122 results about "Combinatorial optimization" patented technology

Provided is a quantum computing method for obtaining an optimal solution of a problem with multiple discrete variables, wherein the problem is represented by a cost function, the method comprising:—generating a graph structure from the cost function,—dividing the graph structure into at least two disjunct subgraph structures, wherein each subgraph structure comprises a subset of the multiple variables,—mapping each subgraph structure to a local cost function represented as local cost Hamiltonian,—determining, for each local cost Hamiltonian, all eigenstates corresponding to an energy below a predetermined cut off energy using a quantum processing device, wherein each variable of the subset of multiple variables is represented by a qubit of the quantum processing device,—recombining the determined eigenstates, and-approximating a ground state from the recombined eigenstates, wherein the ground state represents the optimal solution.

The invention relates to the technical field of power system auxiliary services, in particular to a multi-agent participated user group dynamic combination and economic optimization method in power auxiliary services, which comprises the following steps of: constructing a multi-agent collaborative model of'upper-layer scheduling-middle-layer aggregation-lower-layer execution ', formulating an incentive price through a Stackelberg game between upper-layer ISO (International Standard Organization) and LAs (Local Assisted Services), and establishing a multi-agent collaborative model of'upper-layer scheduling-middle-layer aggregation-lower-layer execution'; the evolutionary game between the lower-layer LAs and the user realizes user strategy interaction; introducing maximum profit power reduction and power transfer potential indexes, and performing user group preliminary screening in combination with a multi-energy profit map; and outputting an optimal user group combination scheme through iterative solution and joint uncertainty analysis. The invention further provides an alternative scheme, and dynamic combination optimization is realized by utilizing the user potential portrait and a reinforcement learning mechanism. The method can effectively improve the economy, flexibility and robustness of the power auxiliary service, and gives consideration to the comfort of the user and the adjustment demands of the system.

This application relates to a real-time power load forecasting method and system based on a hybrid model. The method includes: acquiring current cycle load data and combining it with historical load data to form a load sequence, and acquiring corresponding weather data; performing timestamp alignment, anomaly processing, and normalization on the load sequence and weather data to obtain a standardized input sequence; determining the order parameters of the linear forecasting model and the set of hyperparameters to be optimized, consisting of the network structure and training parameters of the nonlinear forecasting model, and optimizing them through a combined optimization algorithm to update the training configuration of the two models; outputting the first and second forecast sequences for the next cycle in the current cycle, respectively, and using the second forecast sequence as a trend term to compensate the first forecast sequence to obtain a fused forecast sequence; acquiring the fused forecast sequence for the current cycle from the previous cycle, constructing a residual sequence with the current cycle load data and determining the deviation term, calibrating the current cycle fused forecast sequence online, and outputting the calibrated load forecast result.

The invention relates to the field of quantum calculation and combinatorial optimization solution, and provides a measurement resource adaptive allocation and risk control recursive dimension reduction method and system based on a parameterized quantum circuit, and a medium. The classical controller issues a circuit control and read-out instruction to the quantum processing unit, executes calculation base projection read-out sampling, counts and estimates diagonal Pauli observable measurement and a tensor product term thereof, and generates point fixation and edge contraction candidate actions. Constructing a lower confidence boundary based on observation uncertainty and dynamically distributing sampling times; forming classical confidence in combination with the flipping cost, performing weighted fusion on the classical confidence and the quantum confidence according to uncertainty to obtain a score, and calculating the risk; selecting low-risk actions in batches under conflict constraints, performing elimination, updating coupling and an external field, and executing amplitude cutting, re-calibration and rarefaction; and iterating to a scale solvable solution and backfilling an output solution.

The invention provides an asset combination optimization method which can be applied to the field of artificial intelligence. The asset combinatorial optimization method comprises the steps of obtaining a to-be-processed asset set, wherein each asset corresponds to a group of structured attribute data; receiving an optimization target and a business rule constraint defined by a user; constructing a mixed integer linear programming model according to the attribute data, the optimization target and the business rule constraint; wherein the optimization target is converted into a target optimization function of the model, and the business rule constraint is used as a linear constraint condition of the model; selecting a target engine from at least two mutually independent and pluggable optimization solution engines, transmitting the mixed integer linear programming model to the target engine for solution operation, and obtaining a final assignment result of the binary decision variable; and based on the final assignment result, screening out asset subsets conforming to business rule constraints, and forming an optimized asset combination. The invention further provides an asset combinatorial optimization device and equipment, a storage medium and a program product.

The invention relates to the technical field of discrete manufacturing workshops, in particular to a discrete manufacturing unmanned intelligent workshop AGV number optimization method and a related device. The method comprises the following steps: constructing a mathematical combination optimization model for the discrete manufacturing unmanned intelligent workshop according to a preset constraint condition and a preset objective function; the process information of the discrete manufacturing unmanned intelligent workshop is determined, the process information comprises a plurality of processes, and each process corresponds to single demand information; and solving the demand information corresponding to each process based on the mathematical combination optimization model to obtain the number of AGVs required in each process. According to the invention, the number of AGVs in the discrete manufacturing unmanned intelligent workshop can be optimized, and the operation cost is reduced.

The invention provides the system, the device and the method for determining the optimal bidding strategy of which the performance is better than that of the bidding adjustment mode in a scene of repeatedly participating in the first-level price auction. The invention provides a method for predicting inventory. According to the method, an online real-time repeated auction scene can be converted into an offline optimization problem; specifically, auction is summarized through a small number of parameters, and joint distribution of the parameters is predicted based on historical data. Meanwhile, the invention further develops a method named as strategy search, and the method adopts a combined optimization means to search a bidding strategy capable of realizing the optimal performance of the advertisement activity on the premise of meeting specific constraint conditions.

The present application relates to the technical field of intelligent manufacturing production scheduling and combination optimization, in particular to a distributed flexible job shop scheduling method considering process dependency, comprising: initializing algorithm parameters, alternately using a heuristic method and a random method to generate an initial population; selecting a parent solution from the current population through an adaptive adjustment strategy, sequentially executing a crossover operator, a mutation operator and a local search on the parent solution, updating the population; calculating the current optimal solution of the current population, judging whether the termination time is reached, if yes, terminating evolution, outputting the current optimal solution and the maximum completion time, otherwise, continuing iteration. The present application solves the problems of existing solving methods, such as significant time consumption increase and search into local optimum when the scale of the distributed flexible job shop scheduling problem considering process dependency is expanded, and achieves the positive effects of reducing the maximum completion time and improving the search efficiency and solution quality of large-scale instances.

The application discloses a processing method and device for a combination optimization problem. The method comprises the following steps: running a segmented quantum circuit obtained by splitting an original quantum circuit, and performing error suppression on the measurement result of each segmented quantum circuit to obtain a first measurement result corresponding to each segmented quantum circuit after error suppression; recombining the obtained first measurement result to generate a second measurement result; obtaining an average energy expectation of the original quantum circuit based on the generated second measurement result; and when the execution of the segmented quantum circuit satisfies a specified condition, generating a target combination corresponding to the target combination optimization problem based on the average energy expectation. By using the embodiment of the application, the operation of a combination optimization algorithm with a large number of bits on quantum hardware is realized through the operation and recombination of the split quantum circuit.

The invention discloses a modularized precise measurement and control system and method for carbon dioxide capture and reutilization, and relates to the technical field of carbon dioxide capture and reutilization process control, and the system comprises a collection and judgment module which collects original data for preprocessing, calculates the absorption efficiency for efficiency state judgment, and generates a judgment result; the calculation and adjustment module is used for calculating the total flue gas flow and the error based on the judgment result, obtaining the rotating speed frequency value of the variable-frequency fan as an input instruction for preliminary adjustment by combining a proportional-integral control form formula, obtaining an adjustment result and freezing the current parameter combination; the optimization feedback module is used for optimizing and updating the current parameter combination by using a hybrid optimization algorithm based on the adjustment result to obtain an optimal parameter combination, feeding back the optimal parameter combination to a proportional-integral control form formula, generating a new input instruction and a new adjustment result, and then executing corresponding measures; according to the invention, accurate measurement and control and energy efficiency optimization of the trapping and recycling process are realized.

A multi-task, multi-radar resource scheduling method for space object detection is proposed. This method constructs an allocation relationship model between detection tasks and detection equipment, establishing an objective function that optimizes the overall task priority and overall equipment time utilization. A genetic algorithm is used for combinatorial optimization to find an optimal equipment allocation scheme for all search tasks. Based on the search task planning results based on the genetic algorithm, the system is updated and tasks are transformed. The remaining tracking tasks and tasks transformed from search to tracking are then processed. A joint algorithm combining deep reinforcement learning and multi-objective evolution is constructed to achieve multi-objective optimization and resource scheduling. This invention uses a two-layer planning strategy to allocate tasks of different natures to two independent planning levels, thereby reducing problem complexity and matching the most suitable optimization algorithm for each type of task.

The invention discloses an ultra-parallel microwave photon Isin machine system based on wavelength division multiplexing and an optimization method, and belongs to the technical field of photon calculation and combination optimization. The Isin machine system comprises a multi-wavelength light source module, a microwave modulation and wavelength division multiplexing coding module, a nonlinear calculation and evolution module and a demultiplexing and dynamic feedback module, all the modules are optically and electrically connected to form a closed-loop system, and different optimization problems can be coded to independent wavelength channels to achieve parallel solving. The optimization method is applied to the Isin machine system, and closed-loop optimization is completed through the steps of coupling matrix mapping, microwave modulation loading, nonlinear evolution, dynamic feedback control and the like. The method supports synchronous processing of 1024 optimization problems, and energy consumption of a single problem is lt; the method is mainly applied to the fields of logistics scheduling, financial modeling, communication network optimization and the like, and an efficient physical calculation solution is provided for a large-scale combination optimization problem.

A large language model-driven unit combination optimization method, relating to the field of power system technology, includes: acquiring basic economic operation data of the power system; constructing and solving a unit combination model with integer relaxed security constraints based on the basic economic operation data; obtaining and solving the unit combination model; constructing variable dimensionality reduction algorithms, constraint dimensionality reduction algorithms, and infeasibility solution repair algorithms; using a large language model to perform single-algorithm evolution on the variable and constraint dimensionality reduction algorithms; using a large language model to perform single-algorithm evolution on the infeasibility solution repair algorithm; solving and calculating the unit combination model with integer relaxed security constraints; calculating the variable constraint dimensionality reduction security constraint unit combination model and obtaining the optimal solution; and using the optimal infeasibility solution repair algorithm to repair the optimal solution of the variable constraint dimensionality reduction security constraint unit combination model, obtaining the result. This method addresses the shortcomings of existing model dimensionality reduction methods in terms of generality, stability, and interpretability.

The invention belongs to the technical field of communication protocol acquisition response, and discloses a method and a system for improving communication protocol acquisition response time, and the method comprises the following steps: obtaining basic information of all acquisition variables, generating an imported variable list according to the basic information, and sorting the imported variable list to obtain a sorted variable linear sequence; grouping the sorted variable linear sequences to obtain grouped variable sequences, and adaptively constructing a reading instruction set according to the grouped variable sequences; and performing communication protocol optimal request construction on the reading instruction set by a capacity constraint-based combinatorial optimization mechanism to obtain an optimal request group set, and improving the communication protocol acquisition response speed according to the optimal request group set. According to the method, the number of reading instructions of a communication protocol is remarkably reduced, the size of a communication protocol packet and the communication round-trip time are reduced, and therefore the data acquisition response speed of the programmable logic controller is effectively increased.

To provide an analysis apparatus an analysis method, and an analysis program each adapted to extract information about the molecular structures from the molecular vibration spectra. An analysis device includes an acquisition unit configured to analyze, by applying a combinatorial optimization technique using the Ising model, a molecular vibration spectrum of each of a plurality of organic molecules including a plurality of first organic molecules and second organic molecule serving as a reference for analyzing a molecule structure of each of the plurality of the first organic molecules, and to acquire information about a molecular structure of each of the plurality of the first organic molecules.

The invention discloses a vehicle path planning problem optimization method and system for landscape smoothing, and belongs to the technical field of combinatorial optimization algorithms, and the method comprises the steps: randomly generating an initial population for a vehicle path planning problem, constructing a toy problem, and carrying out the optimization of the vehicle path planning problem; the toy problem and the vehicle path planning problem are equal in scale, and the fitness landscape of the solution space has a single-peak characteristic; constructing a fitness function, and evaluating each individual in the population through the fitness function to obtain a fitness value of each individual; executing operation of a selection operator, a crossover operator and a mutation operator to generate a next generation population; and the fitness function design and evaluation and genetic operator operation are repeatedly executed until a preset termination condition is met, and an individual with the minimum original problem objective function value in the evolution process is output to serve as an optimal solution of the vehicle path planning problem. According to the method, local optimum can be jumped out, global exploration and local development are balanced, the solving quality and stability can be improved, and the method has applicability in vehicle path planning.

The invention relates to the technical field of quantum calculation and combinatorial optimization solution, and discloses a parameter migration gating subgraph growth quantum approximate optimization method and system and a medium. For a quantum bit limited scene, the method comprises the following steps: S1, executing migration loss guide block division by a classical controller, and generating sub-blocks and a halo set which meet hardware scale constraints; s2, constructing a sub-problem and selecting a migration source, constructing an effective sub-problem and matching migration source parameters from a parameter library; s3, calculating a migration risk upper bound and budget allocation, calculating a migration risk upper bound of a line which does not need to be operated, and allocating a resource budget according to the migration risk upper bound; s4, performing gating judgment and execution, dynamically selecting a parameter multiplexing, limited optimization or structural mitigation strategy according to the risk level, and controlling QPU sampling; and S5, writing back and updating the solution, integrating the block solution and updating the boundary condition. Through a risk gating mechanism, while the problem of large-scale graph optimization is solved, the quantum circuit execution times and the measurement overhead are remarkably reduced.

This improves the accuracy and speed of solving large-scale combinatorial optimization problems by dividing them into subproblems. [Solution] The information processing device 100 for processing combinatorial optimization problems includes: a graph creation unit 112 that creates one or more subgraphs from a main graph; a mathematical optimization unit 115 that solves the combinatorial optimization problem for each subgraph using a mathematical optimization solver; a machine learning unit 117 that trains each subGNN so that the output of the subGNN corresponding to each subgraph is close to the solution of the mathematical optimization solver; a feature vector assignment unit 118 that assigns the feature vectors at each vertex of the subGNN obtained as a result of training to the corresponding vertices of the main GNN as input to the feature vectors of the main GNN corresponding to the graph data of the main graph; and a solution output unit 119 that outputs the solution obtained as a result of training the main GNN by setting a loss function so that the machine learning unit 117 solves the combinatorial optimization problem for the main graph.

The application discloses an adaptive unit combination optimization method and system based on extreme scenario driving, constructs an uncertainty set in robust optimization; determines an overall model of adaptive robust optimization unit combination according to the constructed uncertainty set; converts the overall model of adaptive robust optimization unit combination into a random programming model with vertices and extreme scenarios, adds a full-scenario feasibility constraint and an unexpected constraint into the random programming model, and realizes adaptive unit combination optimization based on extreme scenario driving. The method solves the defects that the conventional uncertainty set robust optimization and the random programming do not satisfy the unexpectedness and full-scenario feasibility, and the scheduling instruction obtained by the model can be used for high-proportion new energy power market unit combination optimization decision.

The application discloses a power spot market minimum power purchase cost function fitting method suitable for quantum calculation, belongs to the cross field of power system optimization and quantum calculation, and comprises the following steps: constructing a security constrained unit commitment optimization model based on power system parameters; constructing a training sample set of unit start state and minimum power purchase cost based on the security constrained unit commitment optimization model; fitting an explicit function relationship between the minimum power purchase cost and the unit start state by adopting a kernel function enhanced elastic net regression algorithm based on the training sample set; converting the explicit function relationship into a quadratic unconstrained binary optimization model suitable for quantum calculation, and solving the quadratic unconstrained binary optimization model, so that the power spot market minimum power purchase cost function fitting suitable for quantum calculation is realized. The application solves the calculation efficiency bottleneck of the existing method in the power spot market clearing and the difficulty in explicit expression of a target function in quantum calculation adaptation.

This application provides a knowledge distillation-based method for optimizing unmanned delivery vehicle routes, relating to the fields of combinatorial optimization and intelligent logistics scheduling. The method includes: constructing a problem instance with capacity constraints and safety preferences, and a comprehensive objective function; inputting node features into a machine learning model, constructing path solutions through autoregression and saving intermediate solutions, iteratively outputting candidate solutions; inputting candidate solutions into an adaptive large neighborhood search module, and outputting high-quality solutions and generating a new dataset after destruction repair, acceptance criterion judgment, and weight updates; extracting the action sequence and probability distribution of the teacher model as supervision information; constructing a lightweight student model and using a shared matrix to predict node selection; constructing a distillation loss and jointly training to obtain the student model; and obtaining the final model through secondary training on the new dataset. This application reduces model complexity and inference overhead, improving path solution quality and computational efficiency in small-sample scenarios.

The invention discloses a resource scheduling system and method based on intelligent algorithm combinatorial optimization, and belongs to the technical field of machine learning and resource scheduling, and the system comprises a task load prediction module used for predicting the resource demand condition of a future task based on historical task data and a current system state, and a genetic optimization module used for optimizing the resource demand condition of the future task. And the candidate resource allocation module is used for generating and globally optimizing a candidate resource allocation scheme on the basis of a prediction result of the task load prediction module. Through fusion of LSTM prediction, GA global search and MCTS local optimization, the perspectiveness, global optimality and environmental adaptability of resource scheduling are remarkably improved, the problems that in the prior art, response lags behind, local optimality is likely to be caught in and generalization ability is insufficient are effectively solved, more efficient and stable dynamic resource allocation is achieved, and meanwhile, the resource scheduling efficiency is improved. According to the system, the resource utilization rate and the automation level are improved, the calculation and operation and maintenance cost is reduced, energy consumption is reduced, and good economic and social benefits are achieved.

Methods and systems for performing a combinatorial optimization task are provided. The method includes: receiving a first set of data items and discretizing each of the first set of data items in order to generate a first discretized set of data items; receiving a second set of data items and discretizing each of the second set of data items in order to generate a second discretized set of data items; reorganizing the first and second discretized sets of data items into two respective groups of positive integers; using the two groups of positive integers to generate two respective tables for storing a feasibility of obtaining at least one subset sum from among the elements of the first and second discretized sets of data items; and performing a subset sum matching procedure upon the two tables in order to identify the at least one subset sum.

The invention discloses a ballastless track structure type optimization method for a foundation large-deformation section in a tunnel, and relates to the field of railway track engineering, comprising the following steps: determining a foundation deformation load and train load mode borne by a ballastless track structure; constructing a multi-level evaluation system of the ballastless track structure; function evaluation objects are divided, and all feasible ballastless track structure scheme sets are generated through a combinatorial optimization algorithm; evaluating and sorting each function evaluation object in each scheme to obtain a standardized numerical value of each scheme under each secondary evaluation index; determining the weight of each first-level index and each second-level index in the evaluation system in combination with an analytic hierarchy process; and obtaining the comprehensive adaptation degree score of each scheme through the comprehensive adaptation degree calculation model, and selecting the scheme with the highest score as the optimal ballastless track structure type. According to the method, the comprehensiveness and the accuracy of ballastless track structure type selection are improved through a system generation scheme, refined quantitative evaluation and a normalized decision process.

The invention relates to the technical field of quantum computing and combinatorial optimization, and discloses an iterative bias field hierarchy QAOA solving method and device based on a quantum processor and a medium. In order to solve the problem that a parameterized quantum circuit is difficult to directly solve due to the fact that the graph scale is far larger than the number of available quantum bits, the method comprises the steps that S1, a core block set, a halo extension set and node affiliation mapping are obtained, and bias field parameters are initialized; s2, executing an inner layer quantum step, and calculating a first statistical magnitude and a second statistical magnitude by executing a quantum circuit on the QPU; s3, executing an outer layer interaction step, constructing an outer layer block diagram based on a cross-block edge, and obtaining outer layer soft variable estimation and outer layer soft orientation; s4, executing bias field recharge updating, calculating a next round of inner layer bias field candidate value, and updating the next round of inner layer bias field candidate value; and S5, judging a termination condition, and carrying out loop iteration or outputting a discrete solution. Through hierarchical iteration and bias field recharge, information loss caused by blocking is effectively reduced, and the solution stability on limited quantum hardware is improved.

The invention provides an interpretable power system blocking risk perception method based on topology robust features, and the method comprises the steps: constructing a feature variable alternative set based on a power grid operation mechanism and blocking event features, and carrying out the screening through employing an information-assisted feature combination optimization method, and obtaining a topology robust feature set; constructing an interpretable prediction model based on the topological robust feature set; obtaining a historical blocking event case, constructing a data set covering a plurality of topology scenes and operation conditions, and training the interpretable prediction model through the data set; and obtaining to-be-tested data, and performing risk perception on the to-be-tested data through the trained interpretable prediction model to obtain a prediction result. According to the method and the device, various topology scenes can be adapted without notification of topology change in advance, and the problem of poor applicability in the prior art is solved.

A method for managing order processing includes obtaining a set of time series datasets associated with the order processing for an order processing system, generating a state space for the set of time series datasets using bi-partite graph representations for a mixed integer program (MIP), applying a transition function on the state space to obtain state-to-state transition function, performing a graph embedding on the state-to-state transition function to obtain historical transition data, applying a transformer multi-head attention function on the historical transition data to obtain positional encodings of observation history and feature representation, and perform an agent deployment on an order processing system based on finalized state-to-state transition functions, wherein the finalized state-to-state transition functions are based on the positional encodings.

This invention discloses a method for constructing a quantum Ising model for safety-constrained unit combinatorial optimization problems, relating to the field of quantum computing. The method includes: constructing a safety-constrained unit combinatorial optimization model and obtaining parameters for a mixed-integer programming problem; using the Benders decomposition method to decompose the mixed-integer programming problem into a main problem and subproblems; substituting the optimal binary solution into the subproblems to obtain new cutting planes and expanding the set of cutting planes; constructing a compact high-dimensional quadratic function to fit the set of cutting planes; solving a semi-definite programming problem to obtain the parameters of the high-dimensional quadratic function; transforming the quadratic unconstrained binary optimization model constructed based on the high-dimensional quadratic function into an Ising model; solving the Ising model to obtain the qubit states and obtaining the optimal binary solution; and substituting the optimal binary solution into the above steps for iterative solving. This invention solves the problem of huge qubit resource consumption in existing technologies, especially in dealing with NP-hard problems with complex constraints and many variables.

The application is suitable for the technical field of parameter optimization, and provides an optimization method for maximizing power density of a proton exchange membrane fuel cell, comprising the following steps: optimization variable and optimization target selection; adopting an AdaBoost integrated neural network model as a proxy model for calculating fitness function values in an iteration process of a swarm intelligence optimization algorithm; selecting an improved grey wolf optimizer (IGWO) as an optimization tool for iteration optimization in a value range of the optimization variable, with the maximum power density as the target, to obtain an optimal optimization variable combination; and optimization result verification.The application proposes an optimization method for the power density of the proton exchange membrane fuel cell by coupling machine learning and a swarm intelligence algorithm, has a fast convergence speed, can obtain an optimal solution or an approximate optimal solution under a small number of iteration times, effectively reduces the number of target function calls, significantly reduces the calculation time and the calculation cost, and has a strong global search capability and can avoid falling into a local optimum.

The present disclosure relates to systems and methods for obtaining a problem specification file descriptive of a combinatorial optimization problem; identifying a plurality of combinable vector-matrix operations of the combinatorial optimization problem; generating an instruction set for the one or more processors, wherein the instruction set includes a first instruction that, when implemented, causes the one or more processors to perform a combined matrix-matrix operation replacing the plurality of combinable vector-matrix operations; and executing the instruction set.