least square method to obtain a grid item and a derivative item coefficient corresponding to the current support set; s5, updating the residual error and circularly iterating S3 and S4 until a termination condition is met, namely, the number of iterations reaches the maximum value or the iteration error is smaller than a preset threshold value; and S6, according to the support set, the grid item and the derivative item coefficient, solving an off-grid offset and a spatial frequency estimation value, and realizing off-grid DOA estimation. The technical problem that an existing off-grid DOA estimation method is difficult to consider estimation precision, calculation complexity and algorithm robustness at the same time is solved.

The application discloses a civil aviation logistics intelligent loading system based on adaptive constraint relaxation, which comprises a data input and preprocessing module, a constraint multi-objective modeling module, a three-population collaborative evolution optimization module and a scheme output and application module. The data input and preprocessing module is used for extracting multi-source data required by a loading task from an airline operation database, arranging and converting the data into a system readable format, loading the data into a system cache area and completing data preparation of a model. The constraint multi-objective modeling module is used for constructing a constraint multi-objective optimization model of the civil aviation logistics intelligent loading based on the data output by the data input and preprocessing module. The three-population collaborative evolution optimization module adopts a three-population collaborative evolution algorithm ThcAp as a core solver. The application has more flexible and intelligent constraint processing, stronger multi-objective optimization capability, higher algorithm robustness, better scene adaptability, significantly improved loading efficiency and comprehensive benefits.

The application discloses a kind of cognitive interference decision-making method and its application system based on deep reinforcement learning, it is suitable for our side interference unmanned aerial vehicle when networked radar system in complex electromagnetic environment under confrontation completes intelligent interference decision-making task, based on interference decision-making algorithm DAR-PPO, to improve the performance of existing interference decision-making system in sample efficiency and strategy stability, interference decision-making algorithm DAR-PPO includes two big innovation modules: one is KTD-Clip module, feedback signal is constructed based on experience KL divergence, clipping threshold in strategy update is dynamically adjusted, to control strategy update step, so that it is always in stable and effective trust domain, significantly improve the adaptability of algorithm to environmental change;Second is RA-IS module, by constructing trajectory playback pool and introducing clipping importance sampling, realize the effective reuse of historical interaction samples, while designing weight entropy regularization mechanism, inhibit the strategy fluctuation caused by abnormal weight, thereby significantly improve training sample utilization and enhance algorithm robustness.

This invention provides a sampling path planning method and system based on adaptive dynamic batch optimization. The method includes: when the edge processing queue (QE) is empty and a new batch needs to be created, calculating the batch size according to an adaptive batch size calculation formula. The system includes an adaptive batch manager, a search progress monitor, a solution quality evaluator, an environment complexity analyzer, and a batch size calculator. The adaptive batch manager is configured to: receive iteration information from the search progress monitor, receive cost improvement data from the solution quality evaluator, receive environment information from the environment complexity analyzer, and calculate the batch size parameter using the batch size calculator according to the adaptive batch size calculation formula. According to this invention, it is possible to significantly improve planning efficiency, enhance environmental adaptability, support real-time dynamic applications, meet the practical needs of multi-objective optimization adaptation, reduce computational resource consumption, and improve algorithm robustness when planning paths.

This invention discloses a method for semantic similarity calculation of multi-source heterogeneous data, belonging to the field of multi-source heterogeneous data processing and semantic analysis technology. It solves the problems of weak cross-type calculation capability, insufficient semantic feature extraction, imbalance between accuracy and efficiency, and poor compatibility of heterogeneous data in existing technologies. This method sequentially performs heterogeneous data preprocessing, dynamic type identification and strategy matching, heterogeneous semantic feature extraction, feature optimization and fusion, unified semantic space construction, multi-dimensional semantic similarity calculation and result optimization. It adopts a bilinear mapping model to construct a unified semantic space adapted to multiple types of data, and combines a multi-dimensional weighted fusion mechanism to complete the similarity calculation. This invention is compatible with multiple types of heterogeneous data, balances calculation accuracy and computational efficiency, improves algorithm robustness and scenario adaptability, and is suitable for scenarios such as data fusion, intelligent retrieval, and knowledge graph construction.

The invention discloses a distributed green production distribution integrated scheduling system and method, and relates to the technical field of distributed production scheduling in the manufacturing industry. Comprising a workpiece sequence initialization module for generating an initial workpiece sequence, an optimization module for optimizing the performance of a solution, a decision module for optimizing the learning and decision process of a hyper-heuristic algorithm, and an energy-saving module for reducing the processing energy consumption while reducing the total completion time, a high-quality initial solution is generated through the workpiece sequence initialization module, and invalid search time consumption is reduced; the optimization module pertinently improves the performance of the solution, effectively reduces the problem complexity, and guarantees the calculation efficiency; the decision-making module optimizes the learning and decision-making process of the hyper-heuristic algorithm, dynamically adapts to the change of the production environment, avoids scheme failure caused by fixed parameters, reduces dependence on manual parameter adjustment, and improves the robustness of the algorithm; and the energy-saving module reduces the processing energy consumption while shortening the total completion time, so that the balance of efficiency and green is realized.

The invention provides an improved taboo search topological optimization method for an offshore wind plant current collection system, which comprises the following steps of: performing intelligent preliminary grouping and sorting on the basis of fan positions to form an initial connection scheme, connecting all fans one by one at the minimum cost in each group, calculating the cost of grouped cables, and checking whether lines are crossed or not; if crossing is carried out, punishment is applied to the total cost, the scheme is ensured to be feasible, the current scheme is compared with the historical optimal scheme, records are updated, fan groups are intelligently exchanged to explore a new scheme, the optimization process is repeated until the number of iterations is met, and finally the global optimal topology scheme is output. The engineering practicability is higher, the algorithm robustness is better, the scheme reliability is better, the design automation degree is high, and the auxiliary decision-making capability is high.

The invention relates to the technical field of micro-grid optimization, and particularly discloses a micro-grid optimization scheduling method and system based on an improved electric eel foraging optimization algorithm, and the method comprises the steps: constructing a target function with the minimum total operation cost of a micro-grid as a target, and building constraint conditions which need to be satisfied in the operation process of the micro-grid; based on an improved electric eel foraging optimization algorithm, solving a micro-grid optimization scheduling model formed by the objective function and the constraint conditions, and generating an optimal scheduling scheme; according to the algorithm, an optimization mechanism is deeply reconstructed: in an initialization stage, an elite reverse learning strategy is adopted to mine a potential high-quality solution, and population diversity is remarkably enhanced; in the migration and hunting stage of core evolution, a Cauchy mutation operator is introduced to be combined with a champion selection mechanism, and premature convergence is effectively avoided while the robustness of the algorithm is improved; in addition, a crisscross strategy is fused to dynamically coordinate algorithm behaviors, so that precise balance between local search precision and global development capability is realized.