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162 results about "Combinatorial optimization problem" patented technology

Combined optimization method for agricultural chain-operation logistics delivering and loading-distribution

The invention relates to a combined optimization method for agricultural chain-operation logistics delivering and loading-distribution, belonging to the technical field of the combined optimization ofthe logistics delivering and loading-distribution. The technical scheme comprises: proposing a model for the combined optimization of the agricultural chain-operation logistics delivering and loading-distribution and converting the model into the problem of the combined optimization of a single delivering center, a single variety and non-full-loading delivering and loading-distribution; designinga solution algorithm for the model for the combined optimization of the agricultural chain-operation logistics delivering and loading-distribution based on the genetic algorithm principle and solving; and developing a visual vehicle delivering and loading-distribution scheduling management system according to an optimization algorithm and a GIS development platform. The proposed model can actually reflect the interaction of the agricultural loading process and the agricultural delivering process in the operation process of agricultural chain-operation logistics delivering enterprises and embodies that the agricultural loading scheme can determine the selection of agricultural delivering routes to a certain degree. The affection of the agricultural loading and the agricultural delivering on the cost of the agricultural chain-operation logistics delivering enterprises can be comprehensively considered, thereby effectively lowering the loading and delivering cost and the operating cost of agricultural logistics delivering enterprises.
Owner:BEIJING JIAOTONG UNIV

Federal learning computing unloading computing system and method based on cloud side end

The invention discloses a federated learning computing unloading resource allocation system and a method based on a cloud side end, and aims to make an accurate decision for computing task unloading and resource allocation, eliminate the need for solving a combinatorial optimization problem and greatly reduce the computing complexity. Based on cloud side three-layer federated learning, the adjacent advantage of edge nodes to a terminal is comprehensively utilized, core powerful computing resources in cloud computing are also utilized, the problem that the computing resources of the edge nodes are insufficient is solved, a local model is trained at each of multiple clients to predict an unloading task. A global model is formed by periodically executing one-time parameter aggregation at an edge end, the cloud end executes one-time aggregation after the edge executes the periodic aggregation until a global BiLSTM model is formed through convergence, and the global model can intelligently predict the information amount of each unloading task. Therefore, guidance is better provided for calculation unloading and resource allocation.
Owner:XI AN JIAOTONG UNIV

Multi-objective optimization virtual machine placing method under cloud environment

The invention discloses a multi-objective optimization virtual machine placing method under the cloud environment. According to the method provided by the invention, firstly, initializing various parameters in an improved ant colony algorithm, wherein the number of ants NA is included; secondly, sorting a server list in a random arrangement manner, and selecting a new server to perform the virtual machine placing from servers; thirdly, using the formula to calculate the server which is the most valuable server to be placed into a virtual machine based on the all servers which can be placed into the virtual machine, and placing the virtual machine on the current server; lastly, repeating the former step until the resource of the current server cannot meet resource needs of all virtual machines required to be placed. According to the method provided by the invention, the virtual machine placement problem serves as a multi-objective combinational optimization problem, and meanwhile, resource usage and power consumption of a physical machine during running of the virtual machine are taken into account. Compared to other solutions, the method can better improve the resource utilization of a physical server and reduce the power consumption of the physical server.
Owner:HANGZHOU DIANZI UNIV

Spectral method for sparse principal component analysis

InactiveUS20070156471A1Improve approximate candidate solutionEasy to solveFinanceCharacter and pattern recognitionFeature vectorRenormalization
A method maximizes a candidate solution to a cardinality-constrained combinatorial optimization problem of sparse principal component analysis. An approximate method has as input a covariance matrix A, a candidate solution, and a sparsity parameter k. A variational renormalization for the candidate solution vector x with regards to the eigenvalue structure of the covariance matrix A and the sparsity parameter k is then performed by means of a sub-matrix eigenvalue decomposition of A to obtain a variance maximized k-sparse eigenvector x that is the best possible solution. Another method solves the problem by means of a nested greedy search technique that includes a forward and backward pass. An exact solution to the problem initializes a branch-and-bound search with an output of a greedy solution.
Owner:MITSUBISHI ELECTRIC RES LAB INC

Thermal process model parameter identification method through improved hybrid particle swarm algorithm

The invention discloses a thermal process model parameter identification method through an improved hybrid particle swarm algorithm. The method comprises the following steps: 1) determining an identification system structure and parameters to be identified; 2) obtaining input / output data for identification; and 3) carrying out the improved hybrid particle swarm algorithm to obtain an optimal solution. The identification problem of a thermal process model is converted into the combinatorial optimization problem of parameters; effective searching is carried out on a parameter space through the improved hybrid particle swarm algorithm to obtain optimal estimation of system model parameters; compared with a basic particle group algorithm, the method introduces selection, hybridization and mutation mechanisms in a genetic algorithm, thereby keeping population diversity and preventing the algorithm from being trapped in the local optimal solution; the idea of vaccine extraction and vaccination in artificial immunity is introduced, so hat algorithm search speed is improved; improved adaptive mutation is adopted, so that diversity of particles is kept more reasonably; and through introduction of a simulated annealing idea, the method has probabilistic leap capability in the searching process and prevents the searching process from being trapped in the local optimal solution.
Owner:SOUTHEAST UNIV

Spectral method for sparse linear discriminant analysis

A computer implemented method maximizes candidate solutions to a cardinality-constrained combinatorial optimization problem of sparse linear discriminant analysis. A candidate sparse solution vector x with k non-zero elements is inputted, along with a pair of covariance matrices A, B measuring between-class and within-class covariance of binary input data to be classified, the sparsity parameter k denoting a desired cardinality of a final solution vector. A variational renormalization of the candidate solution vector x is performed with regards to the pair of covariance matrices A, B and the sparsity parameter k to obtain a variance maximized discriminant eigenvector {circumflex over (x)} with cardinality k that is locally optimal for the sparsity parameter k and zero-pattern of the candidate sparse solution vector x, and is the final solution vector for the sparse linear discriminant analysis optimization problem. Another method solves the initial problem of finding a candidate sparse solution by means of a nested greedy search technique that includes a forward and backward pass. Another method, finds an exact and optimal solution to the general combinatorial problem by first finding a candidate by means of the previous nested greedy search technique and then using this candidate to initialize a branch-and-bound algorithm which gives the optimal solution.
Owner:MITSUBISHI ELECTRIC RES LAB INC +1

Water supply control method and water supply control system

The invention discloses a water supply control method and a water supply control system, wherein the method comprises the steps of: analyzing historical data to establish a water supply predication model and a function between the parameters of an input layer and the water supply amount output at an output layer; establishing a target function, wherein D(X) = is time interval, T is time, M(T) is a time-varying function of the price of power consumption per unit cost, D(X) is total power consumption and X[T] is solution; and calculating an optimized scheme for X[T] by means of genetic algorithm under restricted conditions; according to the water supply control method and the water supply control system, historical data is analyzed to establish the water supply predication model and the water supply function, the target function is established to perform combined optimization on water supply scheduling, optimization for the solution is achieved by means of genetic algorithm under restricted conditions, relatively high efficiency is obtained in optimization by simulating the principle of biologic evolution and the problem of complex combined optimization can be settled, and the stability and practicability of optimization method are verified by introducing the restricted conditions for research on water supply model, so as to realize stable supply during the water supply procedure of waterworks and the most economical energy-saving optimization control.
Owner:GUANGZHOU TOSHIBA BAIYUN AUTOMATION SYST

Group virtual machine scheduling policy for cloud computing environment

The invention discloses a group virtual machine scheduling policy for a cloud computing environment. The policy comprises the following steps of S1, establishing a feasible decision space of virtual machine scheduling; S2, minimizing total flow of a network where group virtual machines are located, and establishing an objective function for optimizing the total flow of the network; S3, minimizinga maximum link utilization rate in the network, and establishing an objective function for optimizing the maximum link utilization rate; and S4, establishing an overall objective function, and solvingthe overall objective function in combination with an ant colony algorithm and a simulated annealing algorithm to obtain an optimal solution of the function and mapping relationships between the virtual machines and a physical machine. Under the condition of fully considering resource constraints, the control of the total flow of the network and the balance of flow distribution on a network linkare defined as a combination optimization problem, and solving is performed in combination with the ant colony algorithm and the simulated annealing algorithm. According to the scheduling policy provided by the invention, the performance of the network where the group virtual machines are located can be better optimized; the congestion is reduced; and the service quality of users is effectively improved.
Owner:成都启力慧源科技有限公司

Flexible constraint propagation engine for combinatorial optimization applications

The present disclosure describes a computer-implemented constraint propagation system that supports a variety of different constraint propagation and / or constraint retraction algorithms, including monotonic and / or non-monotonic algorithms. In one embodiment, the system selects particular constraint propagation and / or retraction methods based on the nature of the combinatorial optimization problem (COP) being solved and the attributes of the particular COP application involved. The system may also enable new methods for constraint propagation and / or retraction to be added with relatively little disruptive effect on other components of the system. Embodiments of the system allow the propagation of constraints to be tuned to the semantics of each constraint, the likelihood of significant variable domain reduction, and other problem specific properties. The constraint propagation system is capable of being used as part of a reconfigurable search engine.
Owner:ACTENUM CORP

Walking aid electrostimulation fine control method based on genetic-ant colony fusion fuzzy controller

The invention relates to the rehabilitation training field and aims to optimize the quantifying factor and scale factor of a fuzzy controller and the fuzzy control rules, then control the current mode of an FES system accurately, stably and instantly and effectively improve the accuracy and stability of the FES system. The technical scheme adopted by the invention is as follows: the walking aid electrostimulation fine control method based on genetic-ant colony fusion fuzzy controller comprises the following steps: firstly, converting the selection of fuzzy control decision variable to the combinational optimization problem adapting to the genetic-ant colony algorithm, coding the decision variable, randomly generating a chromosome composed of n-numbered individuals; secondly, using the genetic algorithm to generate the initial pheromone distribution of the ant algorithm, utilizing the ant colony algorithm to randomly search and optimize the membership function, quantifying factor and scale factor of the fuzzy controller; and performing repeated self-learning and self-regulating according to the system output, and finally using the processes in the FES system. The invention is mainly used for rehabilitation training.
Owner:大天医学工程(天津)有限公司

Cooperation segmentation method fusing perception information

The invention discloses a cooperation segmentation method fusing perception information, which is used for performing joint segmentation on a group of image data set containing a common object, and each image can contain a plurality of common objects. The cooperation segmentation method comprises steps of using perception information like the significance, the repeatability, the space position, etc, which are based on the area as a global bound term to introduce an energy model to define the foreground likelihood, fully playing the important role of the perception information, converting the segmentation problem to the combination optimization problem to solve and using the object structural constraint based on the perception to perform iterative solution. Compared with the similar algorithm, the cooperation segmentation method fusing perception information and the solving method can be applicable to various complex occasions and can effectively segment the object.
Owner:ZHEJIANG UNIV

Test Size Reduction via Sparse Factor Analysis

A database of questions is designed to test understanding of a set of concepts. A subset of the questions is selected for administering to one or more learners in a test. One desires for the subset to be small, to minimize testing workload for the learners and grading workload for instructors. However, to preserve the ability to accurately estimate learners' knowledge of the concepts, the questions of the subset should be appropriately chosen and not too small in number. We propose among other things a non-adaptive algorithm and an adaptive algorithm for test size reduction (TeSR) using an extended version of the Sparse Factor Analysis (SPARFA) framework. The SPARFA framework is a framework for modeling learner responses to questions. Our new TeSR algorithms find fast approximate solutions to a combinatorial optimization problem that involves minimizing the uncertainly in assessing a learner's knowledge of the concepts.
Owner:RICE UNIV

Land use partition method based on tabu search algorithm

InactiveCN101763601ASolving Combinatorial Optimization ProblemsTaking into account hard constraintsResourcesNeighborhood searchKey issues
The invention provides a land use partition method based on tabu search algorithm, aiming to solve the problem that the traditional land use space partition method hardly takes multi-target characteristics of land use into consideration and lacks intelligent and efficient partition decision algorithm. The tabu search algorithm in mathematics field is applied in the land use space partition field; and key issues such as initial plan generation method, neighborhood rule definition, neighborhood generation method, neighborhood search method, memory list update, breadth search, depth search and restarting strategy are modified, a technical method for supporting the land use partition optimization decision is provided, the problem for optimizing spatial unit combination of multi-target in the land use partition is solved better and the problem of hard constraint conditions such as the continuity and integrity of spatial district is considered, so that the partition efficiency is higher and the stability and high efficiency of land use partition process are guaranteed.
Owner:WUHAN UNIV

Dot arrangement determination method, program and apparatus, threshold matrix creating method and program, and image forming apparatus

The dot arrangement determination method for determining an arrangement pattern when m dots (where m is a natural number) are arranged in a specific pixel area, the method comprises: a setting step of variably setting an arrangement pattern in which the m dots are arranged without overlapping at pixel positions in the specific pixel area; a first image evaluation value calculating step of calculating an image evaluation value of a halftone image formed by arranging the m dots in the specific pixel area in accordance with the arrangement pattern set in the setting step; a second image evaluation value calculating step of calculating an image evaluation value of a halftone image implemented according to the arrangement pattern set in the setting step when dots cannot be formed in an arbitrary pixel row in the specific pixel area; and a dot arrangement calculating step of determining the arrangement pattern with least image quality degradation for inability to form dots in an arbitrary pixel row in the specific pixel area by approximately solving a combinational optimization problem for the arrangement pattern using calculation results of the first and second image evaluation value calculating steps.
Owner:FUJIFILM CORP

Ant colony algorithm-based firepower distribution method

ActiveCN106779210AFast convergenceThere is no "gene drift" phenomenonForecastingArtificial lifeLocal optimumDecision model
The invention discloses an ant colony algorithm-based firepower distribution method. The method comprises the steps of firstly building an air combat threat degree model and a firepower distribution decision model according to a fight situation of both sides; and secondly, in the aspect of model solving, performing algorithm improvement for deficiencies of a typical ant colony algorithm: improving the ant colony algorithm in combination with thoughts of typical ant colony system and max-min ant colony system algorithms, so that the improved ant colony algorithm is more reasonable in early evolution trend and higher in convergence speed, and can be better prevented from falling into local optimum. The improved ant colony algorithm proposed for firepower distribution not only can be used for firepower distribution of an air combat but also can be expected to be used for other combination optimization problems such as decision problems of firepower distribution and the like in an attack battle of ground tank groups and a maritime warship formation combat.
Owner:NAT UNIV OF DEFENSE TECH

Method of optimization for power electronic circuit based on ant colony algorithm

The invention discloses - a power electronic circuit optimizing method based on an ant-colony-algorithm, which relates to the fields of the intelligent calculation and the power electron. The invention discretizes the possible values of all elements of the power electronic circuit into a series of standard values, and then maps the standard values into a structural diagram of an ant-colony-algorithm optimizing circuit. The ant-colony algorithm is adopted to select the best route in the structural diagram, namely, a best element value is selected to meet the required circuit performance. The ant-colony algorithm is very applicable to the discretization combinational optimization, thereby can rapidly and effectively optimize the power electronic circuit. The optimized results of the method are the standard values of all elements, so the optimized results can be directly applied to the actual production without any approximation.
Owner:SUN YAT SEN UNIV

Method for solving combination and optimization problems using ant colony optimization technology based on Map Reduce

The invention discloses a method for solving combination and optimization problems using an ant colony optimization technology based on Map Reduce and belongs to the technical field of solving the combination and optimization problems. The method for solving the combination and optimization problems using the ant colony optimization technology based on the Map Reduce comprises the following steps: dividing solution spaces of appointed combination and optimization problems according to amount of set mapper; in a Map period, every mapper independently executes an improved ant colony algorithm in parallel in a subproblem solution space acquired through division in the first step and searches a locally optimal solution; in the Reduce period, the reducer accepts all locally optimal solutions searched in different solution spaces by the mapper, and globally optimal solution is acquired according to a solution space division condition adopted in the first step; the globally optimal solution acquired currently by the reducer is output and the steps come to an end. The method for solving the combination and optimization problems using the ant colony optimization technology based on the Map Reduce is good in flexibility and capable of improving efficiency of solving a large-scale combination and optimization problems.
Owner:SOUTHEAST UNIV

Three-dimensional box loading method based on three-dimensional moving mode sequence and memetic algorithm

The invention discloses a three-dimensional box loading method based on a three-dimensional moving mode sequence and a memetic algorithm. The method mainly solves the problem of low utilization rate on the volume of a three-dimensional box loading container in the prior art. The three-dimensional box loading method comprises the following realization steps that 1, each parameter is set; 2, an initial population is randomly generated, and the adaptive fitness of individuals in the population is calculated; 3, whether the termination conditions are met or not is judged, if so, the step 4 is executed, and otherwise, the step 9 is executed; 4, a binary tournament method is used for selecting the individuals; 5, the individuals are crossed, and the individual adaptive fitness value is calculated again; 6, the individuals are subjected to variation, and the individual adaptive fitness value is calculated again; 7, the individual with the greatest adaptive fitness value in the current generation is stored; 8, the number of the iteration times is added to 1, and the operation returns to the step 3; 9, a hill climbing method is used for optimizing the individuals with the greatest adaptive fitness value, and the optimized box loading result is output. The method has the advantages that the volume utilization rate of the container can be improved, and the method can be used for solving the box loading problem, and can also be used for soling other combination optimization problems.
Owner:XIDIAN UNIV

SoC software-hardware partition method based on discrete Hopfield neural network

This invention relates to one SoC software and hardware division method based on discrete Hopfield neutral network, which comprises the following steps: adopting pattern description method to divide the software and hardware problem into one detail combination optimization problem to introduce SoC division problem new module; then according to the division property, re-defining discrete Hopfield neural network element, energy function, operation equation and parameters; dividing the discrete Hopfield network as division formula on SoC chip functions.
Owner:SICHUAN UNIV

Resource-constrained project scheduling method based on multi-agent evolutionary algorithm

The invention discloses a resource-constrained project scheduling method based on a multi-agent evolutionary algorithm, belonging to the technical field of automatic control and information. According to the method, a multi-agent system is combined with evolutionary computation so as to solve the resource-constrained project scheduling problem. The method is characterized by comprising the following steps of: initializing each agent in an agent grid according to two algorithms; and designing a neighborhood competition operator, a neighborhood cross operator, a mutation operator and a self-learning operator for optimizing the agent. The verification result proves that the method has advantages in two aspects for evaluating the effectiveness of the method for solving the resource-constrained project scheduling problem, namely the solving of the optimal solution proportion and the solving of the average error deviating from the optimal solution, so that the method effectively solves the resource-constrained project scheduling problem, and the method can be expanded to solving other combined optimization problems with precedence relationship constraints.
Owner:XIDIAN UNIV

Resource optimized distribution method for wireless power private network based on virtualization technology

The invention relates to a resource optimized distribution method for the wireless power private network based on a virtualization technology, and belongs to the technical field of electric communication. The method comprises the following steps that 1) the virtualized wireless power private network which comprises a base station model, a user model and a channel model is established; 2) a Tabu algorithm is used to optimize the virtualized wireless power private network; and 3) an objective function is optimized according to different constrained conditions, and a result of the objective function is obtained. According to the invention, the wireless resource is made abstract according to the power service type and present resource condition on the basis of the network virtualized technology; an optimal problem model of resource distribution is obtained by abstracting factors including the integrated network cost, profit, service isolation constraint, backhaul capacity constraint and QoS constraint, a combined optimization problem is solved by Tabu search, and problems in resource distribution of the wireless power access network are solved under the condition that the service isolation and service quality are ensured.
Owner:JIANGSU ELECTRIC POWER CO +1

Automating dynamic programs

InactiveUS20100205590A1Reduce in quantityImproves overall speed and scalabilityProgram controlMemory systemsExtensibilityTheoretical computer science
Solving combinatorial optimisation problems using dynamic programming involves automating the integration of bounds propagation into compilation of a dynamic program. This is done by extracting bounds from partial results obtained during dynamic programming, and tightening the bounds throughout execution of the dynamic program. This dramatically reduces the number of “good” solutions that need to be constructed at each stage, improving speed and scalability of algorithms using such dynamic programming.
Owner:NAT ICT AUSTRALIA

Markov decision process-based dynamic resource optimization method

The invention belongs to the technical field of dynamic resource optimization and specifically relates to a Markov decision process-based dynamic resource optimization method. The method is different from a conventional manufacturing resource selection method; a multi-development-task precision regulation and control cloud manufacture resource problem in a cloud manufacture environment is abstracted into a Markov decision process, mathematical modeling of effects exerted on resource selection by development process uncertainty can be realized, expected development cost is an object function, a cross entropy method is used for calculation, a combinational optimization problem is converted into a random association optimization problem, optimal selection probability of cloud manufacture resources can be obtained, reasonable scheduling and high efficiency utilization of manufacturing resources during cooperative development of complex products can be realized, and product development risk and manufacture cost can be effectively lowered.
Owner:INFORMATION CENT OF CHINA NORTH IND GRP

Walking aid functional electrical stimulation precision control method based on ant colony fuzzy controller

The invention relates to the field of rehabilitation devices and discloses a walking aid functional electrical stimulation precision control method based on an ant colony fuzzy controller, aiming at effectively improving the accuracy and the stability of an FES (Functional Electrical Stimulation) system. In the technical scheme of the invention, the walking aid FES precision control method based on the ant colony fuzzy controller comprises the steps of: firstly, converting a quantitative factor and a proportional factor of the fuzzy controller and the selection of 12 decision factors of a membership function parameter into a combination optimization problem applicable to an ant colony algorithm and carrying out encoding on the combination optimization problem and generating n initial urban agglomerations formed by individuals randomly; then, establishing a reasonable corresponding relationship target function of an actual joint angle and a muscle model output joint angle and determining the parameter configuration of the ant colony algorithm; entering an optimizing process; and regulating ant colony information quantity according to deviation, entering a next optimizing process, repeating the process, finally realizing the self-adaption on-line setting of the parameters of the fuzzy controller and applying to the FES system. The invention is mainly used for improving the accuracy and the stability of the FES system.
Owner:大天医学工程(天津)有限公司

Conveying path combination optimization method based on stage structured predator-prey model

The invention discloses a conveying path combination optimization method based on a stage structured predator-prey model. Supposing that multiple animal populations grow in an ecological system, and each population is divided into two stage types of adult populations and young populations; the adult populations produce their young populations at a certain birth rate, and the young populations grow up into the adult populations after a period of time; the influence of mutual competition for living resources between the populations is shown in the influence on the characteristics; the population having larger proportion in all the populations has higher influence, and the population spreads its influence to other populations; the advantageous and strong populations spread their high quality characteristics to other populations; and if one population is continuously influenced by other populations, its growth state continuously changes, and the global optimal solution of the problem of conveying path combination optimization can be rapidly determined by using the change and the multi-population predator-prey system model having the stage structure.
Owner:XI'AN UNIVERSITY OF ARCHITECTURE AND TECHNOLOGY

Joint optimization method for base station dormancy and cooperative cache in D2D-assisted ultra-dense network

ActiveCN109587776AGuaranteed Latency CharacteristicsGood performance gainPower managementNetwork traffic/resource managementQuality of serviceFile transmission
The invention discloses a joint optimization method for base station dormancy and cooperative cache in a D2D-assisted ultra-dense network. The method considers a caching scheme and a base station dormancy strategy at the same time. By analyzing the file transmission delay and the average system energy consumption, the energy consumption and delay weighing problem are described as the minimum costfunction problem. The minimum cost function problem is a complex combinatorial optimization problem. Since dormancy and caching are two independent processes, the original problem is decomposed into two sub-problems: first, the caching scheme is given, and the optimization base station dormancy scheme corresponding to the minimum cost function is found; and secondary, based on the optimization base station dormancy scheme, the optimal cooperative cache scheme is iteratively solved according to the combinatorial optimization algorithm, so that the system energy consumption is minimized under the condition that the user service quality is guaranteed.
Owner:SOUTHEAST UNIV

Intelligent vehicle SLAM data association method based on improved artificial fish swarm algorithm

The invention discloses an intelligent vehicle SLAM data association method based on an improved artificial fish swarm algorithm. The method is characterized by firstly, using an independent compatible criterion and a combined maximum likelihood criterion to determine an association hypothesis and converting a SLAM data association problem into a combined optimization problem; secondly, using an improved artificial fish swarm algorithm based on a jump behavior and a taboo strategy to solve the combined optimization problem, and solving an optimal data association set; introducing the jump behavior in the artificial fish swarm algorithm so that one part of artificial fishes jump out of a local extremum and global optimum is reached as far as possible; then using the improved artificial fish swarm algorithm based on the jump behavior to search a global suboptimal solution and taking the global suboptimal solution as an initial solution of a taboo search algorithm; and using the taboo search algorithm to search a local optimal solution so as to enhance a global optimization capability and optimization efficiency. By using the method of the invention, in a large outdoor range scene, an intelligent vehicle SLAM data association problem is effectively solved, a correct rate of data association and search efficiency of the optimal association set are increased, and operation time is reduced.
Owner:BEIJING UNIV OF TECH

Maximum power point-tracking photovoltaic system based on ant colony-artificial immune hybrid optimization algorithm

InactiveCN102651087AImproving Maximum Power Point Tracking EfficiencyImprove power generation performance ratioBiological modelsLight radiation electric generatorLocal optimumIdentifying problems
The invention provides an ant colony-artificial immune hybrid optimization algorithm-based maximum power point-tracking solution in a set of photovoltaic power generation system, in particular to a maximum power point-tracking photovoltaic system based on an ant colony-artificial immune hybrid optimization algorithm. The ant colony algorithm has a powerful advantage in solving the complex combinatorial optimization problem but has certain defects as well, and aimed at the problem that individual ants in the ant colony algorithm lack the ability of identifying problem characteristic information, the idea of vaccine in the immune algorithm is introduced into the ant colony algorithm to provide the ant colony-immune hybrid algorithm. Power optimizers applying the algorithm carry out dual-tracking on solar panels; on one hand, the optimal local MPP (maximum power point) is tracked; and on the other hand, the energy transmission in the system is increased to the max. The power optimizers are indirectly connected with one another, have cognitive ability and self-organization ability, and can detect and independently regulate respective current and voltage environments until the whole string of solar panels reaches an optimal value, and meanwhile, the level of the solar panels reaches a local optimal point.
Owner:GUANGXI NANNING HUATAI DELONG INFORMATION TECH

Cloud manufacturing service combination optimization method based on improved genetic algorithm

ActiveCN112801387AInhibit premature maturityOvercome Metric Inconsistency Between ValuesForecastingArtificial lifeLocal optimumMathematical model
The invention discloses a cloud manufacturing service combination optimization method based on an improved genetic algorithm. The method comprises the steps: synthesizing the execution time and execution cost of a task according to a user request on the basis of a QoS evaluation model of a cloud manufacturing service combination, combining the objective functions, such as a service configuration degree, a combination collaboration degree, a combination entropy, and the like, establishing a mathematical model for cloud manufacturing service combinatorial optimization, using an improved genetic algorithm for searching, and providing a multi-objective optimization solution for the cloud manufacturing service combinatorial optimization problem. By means of the method, the initial population can keep good stability, in the early stage of the algorithm, the two-point crossover operation is adopted to expand the search space, and the population gene diversity is improved. In the later stage of the algorithm, single-point crossover operation is adopted, convergence is accelerated, search time is shortened, and therefore the problem of falling into local optimum is better avoided.
Owner:NANJING UNIV OF POSTS & TELECOMM
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