23 results about "Single objective optimization problem" patented technology

The invention provides an RIS-assisted MISO system discrete phase shift optimization method based on a DYYPO algorithm, and the method comprises the steps: constructing an RIS-assisted MISO communication system model, and carrying out the modeling of RIS passive beam forming into a high-dimensional discrete optimization problem; the fixed base station performs active beam forming, and the optimization problem is simplified into a single-target optimization problem with the maximum spectrum efficiency as the target; initializing a DYYPO algorithm, generating a development point and an exploration point, and setting algorithm parameters; a splitting stage is executed, local one-way splitting is carried out on the development points in parallel, and global multi-way splitting is carried out on the exploration points; executing an archiving stage, and periodically updating search points by adopting a double-elitist retention strategy; in the execution parameter updating stage, the development and exploration probability is dynamically adjusted to achieve self-adaptive convergence; and finally, outputting an optimal discrete phase shift vector as an RIS configuration scheme. According to the method, the problem of high-dimensional discrete phase shift optimization is solved, the method has the advantages of high global search capability and high convergence speed, and the spectrum efficiency of the system is remarkably improved.

The invention discloses a low-voltage distribution network state estimation-oriented low-voltage topology tracking method, which comprises the following steps of: acquiring standing book information of a target station area integrated power distribution information system, three-phase voltage and power measurement information of a public assessment ammeter on the secondary side of a distribution transformer, and voltage and power measurement information of a single-phase ammeter user; a multi-target 0-1 variable optimization problem considering minimum power balance and maximum voltage correlation is respectively constructed by three-phase voltage and power measurement information of three-phase electric meter users, a linear weighting method is adopted to integrate into a single-target optimization problem, and a branch and bound technology is adopted to solve so as to identify phases to which the electric meter users belong. And based on the user-phase-change identification result, the transformer area archive information is corrected, and accurate and complete low-voltage topological information is provided for subsequent state estimation and fine management of the distribution network.

The application relates to a hybrid energy storage system capacity optimization method. First, a multi-objective mathematical model of the hybrid energy storage system is established, which is characterized by the cost of the hybrid energy storage system and the minimum number of charge-discharge conversion times of the battery; then, the multi-objective optimization is converted into a single-objective optimization problem with weights through an entropy weight method, and an improved particle swarm algorithm is used to optimize the single-objective function with the weight coefficient, so that the optimal solution of the power and capacity of the hybrid energy storage system is obtained; finally, in order to measure the optimization effect of the hybrid energy storage system applied to the suppression of wind power fluctuation, the application defines the state of charge coefficient and the grid connection smoothing rate of the hybrid energy storage system as the evaluation indexes of the multi-objective optimization of the hybrid energy storage system.

The application discloses a pre-hospital care helicopter optimization deployment method fusing weighted K-means and IABC, first, patient data is acquired and the geographical position coordinates thereof are standardized pretreated, the patients are classified according to the injury degree and corresponding weight values are given, and a weighted data set is formed; subsequently, a weighted clustering square error and a silhouette coefficient are calculated, the elbow rule and the silhouette coefficient method are cooperatively decided, and the optimal helicopter deployment number k is determined; then, the weighted data set is taken as input, and the initial deployment position coordinates of the k helicopters are solved through a weighted K-means algorithm; then, a single-target fitness function integrated by three sub-functions including service coverage, rescue response time and economic cost is constructed, and a multi-target optimization problem is integrated into a single-target optimization problem; finally, an improved artificial bee colony (IABC) algorithm with a directional learning mechanism is adopted, the initial deployment position of the helicopter is taken as an initialization population for iterative updating, and the final pre-hospital care helicopter deployment coordinates are output.

The invention relates to a mobile antenna beam forming method and a wireless data energy integration system, and the method comprises the steps: building a large-scale fading channel model, and obtaining a data transmission rate expression and an energy signal power expression; linear precoding transmission is adopted at a base station, and a channel vector matrix from all positions of a movable antenna to all users is defined to obtain a constraint condition; constructing an optimization problem taking the energy signal power as a main target and taking the data transmission rate as a secondary target, and decomposing the optimization problem through a Pareto epsilon-constraint method to obtain a solvable single-target optimization problem; and through a generalized Benders decomposition algorithm and an alternating optimization algorithm, carrying out splitting solution on the solvable single-target optimization problem, and carrying out iterative convergence to obtain a final optimization result. According to the technical scheme provided by the invention, the transmission signal-to-noise ratio and the energy efficiency can be improved through the optimization of the movable antenna and the design of beam forming, and the flexible balance of data-energy cooperative transmission and the optimal design of system parameters are realized.

An optimal configuration method for a decommissioned battery energy storage system in power distribution network heavy overload treatment comprises the steps that a life loss model of current-carrying equipment is established, and the severity of power distribution network heavy overload is measured through a current-carrying equipment life loss fluctuation quantity sum index; constructing an energy storage system ESS economic evaluation model considering battery capacity attenuation and compensation, and quantifying the annual net cost of the energy storage system ESS; establishing a dual-objective optimization model based on the total life loss fluctuation quantity of the current-carrying equipment and the annual net cost of the energy storage system ESS; an improved fuzzy membership function is adopted to convert double targets into a single target optimization problem, and an energy storage system ESS optimal configuration and output strategy is solved based on an adaptive inertia weight particle swarm optimization algorithm. According to the method, a more accurate and economic scheme is provided for energy storage optimization configuration in a power distribution network heavy overload treatment scene.

The invention relates to a park carbon reduction path planning model and optimization method based on a zero carbon target, and the model comprises a data collection and preprocessing module which is used for constructing an energy and carbon emission database; the carbon emission accounting module is used for determining the current situation of carbon emission and a main emission source; the energy system modeling sub-module is used for establishing a mathematical model of the energy system; the industrial structure adjustment sub-module is used for analyzing the carbon emission intensity and the development trend of different industries in the park and establishing an industrial adjustment model; the constraint condition setting sub-module is used for setting various constraint conditions which need to be met by the park carbon reduction path planning; and the objective function construction sub-module is used for constructing a multi-objective optimization function by taking realization of a zero carbon objective as a guide, and converting the multi-objective optimization function into a single-objective optimization problem. According to the method, the carbon emission can be comprehensively calculated, the emission reduction key point is defined, and the scientific, economic and operable optimal carbon reduction scheme is planned for the park through the multi-objective optimization algorithm under the condition that the actual constraint condition is met.

The application discloses a kind of air-cooled PEMFC temperature control method based on NSGAIII, it is related to fuel cell thermal management technical field, comprising the following steps: obtaining multiple adjustment parameters, overshoot, temperature identification error and temperature control error when RBF-PID controller controls thermal management system model;Establish single-objective optimization problem, solve single-objective optimization problem;Establish multi-objective optimization problem, solve multi-objective optimization problem by NSGAIII, obtain multiple optimal adjustment parameters;Multiple optimal adjustment parameters are input to RBF-PID controller, and the temperature of air-cooled PEMFC is controlled by RBF-PID controller with multiple optimal control parameters.The application can ensure that the obtained optimization result is in the Pareto frontier, realize the accurate control of the temperature of air-cooled PEMFC, while providing multiple optimal solutions for different design requirements, improve the solving efficiency.

The invention discloses a Kubernetes micro-service redeployment method and device based on service communication resource sharing and a medium, and relates to the field of electric digital data processing, and the redeployment method comprises the following steps: S1, constructing a multi-target service communication optimization model; s2, converting the multi-target service communication optimization model into a single-target optimization problem; s3, solving a single-target optimization problem by adopting a continuous convex approximation method to obtain an optimal solution; s4, on the basis of the optimal solution, constructing a micro-service redeployment scheme in combination with computing resource constraints; and S5, according to the micro service redeployment scheme, redeploying the micro service in the Kubernetes cluster. According to the method, the multi-target service communication optimization model based on service communication resource sharing is constructed, the micro-service communication overhead is converted into two core targets of mirror image pulling delay and data transmission overhead, the communication overhead for operating the micro-service for a long time is reduced, and on the premise that the resource requirement of Kubernetes deployment is guaranteed, the service communication efficiency is improved. And a micro-service communication network and bandwidth consumption are optimized.

The invention discloses an unmanned aerial vehicle computing power network method assisted by a dynamic partition intelligent reflecting surface, and the method comprises the steps: firstly constructing a system model, and determining a computing power communication matching and energy consumption model; constructing an optimization problem taking minimization of user delay and unmanned aerial vehicle energy consumption as targets, and converting the optimization problem into a single-target optimization problem so as to decompose the optimization problem into three sub-problems of phase shift control and user association, calculation-communication resource matching and unmanned aerial vehicle flight path optimization; and finally, performing sub-problem solving by using a phase alignment and channel optimal matching method, a successive convex approximation method and a multi-agent proximity strategy optimization method. According to the method, combined allocation of reflection units and computing resources is realized, the utilization rate of system resources is effectively improved, the problem of mixed integer non-convex optimization is effectively solved, and the system can autonomously realize optimal balance of time delay and energy consumption in a dynamic environment. Simulation results show that the scheme can stably obtain the optimal comprehensive performance of the system in various scenes.

The present invention discloses a dual decomposition method for the sparse adversarial attack problem of image classification. First, the sparse adversarial attack problem of image classification is modeled as a large-scale multi-objective optimization problem, with each pixel of the image as a decision variable, and then a dual decomposition strategy is executed. The outer decomposition uses a sliding window to divide the large-scale decision variables into overlapping subsets of small-scale decision variables. Each time the sliding window slides, a small-scale multi-objective optimization problem is generated. Once the small-scale multi-objective optimization problem is generated, the inner decomposition immediately creates a set of global direction vectors and converts them into a set of single-objective optimization problems. Finally, a block coordinate descent strategy is used to optimize all single-objective problems, thereby enhancing the integrity and optimality of the solution during the optimization process. Experiments show that the proposed method has better results than other algorithms in solving large-scale multi-objective problems, and can find the best perturbation compared to other algorithms in sparse adversarial attack problems.

The application discloses a traffic signal timing optimization method based on an improved crown porcupine algorithm, and the method comprises the following steps: 1) collecting information of a traffic signal intersection; 2) establishing a traffic signal timing optimization model of the intersection; 3) setting a constraint condition of the traffic signal timing optimization model; 4) converting a multi-objective optimization problem of the traffic signal timing optimization model of the intersection into a single-objective optimization problem, and then combining the model constraint to solve the model to obtain a traffic signal timing scheme of the intersection in a current period. The traffic signal timing optimization method uses the improved crown porcupine algorithm to solve a target function, and optimizes a scheme of an optimal solution, and the application can effectively improve the feasibility and excellence of the intersection signal timing scheme.

The application discloses a proton intensity modulated multi-objective optimization method based on cooperation of multi-objective evolution and traditional optimization method, and relates to the technical field of medical treatment. The application discloses a proton intensity modulated multi-objective optimization method based on cooperation of multi-objective evolution and traditional optimization method, and relates to the technical field of medical treatment. The application discloses a proton intensity modulated multi-objective optimization method based on cooperation of multi-objective evolution and traditional optimization method, and relates to the technical field of medical treatment.

The invention belongs to the technical field of financial investment, and discloses a construction method and application of a hybrid prediction model based on BNN and NAM, and the model comprises a loan evaluation network and an investment portfolio optimization network. The construction method comprises the following steps: preprocessing loan data, and selecting an internal return rate as an evaluation index of loan profitability; the data set is divided into a training set, a verification set and a test set, an NAM framework is adopted for loan evaluation, each sub-module of the NAM framework is realized as a BNN, given loan data serve as input, and the model outputs the prediction return and risk of each loan; and finally, performing investment portfolio optimization, solving a single-target optimization problem by using a genetic algorithm, and finding an optimal investment weight set as an optimal investment strategy. According to the invention, through formulating a practical investment strategy, enhanced investment return is realized; by giving importance to different loan attributes, the integrated NAM framework improves the interpretability of the model.

The application discloses a multi-objective optimization method for trade-off energy efficiency and spectrum efficiency of space-air-ground-thing Internet of Things, and belongs to the field of communication resource allocation.The application realizes the method as follows: constructing a space-air-ground-thing Internet of Things model, constructing a multi-objective optimization problem of maximizing system energy efficiency and system spectrum efficiency according to the space-air-ground-thing Internet of Things model; introducing an energy efficiency and spectrum efficiency trade-off coefficient, and converting the multi-objective optimization problem of trade-off energy efficiency and spectrum efficiency in the space-air-ground-thing Internet of Things network into a single-objective optimization problem by using a constraint method; dividing the complex single-objective optimization problem into three sub-problems of sub-channel selection, unmanned aerial vehicle and Internet of Things device power control and unmanned aerial vehicle position deployment, and further improving optimization efficiency; and adopting a block coordinate descent method to iteratively solve the three sub-problems in turn to obtain a solution of the multi-objective optimization problem of trade-off energy efficiency and spectrum efficiency, until a multi-objective optimization result is obtained.The application is suitable for the case that there are obstacles between unmanned aerial vehicles and Internet of Things devices, and can improve the energy efficiency and spectrum efficiency of the system.

The invention relates to the technical field of wireless communication, and provides a satellite-ground multi-target resource allocation method and device based on STAR-RIS. According to the invention, by constructing a multi-objective optimization problem, collaborative optimization is carried out on the total collected energy of energy collection users, the communication rate of outdoor communication users and the reachable secure transmission rate of indoor satellite users; a multi-objective optimization problem is decomposed into single-objective optimization problems through a weighted Chebyshev method to pursue a Pareto optimal solution, and each single-objective optimization problem is solved by adopting an alternating optimization framework fusing successive convex approximation and semi-definite relaxation, so that a complex non-convex solution problem is converted into a convex optimization problem which can be efficiently solved. After the optimized parameters are solved, beam forming strategies of the satellite and the ground base station and signal regulation and control parameters of the STAR-RIS are respectively adjusted according to the optimized parameters, so that multi-target collaborative balance of safety, rate and energy is realized, and safety threats brought by potential eavesdroppers are effectively handled.

This invention provides an optimization method for a cellless symbiotic communication and sensing system based on passive metamaterial tags, relating to the field of Internet of Things (IoT) technology. The method includes: Step S1, constructing a cellless symbiotic communication and sensing system based on passive metamaterial tags; Step S2, constructing a joint beamforming multi-objective optimization problem, which simultaneously maximizes the communication and rate of the main system and the communication and rate of the secondary system. Constraints include sensing SINR under multiple sensing objectives, and optimization variables are the AP transmit beamforming matrix, the AP receive equalization vector, and the metamaterial tag reflection phase matrix; Step S3, using a constraint transformation-based Pareto optimization framework, transforming the multi-objective optimization problem into a single-objective optimization problem, and obtaining the final beamforming optimization result by iteratively solving the optimization variables described in Step S2. This invention can effectively improve the symbiotic communication and sensing performance of cellless systems.

This invention provides a two-layer optimization method, system, and computer storage device for large-scale, expensive single-objective optimization problems. The method involves steps including initialization, subproblem construction, surrogate model construction, subproblem optimization and evaluation, a bottom-level population update stage, a top-level surrogate model selection stage, and a top-level optimization and global update stage. By constructing and evaluating multiple candidate subspaces at the lower layer, it automatically identifies key subspaces that significantly impact the global objective and focuses on optimizing these key subspaces at the upper layer. Under a finite expensive function evaluation budget, it achieves efficient solutions to large-scale single-objective optimization problems. By introducing a surrogate model to approximate the expensive objective function and combining it with a two-layer optimization structure, it organically combines subspace selection and fine-tuning, effectively improving search efficiency, accelerating convergence speed, and enhancing the quality of the final solution, thus meeting the application needs of complex engineering optimization scenarios.

This invention proposes a multi-layer service optimization deployment method based on cloud-edge computing networks. First, a multi-layer edge computing network scenario is constructed, establishing deployment and communication models. Second, the multi-objective optimization problem of transmission latency and deployment cost is modeled as a single-objective optimization problem. Public services accessible from multiple regions are deployed on the least common parent node of the network tree, and a greedy method is used to progressively create replicas of the services, placing them closer to users. This invention is applicable to multi-layer service optimization deployment in edge computing scenarios, achieving the goal of improving user service quality at low cost by reducing deployment costs and request latency.

The invention provides a photoetching process window calculation optimization method, system and terminal, and the method comprises the steps: firstly calculating a standard EPE value range of a current position segmentation line segment, and defining optimization targets of various key fault mode functions; then selecting a plurality of initial process condition points to carry out photoetching simulation, obtaining real EPE values of all key fault modes of each point, and constructing a data set; training a multi-task Gaussian process agent model based on the data set, re-training the model after Bayesian mixed acquisition function calculation and data set updating, and repeatedly iterating until a termination condition is met to obtain a final agent model; and searching a prediction extreme value through the final model, and comparing the prediction extreme value with a standard EPE range to determine a final EPE value range. According to the method, the multi-target acquisition function is weighted and fused into a single-target optimization problem, so that the calculation complexity is greatly reduced, and the verification time is remarkably shortened; in addition, by adjusting weight parameters, a key fault mode which has a greater influence on the actual photoetching summary yield can be inclined, optimization of calculation process resources is guided, and the overall calculation efficiency is improved.

The invention belongs to the technical field of wireless communication, and discloses a half-duplex decoding and forwarding unmanned aerial vehicle relay communication method based on course angle optimization, which comprises the following steps: constructing a wireless communication system in which an unmanned aerial vehicle performs relay service, obtaining system parameters, obtaining a calculation formula of the distance between user antennas at any moment, and calculating the distance between the user antennas at any moment; the method comprises the following steps: firstly, constructing a course angle optimization design problem of the maximum accumulated channel capacity, and then converting the course angle optimization design problem of the maximum accumulated channel capacity into a solvable single-target optimization problem by utilizing large signal-to-noise ratio approximation, an exponential operation rule, a linear weighting method and a distance formula between user antennas; an optimal solution is solved by utilizing a method for solving an extreme value of a continuous function, and based on the obtained optimal course angle, the unmanned aerial vehicle starts from a starting point to execute a flight task to serve as a forwarding relay to provide relay service for ground users. According to the method, less prior information is needed, meanwhile, higher applicability is achieved, and better system performance can be provided.

The invention discloses a communication resource optimization method for a wireless network control system in a low-altitude Internet of Things, and relates to the technical field of wireless communication resource allocation. The method comprises the following steps: constructing a communication resource coupling model of a wireless network control system in the low-altitude Internet of Things, wherein the communication resource coupling model comprises a communication model, a calculation time model and a unified energy constraint; based on the communication resource coupling model, constructing a resource joint optimization problem with minimization of the LQR control cost as a target by using a monotone mapping relation between the lower bound of the LQR control cost and an effective load; based on a condition that an uplink effective information load is equal to a downlink effective information load, namely, an effective load balance condition, a resource joint optimization problem is converted into a single-target optimization problem taking maximization of the uplink effective information load as a target; on the basis of an alternative optimization algorithm, a single-target optimization problem is decomposed into a time and bandwidth optimization sub-problem and a power allocation sub-problem, and an optimal resource allocation scheme is obtained through iterative solution.

The present invention provides an ADHD case classification method and device based on a weighted sum strategy distributed algorithm, and relates to the field of machine learning technology. The present invention uses fMRI data and classification technology, and proposes a distributed multi-objective optimization method based on a weighted strategy, and applies it to a binary ADHD soft margin support vector machine. The multi-objective support vector machine is converted into a single-objective optimization problem using the weighted sum method, and then it is iteratively solved using a distributed method. This method can effectively handle SVMs with multiple performance indicators, improve optimization efficiency through collaboration and coordination between multi-agent systems, and effectively solve the ADHD classification problem with data distributed on different service hosts. Since rs-fMRI shows its unique advantages in the analysis of mental illness, it can be used not only for the classification of ADHD, but also for the classification of schizophrenia and Alzheimer's cases.