15 results about "Competitive algorithm" patented technology

The invention provides a heterogeneous resource dynamic sensing and self-adaptive scheduling method, which comprises the following steps of: dynamically acquiring basic attributes such as resource types, core configuration, memory capacity and bandwidth and real-time information such as load state and temperature through a heterogeneous resource feature extraction module by utilizing a hardware interface and software probe technology, and storing data in a feature table after processing, accurate scheduling decision basis is ensured; a self-adaptive scheduling algorithm is designed, matching with resource characteristics is carried out based on task calculation, memory and parallelism requirements, proper resources are preferentially allocated, competition is avoided by checking a load state, and the algorithm supports dynamic adjustment so as to optimize allocation efficiency; and finally, through a real-time monitoring and dynamic adjustment mechanism, regularly collecting resource and task state information, detecting a competition or low-efficiency condition, executing operations such as task migration, priority adjustment or resource reservation and the like, and ensuring efficient operation of the system. The heterogeneous resource scheduling efficiency and stability are improved, and the method is suitable for a modern computing environment.

The invention discloses an electronic circuit energy efficiency optimization design method based on a genetic algorithm, and the method comprises the following steps: S1, carrying out the modeling of a to-be-optimized electronic circuit, forming a mixed type chromosome, and initializing a genetic algorithm population; s2, constructing a fitness function based on a genetic algorithm population; s3, executing a selection operation on the current population, and replacing a parent by a child with high adaptability; s4, performing crossover operation on the parent individuals to generate new offspring individuals; s5, performing mutation operation on the offspring individuals to form new individuals; s6, performing circuit simulation on the new individual after the genetic manipulation is executed, and inputting a simulation result into a fitness function for evaluation; s7, updating the population according to the fitness score, and entering next-generation evolution; and S8, configuring an individual parameter which meets a convergence condition and has the highest output fitness score. Based on a local competition algorithm, a double-layer block recombination strategy and a Pareto frontier algorithm are fused, and energy efficiency optimization of the electronic circuit is achieved.

The invention belongs to the related technical field of circuit design, and discloses a storage and calculation integrated solving circuit for solving a regularization linear regression problem, and the circuit comprises a memristor array, a compensation conductance module, an input neuron module and an output neuron module, the compensation conductance module is used for providing compensation conductance for the memristor array, the input neuron module maps calculation, the output neuron module maps calculation of leakage integral and outputs a final solution vector a after processing, and a calculation result of the whole loop strictly conforms to a kinetic equation of a local competition algorithm. According to the invention, only one memristor array is arranged, and the rows and columns of the memristor array can be reused as an input end and an output end, so that the regularization linear regression problem can be quickly solved based on a small-scale storage solving circuit.

The present invention belongs to the technical field of transformer maintenance, and discloses a transformer predictive maintenance system, which includes: collecting the operation data of the transformer, preprocessing the operation data to obtain operation characteristic data; clustering the operation characteristic data to obtain n operation state clusters; constructing a state directed graph with the n operation state clusters as nodes; solving the shortest path probability from each node to all other nodes in the state directed graph; constructing constraint conditions and an optimization function based on the state directed graph and the solved shortest path probability, and solving the optimization function by using the imperialist competitive algorithm based on the constraint conditions to obtain the optimal maintenance strategy of the transformer, which can not only predict potential fault risks, but also optimize the allocation of maintenance resources, avoiding unnecessary over-maintenance or under-maintenance; improving the scientificity and economy of transformer management.

The present invention provides a method and system for scheduling ship main power equipment maintenance projects that take employee characteristics into consideration, and relates to the technical field of project scheduling. With the goal of minimizing order duration, the present invention, based on the real situation of multi-skill, multi-mode project scheduling with team collaboration and employee changes, studies the number of teams employees should form, the type of teams they should form, the task model, team selection, and task allocation. The system uses the Intelligent Fruit Fly Optimization Empire Competition Algorithm (IFOICA) to solve the problem, ultimately obtaining a project scheduling result that takes into consideration employee characteristics such as team collaboration and employee changes. The present invention's method for scheduling ship main power equipment maintenance projects that takes employee characteristics into consideration produces more accurate and efficient solutions.

The present invention provides a method for optimizing ship traffic organization based on an improved multi-objective imperialist competitive algorithm, which relates to the technical field of optimizing port ship traffic organization. It mainly includes: First, an initial empire is constructed according to the designed multi-objective ship sequence cost function based on the total waiting and total scheduling time of ships; Then, an adaptive ship safety time headway revolution operator is designed to perform revolution operations on colonies, simulating unexpected changes in colonies; Then, a multi-objective discrete threshold division strategy is designed to adaptively select fission objects and perform fission operations. The present invention provides a new method for solving the problem of optimizing ship traffic organization, which can solve the problem of easy premature convergence and easy local optimum in the optimization when the imperialist competitive algorithm is simply used to solve the ship traffic scheduling scheme, and has the positive effect of improving the operation efficiency of ships entering and leaving the port.

The invention provides a flexible job shop scheduling optimization method based on a double-target empire competition algorithm. In order to improve the efficiency of the core equipment in the flexible job shop scheduling problem FJSP, a mathematical model is established, which takes the minimization of the maximum completion time and the maximization of the core equipment utilization as the target. An improved empire competition algorithm IICA is designed to solve the model. In the algorithm, a left shift decoding rule is proposed to solve the scheduling integration problem. Two heuristic search strategies are used for global search of the empire competition algorithm. On this basis, the adaptive number revolution algorithm is used to improve the convergence speed in the later stage to improve the local search efficiency. Simulation experiments verify the feasibility of the optimization model. The experimental results show that the algorithm has higher efficiency and effectiveness than the existing algorithm.

The invention provides a task success transmission rate-oriented low-altitude unmanned aerial vehicle cluster network resource allocation method, and relates to the technical field of low-altitude communication, and the method comprises the steps: building a low-altitude communication network system model based on an unmanned aerial vehicle cluster; constructing an unmanned aerial vehicle cluster network resource allocation optimization equation based on the low-altitude communication network system model; based on the unmanned aerial vehicle cluster network resource allocation optimization equation, performing analysis by using an unmanned aerial vehicle cluster frequency block sharing method to obtain a frequency block sharing scheme of the unmanned aerial vehicle clusters, and determining the number of sub-channels contained in a frequency block occupied by each unmanned aerial vehicle cluster by using a dynamic frequency block competition algorithm to obtain a resource allocation scheme between the unmanned aerial vehicle clusters; and optimizing the resource allocation scheme in the cluster by using a multi-priority dynamic resource allocation algorithm and taking maximization of the differentiation task successful transmission probability as a target to obtain a network resource allocation result. The problem that reliable transmission of low-altitude key tasks and efficient utilization of resources are difficult to ensure in the prior art is solved.

The present invention relates to a bearing fault diagnosis method based on an improved imperial competition algorithm to optimize SVC, wherein the penalty factor C and the kernel parameter Gamma of the SVC are obtained after being optimized by the improved imperial competition algorithm; the improved imperial competition algorithm refers to an imperial competition algorithm including an imperial reinforcement strategy; the imperial reinforcement strategy refers to taking each empire and four randomly selected empires as inputs of a differential evolution algorithm in each round of iteration of the imperial competition algorithm, and sequentially undergoing mutation, crossover and selection in the differential evolution algorithm, outputting the strongest empire as the empire of the colony group in the next iteration. The imperial reinforcement strategy provided by the improved imperial competition algorithm of the present invention can increase diversity, prevent the empire and the colony from falling into the local optimum, and further use the improved imperial competition algorithm to optimize the SVC, which can well help the SVC find the best hyperparameters, thereby helping the model improve the accuracy of fault diagnosis.

The application discloses a kind of scheduling method of distributed manufacturing based on empire competition algorithm, comprising the following steps: establishing the objective function including minimizing maximum completion time, minimizing maximum machine load and minimizing total energy consumption, and setting the constraint condition of objective function according to the assembly process of product;Set coding and decoding rules, encode process sequencing and machine selection to obtain process sequencing vector OSV and machine selection vector MAV;Set the hybrid initialization rule of OSV and MAV, generate initial solution set;Calculate the objective function value of all initial solutions in initial solution set, select the better solution as the colony from it, the remaining initial solution is as the colony, adopt OX cross to realize the assimilation between colony, adopt POX cross to realize the assimilation between colony and colony, generate new solution set;Design the neighborhood of OSV and MAV, carry out variable neighborhood descent search to colony, obtain the optimal production scheduling scheme.

A multi-objective flexible job shop scheduling method based on imperialist competitive algorithm is proposed. In order to improve the efficiency of core equipment in flexible job shop scheduling problem, a mathematical model is established to minimize the maximum completion time, maximize the utilization of core equipment and minimize the number of batches. An improved imperialist competitive algorithm is designed to solve the model. In the algorithm, a two-stage coding method composed of batch division and batch scheduling is proposed, and a left shift decoding rule is used to solve the scheduling integration problem. A global search is performed by using a heuristic optimization based on adaptive parameters to avoid falling into local optimum. On this basis, a variety of assimilation strategies are used alternately in the assimilation process to strengthen the local search ability. Simulation experiments verify the feasibility of the proposed optimization model. The experimental results show that the algorithm has higher efficiency and effectiveness than the existing algorithms.

The invention relates to a method for suppressing noise in images captured by means of a vehicle camera (2), wherein - image data (x) acquired by the vehicle camera (2) using a differentiable Locally Competitive Algorithm (LCA) model developed using sparse coding and exhibiting noise are represented as a linear combination of learnable elements of a basis with associated coefficients intended for the reconstruction of the image data (x) and the image data (x) are reconstructed with the elements from the basis while removing the noise, minimizing a reconstruction error and - the corresponding coefficients are predicted using a trained artificial neural estimation network and fed into the LCA model for refinement. The invention further relates to a method for operating a night vision system for a vehicle (1) and a device (3) for suppressing noise in images captured by means of a vehicle camera (2).

The invention discloses a distributed manufacturing scheduling method based on an empire competition algorithm, and the method comprises the following steps: building an objective function which comprises the minimization of the maximum completion time, the minimization of the maximum machine load and the minimization of the total energy consumption, and setting the constraint conditions of the objective function according to the assembly technology of a product; coding and decoding rules are set, and coding is carried out on process sorting and machine selection to obtain a process sorting vector OSV and a machine selection vector MAV; setting a mixed initialization rule of OSV and MAV, and generating an initial solution set; target function values of all initial solutions in the initial solution set are calculated, a better solution is selected from the initial solutions to serve as colonies, the remaining initial solutions serve as colonies, assimilation between the colonies is achieved through OX crossing, assimilation between the colonies and the colonies is achieved through POX crossing, and a new solution set is generated; and designing neighborhoods of the OSV and the MAV, and carrying out variable neighborhood descent search on the colonization countries to obtain an optimal production scheduling scheme.

This invention discloses an IoT-based intelligent irrigation control system for farmland, relating to the technical field of irrigation control. It consists of four modules: a data acquisition module for collecting and preprocessing environmental data to obtain the initial prediction time span, fractional order, and environmental gain weights, forming a combination of representative quantities; an optimization module for optimizing the combination of representative quantities using an Empire competition algorithm to obtain the optimal representative quantity; a decision module for calculating the fractional order compensation term using the optimal representative quantity and discretizing the fractional order compensation term to obtain the control signal; and an execution module for mapping the control signal to the control variables of the actuator, outputting a variable flow rate water supply curve, and generating water supply control commands based on the variable flow rate water supply curve. This enables the actuator to sense the nonlinear transport characteristics of water in the soil and automatically adjust the flow rate output when approaching the irrigation target, using a forward-looking control signal to offset the physical infiltration delay.

The invention discloses a transfusion state real-time monitoring and abnormity early warning method based on an attention mechanism. The transfusion state real-time monitoring and abnormity early warning method comprises the following steps that S1, multi-mode transfusion state data in the transfusion process are collected and subjected to standardization processing; s2, inputting the multi-modal standardized infusion state vector set into an attention mechanism model; s3, constructing a joint fitness function; s4, optimizing structural parameters of the attention mechanism model by using an empire competition algorithm, and introducing a resource awareness mechanism in a colony evolution process to fuse edge deployment constraints; s5, carrying out adaptability evaluation on each structure candidate solution in the structure candidate solution set; s6, constructing a deployable real-time infusion monitoring model, and deploying the real-time infusion monitoring model to edge terminal equipment; and S7, according to the risk score value and the early warning type label, executing infusion abnormal type identification and early warning triggering operation. According to the invention, a resource perception optimization method is adopted, and real-time intelligent early warning of infusion abnormity is realized.