88 results about "Combinatorial optimization problem" patented technology

The invention belongs to the technical field of image classification, particularly relates to an automatic optimization method for image classification migration based on evolutionary computation, and is particularly suitable for automatically searching and optimizing a migration strategy through an evolutionary algorithm in a scene with large data distribution difference between an original domain and a target domain so as to improve the accuracy and generalization ability of target domain image classification. According to the method, fine tuning strategy search in an image classification task is modeled as a combinatorial optimization problem under structural constraints, and a customized genetic algorithm of structural perception and a lightweight performance prediction mechanism are creatively fused; the optimal fine tuning configuration adaptive to the target image classification task is efficiently and automatically searched at low cost, and the classification accuracy and generalization ability of the model in small sample, cross-domain and resource limited scenes are remarkably improved.

The invention discloses a carbon flow optimization method and device for edge calculation under vehicle-road cooperation and a medium, and relates to the technical field of traffic carbon emission. The method comprises the steps that a real-time state parameter sequence and environment information of a vehicle are acquired, and at least two candidate driving strategies are generated through a built-in carbon emission digital twinborn model; according to the candidate driving strategies, the corresponding expected carbon emission amount is calculated by inquiring the emission factor mapping table of the specific power of the vehicle and integrating; packaging the candidate driving strategy and the corresponding expected carbon emission into a bidding packet, and sending the bidding packet to an edge computing node on the roadside; based on the received bidding package, carbon right reverse auction is initiated, a combined optimization problem is solved by adopting a heuristic search algorithm, and a bid winning strategy set is determined; and generating a cooperative driving strategy according to the bid winning strategy set, and issuing the cooperative driving strategy to the corresponding vehicle end node. By means of the method, information distortion is restrained, and direct and real-time optimization control over regional total carbon emission is achieved.

The invention belongs to the technical field of energy consumption optimization of reconfigurable robots, and particularly relates to a deterministic optimization method and system for variable-structure energy consumption of a modular robot. The deterministic optimization method for the variable structure energy consumption of the modular robot comprises the following steps: determining an initial structure before the variable structure of the modular robot starts and a target structure after the variable structure of the modular robot ends; in the structure changing process of the modular robot, a motion replacement mechanism is used, so that the module can penetrate through the interior of the current structure to move; according to the method, the problem of minimum moving steps of all modules in the modular robot allosteric process is converted into the problem of combinatorial optimization of task allocation, a global optimal allosteric solution is obtained through configuration sliding and Hungary algorithm solving, and allosteric energy consumption optimization is achieved. According to the method, a motion substitution mechanism is provided, the globally optimal solution is rapidly and efficiently calculated through configuration sliding and the Hungary algorithm, the limitation that solving of a probability search method has randomness is overcome, and the certainty of the solution can be guaranteed.

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 a method and device for determining a machining execution scheme of a multifunctional machine tool, and the method comprises the steps: determining the type of a tool required by each working procedure and a task relation table between each working procedure and the tool, and generating a plurality of groups of machining execution schemes based on a genetic algorithm; calculating the total switching time of the scheme through a total switching time calculation model and a task relation table, converting the total switching time into fitness, and carrying out collaborative optimization on a process machining sequence and a tool installation sequence of each machining execution scheme based on the fitness to obtain a target machining execution scheme corresponding to a global optimal individual of the genetic algorithm; by constructing a total switching time calculation model, uniformly considering two mutually coupled decision variables and applying a genetic algorithm to provide a thought and a tool for solving a combinatorial optimization problem, collaborative optimization is performed on the model, the tool replacement frequency caused by process switching and unnecessary movement of a tool replacement path in a tool magazine can be systematically reduced, and the tool replacement efficiency is improved. And the total cutter switching time in the machining process is reduced to the minimum.

To efficiently search a solution in a wide search space.SOLUTION: The processing unit 12 determines a plurality of upper solutions from a plurality of solutions of the combinatorial optimization problem stored in the storage unit 11 based on an evaluation function value of each of the plurality of solutions, determines, for each of the plurality of upper solutions, a selection probability such that an upper solution having an evaluation function value closer to a highest solution among the plurality of upper solutions has a higher probability of being selected, and selects a first solution from the plurality of upper solutions according to the selection probability. A second solution different from the first solution is selected from the plurality of solutions, a third solution is generated by a path recombination method using the selected first and second solutions, and the solution search of the combination optimization problem is executed with the third solution as an initial solution.SELECTED DRAWING: Figure 1

This invention relates to an optical Ising calculation method and system based on dual digital micromirror devices (DMDs). First, the combinatorial optimization problem is mapped to an Ising model. Multiple mask vectors are constructed, and each mask vector is decomposed into multiple binary bit planes. A spin configuration matrix is ​​constructed and loaded into DMD-A. For each mask vector, its binary bit planes are sequentially loaded into DMD-B. The light intensity modulated by the two DMDs is collected and processed. The optical inner product is obtained by combining the bit-weighted components, and then the energy contribution value of the mask vector is calculated. The total Hamiltonian is obtained by summing all contributions. The energy difference is calculated by randomly flipping the spins and repeating the above steps. The probability of accepting the current flip is calculated based on the energy difference. This process is iterated until the maximum number of iterations is reached to obtain the final solution. The system is used to implement the above method. Compared with the prior art, this invention can achieve a high-speed, stable, and coherently independent optical Ising solution method.

The application discloses an anti-characteristic-unstable Tor App traffic analysis method and system, first extracts basic characteristics and bidirectional statistics of traffic generated by a mobile phone App running on Tor as a candidate characteristic set, then selects a subset from the candidate characteristic set as the representation of the traffic, filters out the traffic of N specific application types by solving an application type combination optimization problem, selects N characteristic subsets from the candidate characteristic set, and iteratively selects a machine learning model and a characteristic set suitable for the specific application type by solving an optimal combination problem, so that the Tor App can be accurately identified. The application solves the problem of characteristic instability in Tor App traffic analysis caused by the instability of the mobile environment, the diversity of app work content and the increasing number of new apps running on Tor.

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 application claims a service-aware resource collaborative allocation method, belonging to the field of mobile edge computing resource allocation, which comprises: analyzing task demand characteristics, obtaining task abstract categories combined with task demand characteristics, and mapping them into corresponding demand types. Based on the task demand type, candidate collaborative node clusters are selected through destruction operators and repair operators, then the task and resource matching problem is modeled as a combinatorial optimization problem, the collaborative benefits of candidate node combinations are calculated, and the edge node combination with the highest collaborative benefit is selected. The application mainly considers the problems of task demand fuzziness and task and resource matching imbalance in the computing power network, designs a task demand type identification algorithm and a resource collaborative allocation strategy, selects a reasonable collaborative node cluster, and reduces the time delay while improving the resource utilization.

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.

An information processing device judges, with respect to at least any one of multiple quantum bits in a quantum circuit, whether a corresponding first-order term is present in a cost operator. When the first-order term corresponding to any one of the plurality of quantum bits is not present the information processing device updates at least any one of the cost unitary operators so that the first-order term corresponding to the any one of the quantum bits and to which a new first variational parameter is assigned is included in the exponent part. The information processing device solves a combinatorial optimization problem based on multiple mixer unitary operators and the multiple cost unitary operators after updating at least the any one of the cost unitary operators.

A combinatorial optimization problem processing apparatus includes an annealing unit that acquires, using an annealing scheme, a candidate for a first solution that is a variable q satisfying a first condition regarding a partition function whose Hamiltonian is a function representing a combination optimization problem that is an analysis target and the function includes third- or higher-order terms of a variable q, the Hamiltonian being represented by an integral representation using a variable X, the Hamiltonian being represented by a sum of a first equation that includes only second- or lower-order terms of the variable q and a second equation that does not include the variable q, in an alternate optimization algorithm in which first optimization processing for acquiring the first solution and second optimization processing for acquiring a second solution that is a variable X satisfying a second condition regarding the partition function are repeated until an end condition is satisfied, a calculation unit that acquires the second solution, and a solution candidate acquisition unit that acquires a candidate for the immediately preceding first solution when the end condition is satisfied, as a candidate for a solution of the combinatorial optimization problem.

A computer-readable recording medium stores therein an information processing program for causing a computer to execute a process, the process including: calculating a first solution of a combinatorial optimization problem based on an Ising model corresponding to the combinatorial optimization problem; determining a value of a parameter of a quantum approximation optimization algorithm corresponding to the combinatorial optimization problem so as to maximize a probability that a quantum state of a quantum circuit of the quantum approximation optimization algorithm becomes the calculated first solution; and calculating a second solution of the combinatorial optimization problem based on the quantum circuit of the quantum approximation optimization algorithm in which the determined value of the parameter is set.

The application discloses an airport crowd route guidance scheme optimization method and device and a storage medium, and the method comprises the following steps: acquiring layout information of an airport, and abstracting an airport scene into a complete graph according to the layout information; screening edges in the complete graph according to a preset screening condition, so as to delete edges that do not meet the condition; abstracting an airport crowd route guidance scheme optimization problem into a combination optimization problem according to the screened complete graph; and using a discrete differential evolution algorithm to optimize the combination optimization problem, so as to obtain a final optimization result. The application performs mathematical modeling on an airport departure layer scene, and automatically generates and optimizes an airport crowd route guidance scheme by using a discrete differential evolution algorithm, so that a high-quality solution can be obtained, and the guidance of the crowd route in the airport can be effectively assisted. The application can be widely applied to the fields of intelligent transportation and evolutionary calculation.

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.

To improve a system by detecting accuracy deterioration of inference in the system for executing inference of a combination optimization problem using a learned model.SOLUTION: An information processing apparatus includes an acquisition unit configured to acquire input data including a condition in a combinatorial optimization problem, an analysis unit configured to acquire a determination index for evaluating an optimization result based on the optimization result derived by inputting the acquired input data to a learned model that has executed machine learning for deriving the optimization result satisfying the condition based on learning data, and an evaluation unit configured to evaluate the optimization result derived by the learned model based on the acquired determination index and a set determination criterion of the determination index. And an updating unit configured to update at least one of the learned model and the determination criterion.SELECTED DRAWING: Figure 3

The invention provides a strategy recommendation method and electronic equipment, and relates to the technical field of computers. The method comprises the following steps: performing conversion processing on scene description information of a target scene to obtain a scene feature vector corresponding to the target scene; the scene feature vectors are input into a plurality of strategy evaluation models obtained through pre-training, each strategy evaluation model carries out strategy evaluation according to the scene feature vectors, a plurality of strategy evaluation results are obtained, each strategy evaluation model corresponds to one strategy, and the strategy evaluation results are obtained; the strategy evaluation result is used for representing a recommended value of a strategy corresponding to the strategy evaluation model in the target scene; and generating a target strategy set applied to the target scene according to the plurality of strategy evaluation results. According to the method, a complex strategy combination optimization problem is decoupled into a plurality of independent'scene-strategy 'tasks, and the tasks are processed in parallel based on a plurality of strategy evaluation models, so that high-quality, high-efficiency and high-credibility strategy generation and recommendation are realized under the condition of limited computing resources.

The embodiment of the invention discloses a quantum circuit generation method for solving a combinatorial optimization problem and a related device, and relates to the technical field of quantum computing. Covariance matrixes corresponding to multiple pieces of information to be combined are constructed, and a first quantum circuit can be generated according to the covariance matrixes and combination condition information; after a quantum logic gate included in the first quantum circuit acts on a group of quantum bits, the bit number of the quantum bits in an excited state is the same as the information number of the to-be-combined information indicated by the combination condition information; the bit number in the group of quantum bits is greater than or equal to the information number of the information to be combined indicated by the combination condition information; in this way, the combinatorial optimization problem can be solved by using the first quantum circuit, so that the solving speed for the combinatorial optimization problem is improved.

The invention relates to the technical field of robot control, and discloses a pick-and-place time sequence control method and system for a sky rail robot and multiple machining equipments.The pick-and-place time sequence control method for the sky rail robot and the multiple machining equipments.The pick-and-place time sequence control method for the sky rail robot and the multiple machining equipmentsincludes the steps that a machining equipment set and cache capacity parameters are read, and a service urgency index is calculated; operating state information is collected, the abnormal probability is calculated, and the processing equipment is divided into a fault isolation set and a serviceable set; global optimization is carried out on the service path of the sky rail robot, and an action sequence is generated and executed; performing isolation operation on the processing equipment in the fault isolation set, and smoothly incorporating the recovered processing equipment; the whole process of taking and placing operation is recorded, and the estimated machining completion moment is updated; according to the method, the service path of the sky rail robot is globally optimized through the ant colony algorithm, a high-quality action sequence is found in a complex combinatorial optimization problem, downstream accumulation is prevented through automatic adjustment when a fault occurs, and stable operation of a production system is guaranteed.

This invention provides an optimization device that can solve a given combinatorial optimization problem even if the objective function is a non-differentiable convex function. [Solution] An objective function f is a convex function that is not necessarily differentiable, and a selectable combination C of items X. i For a set of combinations F representing the set ⊆X, there exists a combination C i The probability is p i An optimization device that finds a vector x that represents the inclusion of f(x) and minimizes f(x) within a predetermined tolerance, comprising an input unit that inputs a zero-suppressed binary decision graph (ZDD) in which the objective function and the set of combinations F are represented by a zero-suppressed binary decision graph, and a tolerance ε, and in each iteration the solution x obtained in the previous iteration T From this, the next solution x T+1 The optimization unit finds the vector x such that the error from the optimal value of the objective function is less than or equal to the allowable error. T The solution is updated using the subgradient of the objective function f, and the updated solution is projected onto the feasible region of the objective function using ZDD to obtain the next solution.

The invention provides a false data injection attack method for a multi-elastic-joint robot system, and belongs to the technical field of multi-agent network attacks based on computer data processing. Firstly, a multi-elastic joint robot nonlinear dynamic mathematical model is constructed and converted into a high-order all-drive quasi-linear multi-robot system dynamic model based on an all-drive system theory; and then designing a distributed consistency controller containing a linearization item and a neighbor error related item, establishing closed-loop global dynamics and deducing a state analytical solution in combination with equivalent conversion of the model. Designing a false data injection attack strategy, and constructing an attacked system dynamic model; a closed-loop terminal state error in the presence or absence of attacks is taken as a target function, an attack optimization problem is converted into a combinatorial optimization problem, a greedy algorithm is designed by applying a sub-module optimization theory, and a suboptimal solution in polynomial time is obtained and is taken as an optimal scheme of false data injection attacks of the system. According to the method, the applicability of the complex nonlinear multi-agent system is improved, and the calculation complexity is reduced.

A storage unit stores evaluation function information of an evaluation function of a combinatorial optimization problem including a sum of an objective function and one or more constraint functions including a first constraint function weighted by a first coefficient (t) representing a weight of a first constraint condition. When repeatedly executing a local search for searching for a solution candidate (x*) of the combinatorial optimization problem using the evaluation function information while changing a value of t, a processing unit changes the value of t in a direction in which a value (V(x*)) of the first constraint function corresponding to x* obtained by the local search using each value of t approaches a target value (Vtarget) that is a positive value.

The first feature point in the first image and the second feature point in the second image are accurately mapped using a simple process. [Solution] The information processing device according to the embodiment includes a processing unit. The processing unit generates a matching problem, which is a combinatorial optimization problem, based on N first feature points detected from a first image and M second feature points detected from a second image different from the first image, to associate each of the N first feature points with one of the M second feature points that is presumed to represent the same object. Based on the solution to the matching problem, the processing unit generates a correspondence point list representing a correspondence point pair that includes one of the N first feature points and the corresponding second feature point from the M second feature points.

This invention discloses a high-precision RTK positioning method based on an improved adaptive genetic algorithm-based satellite selection strategy, belonging to the field of satellite navigation and positioning technology. A subset of satellites with a specified number of satellites is selected to minimize the loss of the Position Accuracy Factor (PDOP) and satellite geometric volume. The satellite selection problem is transformed into a combinatorial optimization problem. A fitness function is designed with the reciprocal of PDOP and geometric volume as its core, and adaptive crossover and mutation probabilities are introduced. The genetic operation parameters are dynamically adjusted according to the population evolutionary state to balance global search and local convergence capabilities. Iterative evolution is performed under the condition of satisfying subset size constraints. When the convergence condition is met, the subset of satellites with locally optimal PDOP values ​​is output for subsequent RTK ambiguity resolution and positioning. This invention can quickly converge to a near-globally optimal satellite geometry configuration in polynomial time while also considering satellite quality, significantly reducing computational complexity and improving the real-time performance and reliability of RTK positioning.

The application discloses a coupled space-time baseline optimization method suitable for high-orbit differential layer analysis SAR, which models the space-time baseline optimization of a GEO SAR as a combination optimization problem, takes minimizing the space-time correlation coefficient and maximizing the unambiguous height as optimization objectives based on the Cramér-Rao lower bound (CRLB) of GEO D-TomoSAR parameter estimation and the baseline characteristics of the GEO SAR, and searches for the space-time baseline data with the best imaging effect in all space-time baseline data under multiple constraint conditions by combining an NSGA-II algorithm.

The present application belongs to the technical field of reconfigurable robot energy consumption optimization, and particularly relates to a modular robot reconfiguration energy consumption certainty optimization method and system. The modular robot reconfiguration energy consumption certainty optimization method comprises: determining an initial configuration before the start of modular robot reconfiguration and a target configuration after the end of modular robot reconfiguration; using a motion replacement mechanism to enable the module to move through the inside of the current configuration during the modular robot reconfiguration process; converting the minimum movement step number problem of all modules during the modular robot reconfiguration process into a combination optimization problem of task allocation, and obtaining a globally optimal reconfiguration solution by configuration sliding and the Hungarian algorithm to realize the optimization of reconfiguration energy consumption. The present application proposes a motion replacement mechanism, and quickly and efficiently calculates a globally optimal solution by configuration sliding and the Hungarian algorithm, overcomes the limitations of the probabilistic search method in solving randomness, and can guarantee the certainty of the solution.

This information processing apparatus comprises an acquisition means for acquiring target data, an assignment means for assigning a plurality of pieces of partial data that are obtained by dividing the target data and a plurality of partial models that are obtained by dividing a learning model to be trained with reference to the target data, to a plurality of processors in a distributed manner, and a training means for training, in each of the processors, a partial model that is assigned to that processor, with reference to at least one of the plurality of pieces of partial data. This achieves both high-speed performance and solution accuracy regarding a combinatorial optimization problem.

The invention provides a multi-base station coverage joint optimization method and system based on multi-point coordination. The method comprises the following steps: constructing a mixed integer nonlinear programming problem containing antenna parameters and a coordination service set as a joint optimization problem; carrying out continuous and micronizable approximate processing on the joint optimization problem, and converting the joint optimization problem into an end-to-end micronizable continuous optimization problem; and solving the continuous optimization problem by adopting a gradient-based optimization algorithm, and optimizing to obtain the optimal antenna parameter of each cell. According to the method, a discrete combination optimization problem which is difficult to directly solve is converted into a form which can be efficiently solved in a gradient manner, so that the coverage optimization efficiency and expandability of the large-scale cooperative network are remarkably improved.

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.