Patents
Literature
Hiro is an intelligent assistant for R&D personnel, combined with Patent DNA, to facilitate innovative research.
188 results about "Bat algorithm" patented technology
Efficacy Topic
Property
Owner
Technical Advancement
Application Domain
Technology Topic
Technology Field Word
Patent Country/Region
Patent Type
Patent Status
Application Year
Inventor
The Bat algorithm is a metaheuristic algorithm for global optimization. It was inspired by the echolocation behaviour of microbats, with varying pulse rates of emission and loudness. The Bat algorithm was developed by Xin-She Yang in 2010.
Method for determining optimal route of airway of unmanned aerial vehicle
InactiveCN104406593AImprove global search performanceAvoid premature convergenceNavigational calculation instrumentsMicrochiropteraBat algorithm
The invention provides a method for determining an optimal route of the airway of an unmanned aerial vehicle. According to the method, the threat of an operation area is more sufficiently considered, more efficient global searching ability is achieved and a more accurate flying route is provided for the unmanned aerial vehicle. The method comprises the following steps: by adopting a quantum encoding mode, changing the state of a basic quantum bit by using a quantum rotating gate and a quantum not-gate, and further updating the position of a bat individual. Because of the diversity of the quantum state, a quantum bat algorithm (QBA) is relatively high in global searching ability and an available or even optimal route avoiding the threat and limiting conditions can be found for the unmanned aerial vehicle. The experiment result shows that the quantum bat algorithm is an effective and stable method for solving the airway route planning problem of the unmanned aerial vehicle, and the search performance of the quantum bat algorithm is superior to that of other swarm intelligence algorithms.
Owner:GUANGXI UNIV FOR NATITIES
Unmanned aerial vehicle route planning method based on improved bat algorithm
Owner:SHENYANG AEROSPACE UNIVERSITY
Parameter selection method for support vector machine based on hybrid bat algorithm
The invention discloses a parameter selection method for a support vector machine based on a hybrid bat algorithm. Regularization parameters and RBF kernel parameters have great influences on the learning performance and computation complexity. On the basis of analyzing the advantages and disadvantages of some classical parameter selection methods, an intelligent optimization algorithm is introduced to perform optimization on the parameters. The bat algorithm has the advantages of concurrency, high convergence speed and strong robustness. The bat algorithm is firstly utilized to perform optimization on the SVM parameters, then crossover, selection and mutation operators of differential evolution algorithm are introduced in allusion to a defect of early maturing of the bat algorithm, the position is further adjusted according to the three operators in each iteration process by using a bat individual, the search ability of the algorithm is enhanced, the algorithm is avoided from prematurely falling into a local optimal solution, and finally the SVM parameter selection is optimized by using an improved DEBA algorithm to obtain an excellent effect.
Owner:BEIJING UNIV OF TECH
Satellite-borne multi-beam reflector antenna forming method based on bat algorithm
The invention discloses a satellite-borne multi-beam reflector antenna forming method based on a bat algorithm, comprising the steps as follows: selecting the reflector size and feed source position according to the shape of a multi-beam coverage area; deploying a reflector antenna based on a multi-focus reflector equation; and introducing a bat algorithm to optimize the parameters of the multi-focus reflector equation, and accelerating a physical optics method through a GPU to calculate the pattern of the reflector antenna. According to the invention, the designed parameters are all optimized, and the computation time is reduced on the premise of guarantee accuracy.
Owner:NANJING UNIV OF SCI & TECH +2
Driving motor system performance evaluation method for electric vehicle
InactiveCN107085763AAvoid subjective judgmentImprove scienceResourcesNeural learning methodsMicrochiropteraNetwork model
The invention discloses a driving motor system performance evaluation method for an electric vehicle. The driving motor system performance evaluation method analyzes from different dimensions such as motor control performance, motor body design and enterprise qualification and ability of the driving motor system according to performance characteristics of a driving motor used for the electric vehicle, adopts an analytic hierarchy process to determine a driving motor performance evaluation index system and index weights thereof, establishes a BP neural network model for driving motor system performance evaluation, organically integrates a bat algorithm with a particle swarm algorithm to form a bat-particle particle swarm hybrid algorithm, and optimizes parameters of the neural network structural model by adopting the bat-particle particle swarm hybrid algorithm. Simulation examples show that, through training and testing data samples, the driving motor system performance evaluation method which optimizes the neural network based on the analytic hierarchy process and the bat-particle particle swarm hybrid algorithm has the advantages of fast evaluation speed and high accuracy rate, achieves satisfying evaluation results, and has certain promotion value in evaluation, selection and application of a driving motor system for the electric vehicle.
Owner:WUXI OPEN UNIV
Bus arrival time prediction method through optimizing support vector machine based on bat algorithm
InactiveCN105243428ASimple structureFew parametersRoad vehicles traffic controlForecastingData setAlgorithm
The invention provides a bus arrival time prediction method through optimizing a support vector machine based on a bat algorithm. Data of bus operation influencing factors are selected to act as the input variable of the SVM; normalization processing is performed on the data of the bus operation influencing factors; a kernel function is selected and SVM parameters are obtained, and the radial basis kernel is selected to act as the kernel function; the bat algorithm is constructed and the optimal parameters of the optimal support vector machine parameters c and g are searched; the obtained data after normalization processing are divided into three subsets: a training sample set, an inspection sample set and a test sample set, and a data set is inputted so that a prediction value is generated; and error analysis is performed on the prediction value, if error is less the preset value, the prediction value is the prediction result, and the process ends. The adopted bat algorithm has the characteristics of being simple in structure, less in parameter, high in robustness, easy to understand and easy to program so that the bat algorithm is combined with the SVM, and thus the parameters are optimized and prediction accuracy is guaranteed.
Owner:TIANJIN MUNICIPAL ENG DESIGN & RES INST
Novel bat optimization algorithm system
The invention discloses a novel bat optimization algorithm system (Iterated local search and stochastic inertia weight bat algorithm system, ILSSIWBAS), comprising: a bat algorithm operation module, a disturbance module and an iterative local search module, a judgment global optimal solution module, a global optimal solution module, and a global optimal solution module. The optimal solution storage module; through the bat algorithm operation module, run the bat algorithm to obtain the local optimal solution; on the basis of the local optimal solution, through the disturbance module, add disturbance, according to the local iterative part iterative search module, use the iterative local search algorithm The global optimal solution of the population position is searched, and when the judgment conditions of the global optimal solution are met, the global optimal solution is obtained, and the global optimal solution is stored in the global optimal solution storage module. The system proposed by the invention mainly solves the problems that the existing bat optimization algorithm is easy to fall into local optimum and the optimization result is unstable, and improves the optimization accuracy of the optimization algorithm and the stability of the optimization result.
Owner:CHINA UNIV OF GEOSCIENCES (WUHAN)
Output power prediction method, device and apparatus of photovoltaic power generation system and medium
InactiveCN108985521AImprove receptivityOvercoming Mode Aliasing DisadvantagesForecastingAc network circuit arrangementsLearning machineDecomposition
The embodiment of the invention discloses an output power prediction method, device and apparatus of a photovoltaic power generation system and a computer-readable storage medium. The method comprisesthe following steps: decomposing the historical output power data of a photovoltaic power generation system in a preset time period by an integrated set empirical mode decomposition, inputting the decomposed sub-sequences and corresponding meteorological data into a pre-constructed kernel limit learning machine prediction model, and determining the output power prediction value of the photovoltaic power generation system according to the prediction results of each sub-sequence output by the kernel limit learning machine prediction model. The historical photovoltaic power data is decomposed byusing a complete set of empirical modes, the nonstationarity of the photovoltaic sequence is suppressed and the prediction accuracy of the output power is improved. Through the good generalization performance and fast learning speed of the kernel limit learning machine, the prediction accuracy and efficiency can be further improved. The improved bat algorithm is used to optimize the kernel parameters and penalty coefficients of the kernel limit learning machine, which greatly improves the accuracy of power prediction.
Owner:GUANGDONG UNIV OF TECH
Method, device and system for traffic flow prediction based on Bat algorithm
InactiveCN107103397AImprove search abilityWide range of searchForecastingArtificial lifeBat algorithmTraffic flow
The embodiment of the invention discloses a method, device and system for traffic flow prediction based on the Bat algorithm. The method comprises: obtaining traffic flow data; employing a wavelets neural network traffic flow prediction model established in advance to perform processing and obtain a traffic flow prediction result, wherein the wavelets neural network traffic flow prediction model is formed through training based on the Bat algorithm, and the training process comprises calculation of initialization wavelets neural network parameters according to the historical data and the Bat algorithm; and employing the wavelets neural network and the historical data to perform training of the initialization wavelets neural network parameters to obtain the wavelets neural network traffic flow prediction model. Therefore, according to the embodiment of the invention, when the wavelets neural network traffic flow prediction model trained through adoption of the initialization wavelets neural network parameters obtained based on the Bat algorithm is used for prediction of the traffic flow, the prediction speed and the prediction precision are improved to a certain extent.
Owner:GUANGDONG UNIV OF TECH
WSAN actuator task distribution method based on BA-BPNN data fusion
ActiveCN106384153AHas practical valueImprove convergence rateArtificial lifeNeural learning methodsNerve networkActuator
The invention discloses a WSAN actuator task distribution method based on BA-BPNN data fusion, and the method employs a BA optimization BP neural network to build a data fusion model. The method specifically comprises the steps: employing a bat algorithm to optimize the weight value and threshold value of the BP neural network, building a data fusion model, carrying out the data fusion of the sensor node information, and obtaining the task distribution information of an actuator node. The bat algorithm is a meta heuristic type group intelligent optimization algorithm, employs an echo positioning method of a miniature bat under the condition of different transmitting speeds and responses, can achieve a precise capturing and obstacle avoidance random search algorithm. The BP neural network is a multilayer feedforward neural network which can search a global optimal value in a training process, and can increase the convergence rate of the network. The method searches the optimal parameter of the BP neural network through the positioning updating of bats, is more precise in data fusion, and is more reasonable in task distribution of an actuator.
Owner:HOHAI UNIV CHANGZHOU
Bat algorithm support vector machine-based highway traffic state recognition method
ActiveCN108765951AImprove recognition accuracyFast convergenceDetection of traffic movementSupport vector machineData set
The invention relates to a bat algorithm support vector machine-based highway traffic state recognition method. The method includes the following steps that: S1, traffic state parameter data and running state data are obtained, and data sets are divided into a training set and a test set; S2, the parameters of a support vector machine are set, a bat population is constructed and initialized, an optimal bat position and a fitness value are calculated; S3, bat algorithm parameters are updated, a random number is generated for each bat individual, if rand1 is larger than R<t>i, random disturbanceis generated near an optimal solution, thus, the method shifts to local search; S4, a genetic algorithm is adopted to optimize the bat individuals; S5, a random number is generated for each bat individual, if rand2 is smaller than A<t>i, and fi is larger than f<*>, a pulse rate and loudness are updated; S6, the bats are rearranged, so that an xbest is obtained, whether a maximum number of iterations is reached is judged, and the optimal penalty parameters c and g of the support vector machine are determined; and S7, the training set is inputted into the support vector machine model so as to perform training, and an outputted predicted state is compared with the state of the test set, so that recognition accuracy can be calculated.
Owner:GUANGDONG UNIV OF TECH
Improved bat algorithm based method for solving multi-objective active power dispatch of power system
InactiveCN108932566AImprove global search performanceIncrease diversityForecastingArtificial lifeWeight coefficientBat algorithm
The invention provides an improved bat algorithm based method for solving a multi-objective active power dispatch problem of a power system. According to the invention, a bat algorithm for handling the multi-objective active power dispatch problem is proposed and is improved by utilizing an inertia weight coefficient and a global optimum guidance mechanism. The improved algorithm can handle the multi-objective problem effectively, can search an even-distribution Pareto Optimality front end and can search an optimal compromise solution by utilizing fuzzy subordination. According to the invention, the improved bat algorithm adopts congestion distance and non-dominated sorting for maintaining the even distribution of the Pareto Optimality front end and adopts the fuzzy mechanism to determinethe optimal compromise solution. The improved algorithm has a good optimization effect in an aspect of solving the multi-objective active power dispatch problem of the power system and is high in search efficiency and achieves even distribution of disaggregation of the Pareto Optimality front end.
Owner:CHONGQING UNIV OF POSTS & TELECOMM
Bat optimization algorithm based on iterated local search and stochastic inertia weight
InactiveCN107886158AImprove search accuracyImprove stabilityArtificial lifeLocal optimumBat algorithm
The invention discloses a bat optimization algorithm (Iterated local search and stochastic inertia weight bat algorithm, ILSSIWBA) based on iterative local search and random inertia weight. The main steps include: initializing parameters in the bat algorithm; updating pulse frequency, population position, And use the random weight to update the population speed; run the bat algorithm to get the optimal solution; on the basis of the optimal solution, add disturbance, use the iterative local search algorithm; judge whether it meets the judgment conditions of the global optimal solution? If satisfied, the global optimal solution is obtained, and the algorithm ends. The method proposed by the invention mainly solves the problems that the existing bat optimization algorithm is easy to fall into local optimum and the optimization result is unstable, and improves the optimization accuracy of the optimization algorithm and the stability of the optimization result.
Owner:CHINA UNIV OF GEOSCIENCES (WUHAN)
Request scheduling and optimization method for spatial detection in distributed green cloud data center
ActiveCN108123995AScheduling intelligenceGuaranteed Average Request Latency RequirementsData switching networksMicrochiropteraGeolocation
The invention discloses a request scheduling and optimization method for spatial detection in a distributed green cloud data center. The method comprehensively considers changes of factors, such as electric energy price, wind speed, solar radiation strength and field air density generated by a thermal power generation mode, at different geographical locations. Aiming at the requests of a pluralityof applications, the method builds a framework for processing multiple types of application requests under a distributed green cloud data center environment, and accordingly, a non-linear constraintoptimization model of request scheduling of an overall cost of a cloud provider, is built, and a penalty function is designed to convert the non-linear constraint optimization model into an unrestraint optimization model, then a mixed element heuristic optimization algorithm based on simulated annealing and bat algorithms is used for solving the model, and thus request scheduling of spatial detection under the distributed green cloud data center environment is achieved. According to the method provided by the invention, all reached application requests can be scheduled to a plurality of greencloud data centers for executing, so that the overall cost of the cloud provider is minimized and the delay time requirements of all application requests are ensured.
Owner:BEIJING JIAOTONG UNIV
Permanent magnet synchronous motor rotation speed controller based on recursive fuzzy neural network
InactiveCN107968613AFast searchStrong global search capabilitySoftware algorithm controlArtificial lifeNeural network controllerSpeed control system
The invention discloses a permanent magnet synchronous motor rotation speed controller based on a recursive fuzzy neural network. The permanent magnet synchronous motor rotation speed controller combines a bat algorithm and an artificial bee colony algorithm to form a bat-artificial bee colony hybrid algorithm, which is used for optimizing structural parameters of a recursive fuzzy neural networkcontroller, and introducing the recursive fuzzy neural network controller into a rotation speed control system of a permanent magnet synchronous motor. A simulated and experimental analysis shows that: by adopting the recursive fuzzy neural network rotation speed controller optimized based on the bat-artificial bee colony hybrid algorithm, rapid response of a permanent magnet synchronous motor control system can be realized without overshoot, the control precision is high, the robustness is good, the anti-interference capability is high, and precise rotation speed control can be realized.
Owner:WUXI OPEN UNIV
Three-dimensional on-chip network test planning method
ActiveCN106503333AUnified Static ModelingUnified Dynamic OptimizationSoftware testing/debuggingDesign optimisation/simulationTest efficiencyBat algorithm
The invention discloses a three-dimensional on-chip network test planning method. A time Petri network model is established in combination with the characteristics of 3D NoC testing, a change excitation sequence serves as a parallel test task planning scheme, sequential scheduling optimization is carried out based on test path allocation through an improved two-stage hierarchical bat algorithm, and test resources are reasonably and effectively allocated to various IP cores. The model visually describes the 3D NoC test planning problem, the 3D NoC test time can be effectively shortened, test efficiency can be improved, and test effectiveness can be guaranteed. The test planning algorithm has certain advantages on the aspects of the quality of solutions and the convergence rate, the efficiency of parallel testing can be effectively improved, and the test time can be shortened.
Owner:GUILIN UNIV OF ELECTRONIC TECH
Green dynamic scheduling method for flexible production
InactiveCN110618668AHigh energy consumptionReduce energy consumptionTotal factory controlProgramme total factory controlBat algorithmPhysical system
The present invention discloses a green dynamic scheduling method for flexible production, in order to solve the technical problem of poor practicality of the existing dynamic scheduling method for the flexible production. The technical scheme comprises: obtaining initial data parameters of production, and using an information physical system to enter basic information such as equipment, personnel, orders, equipment power consumption, materials and the like as initial calculation parameters for the scheduling; designing a green scheduling algorithm, taking the minimal processing time and the minimum production energy consumption as objectives, based on a Pareto multi-objective optimization theory and an energy consumption model of a manufacturing process, using the equipment load balancingstrategy, and using the bat algorithm to calculate and execute the scheduling scheme; and finally, implementing a dynamic scheduling strategy, using an event-driven strategy to determine whether disturbance events such as the machine failure, the order change, and the like occur, and if so, updating the data, and re-calling and executing the green scheduling algorithm until the entire productionis completed, so that green dynamic scheduling for the production is achieved, and good practicability is realized.
Owner:NORTHWESTERN POLYTECHNICAL UNIV
Rolling bearing fault diagnosis method based on optimized variational mode decomposition
ActiveCN112345249AQuickly find the optimal solutionAvoid manual determinationMachine part testingKernel methodsAlgorithmMarginal spectrum
The invention provides a rolling bearing fault diagnosis method based on optimized variational mode decomposition, and the method comprises the steps: selecting 4096 sampling points of an original vibration signal as input signals of variational mode decomposition; optimizing the modal number and the secondary penalty factor of variational modal decomposition by adopting an improved bat algorithmand taking the minimum average envelope entropy as an optimization target; decomposing the original vibration signal by using the optimized parameters, and solving an energy entropy and an energy spectrum entropy of a decomposed component; taking the kurtosis, the correlation coefficient and the marginal spectrum entropy as screening criteria to screen the components, and solving main frequency distribution characteristics of the reserved components; and taking the energy entropy, the energy spectrum entropy and the main frequency distribution characteristics as characteristic vectors and inputting the characteristic vectors into a support vector machine so as to realize fault diagnosis. According to the method, the variational mode decomposition parameters are optimized through the improved bat algorithm, and the feature vectors are obtained according to the optimized parameters, so that manual parameter determination is avoided, the optimal solution can be found more quickly, and therecognition rate of the fault state is improved.
Owner:JIANGSU UNIV OF TECH
Criminisi image restoration method based on bat algorithm
InactiveCN103955906AMeet visual needsGuaranteed repair qualityImage enhancementBat algorithmImage restoration
A Criminisi image restoration method based on a bat algorithm comprises the steps of calculating the priority of each pixel point of to-be-restored region edges of to-be-restored images, selecting a pixel point with maximum priority as a pixel point with restoration priority, performing searching and filling on best matching blocks in perfect regions of the to-be-restored images, wherein searching (as mentioned) is performed by adopting the bat algorithm according to a matching principle; updating the to-be-restored region edges, returning the edges and performing repeated circulating operation till the restoration of to-be-restored regions is finished and obtaining an image restoration result. The Criminisi image restoration method is wide in image restoration range, improves the restoration speed, reduces the time consumption and meets the visual demands of people on the premise that the restoration quality of the to-be-restored images having different emphasis areas is ensured. Therefore, the Criminisi image restoration method has important practical significance.
Owner:WUHAN UNIV OF SCI & TECH
Short-term wind speed prediction method based on improved empirical modal decomposition and support vector machine
PendingCN110991721AOvercome Mode Aliasing PhenomenonImproved decomposition is incompleteKernel methodsForecastingBat algorithmEngineering
The invention provides a combined short-term wind speed prediction method based on improved empirical modal decomposition CEEMDAN and a bat algorithm BA optimization support vector machine SVM. CEEMDAN is adopted to decompose an original wind speed time sequence, and a BA-SVM model is adopted to independently predict each sub-sequence obtained through decomposition; and finally, all obtained prediction results sum to obtain a wind speed prediction value. According to the invention, the original wind speed time sequence is accurately reconstructed; the modal aliasing phenomenon existing in theprior art is overcome; meanwhile, the defects of incomplete decomposition and increased calculation amount due to the fact that the reconstruction error is reduced by increasing the number of decomposition times in the prior art are remarkably overcome; parameters of a support vector machine are optimized by adopting a bat algorithm; each component is predicted by adopting a formed BA-SVM model; prediction results of the components are superposed; and the accuracy of wind speed prediction is greatly improved.
Owner:ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY +3
Two-stage optimization scheduling method supporting source-network-load-storage multivariate ubiquitous coordination
PendingCN112465181ALow-carbon economic dispatch improvesReduce the number of starts and stopsForecastingSystems intergating technologiesElectricity priceEngineering
The invention relates to a two-stage optimization scheduling method supporting source network load storage multivariate ubiquitous coordination. The method comprises the following steps: 1, a day-ahead stage: predicting next-day data according to historical data and a load uncertainty model considering demand side management; 2, taking low-carbon economy as a target, considering a deep peak regulation working condition and a normal operation working condition of the thermal power generating unit, carrying out random sampling by utilizing a Monte Carlo method, and solving a day-ahead low-carboneconomy scheduling model by utilizing a hybrid bat algorithm to obtain a low-carbon economy scheduling model; 3, in the intra-day stage, according to the ultra-short-term prediction values of the wind power plant and the photovoltaic power station and the intra-day system load considering the day-ahead price demand response, based on an intra-day thermal power generating unit correction model andan intra-day low-carbon economic dispatching model, determining the unit start-stop combination and the price and price type demand response quantity of each time period; and solving and adjusting the day-ahead scheduling plan by using a hybrid bat algorithm. The low-carbon economic dispatching of the power system is realized, the local optimum in a high-dimensional condition is effectively avoided, and the global optimal solution is quickly obtained.
Owner:ELECTRIC POWER SCI & RES INST OF STATE GRID TIANJIN ELECTRIC POWER CO +2
Antenna design optimization method and system based on bat algorithm
ActiveCN110232212AAvoid generatingRapid designArtificial lifeDesign optimisation/simulationAntenna designBat algorithm
The invention provides an antenna design optimization method and system based on a bat algorithm. The antenna design optimization method comprises the steps: setting a fitness function according to the type of an antenna, and determining a processing material and a manufacturing process according to the type of the antenna; setting ranges of known parameters and unknown parameter variables of theantenna according to a processing material and a manufacturing process, and initializing the unknown parameter variables according to a physical dimension range of the antenna; determining the physical size of the antenna according to the current population parameter, and calculating a fitness function through electromagnetic simulation to obtain a fitness value; iteratively updating population parameters based on a bat algorithm to obtain an offspring population and a current fitness value; judging according to a preset termination condition, judging whether the current fitness value meets the requirement or whether the number of iterations reaches the upper limit or not, and outputting if the current fitness value meets the requirement. According to the antenna design optimization method, the optimization efficiency can be improved, and rapid and automatic optimization design of the antenna with specific performance is realized.
Owner:SHANGHAI JIAO TONG UNIV
Dynamic fire allocation method based on DDE improved bat algorithm
ActiveCN108416421AReduce the chance of getting stuck in a local extremumImprove global search performanceArtificial lifeResourcesMicrochiropteraBat algorithm
The invention discloses a dynamic fire allocation method based on a DDE improved bat algorithm. A dynamic fire allocation model is determined and then generation of the individual bat is initialized by softening certain constraint conditions, and then the differential variation mechanism of the dynamic differential evolution algorithm is fused into the bat algorithm so as to optimize the convergence accuracy and the convergence rate of the dynamic fire allocation problem solution and provide the better auxiliary combat decision for the commander.
Owner:DALIAN UNIV
Bat algorithm-based control parameter self-tuning method for AC servo speed regulation system
ActiveCN110824921AFlexible choiceTaking into account the speed of responseAdaptive controlParametric searchSelf-tuning
The invention discloses a bat algorithm-based control parameter self-tuning method for an alternating current (AC) servo speed regulation system. The method comprises the following steps of step one,setting corresponding linear weighting performance indexes and setting a parameter search space according to different application working conditions and performance preferences; step two, collectingsignal parameters of the AC servo speed regulation system for calculating the performance index of the current system; step three, establishing a fitness evaluation function and initializing relevantparameters; and step four, searching for a control parameter capable of realizing the optimal system performance by using a bat algorithm. Through the method disclosed by the invention, the difficultand time-consuming manual parameter tuning process is avoided; and compared with the controller parameter tuning method of the servo system at this stage, the operation is simple, the adaptability isgood, and the comprehensive performance demands under multiple performance indexes can be particularly guaranteed.
Owner:HUAZHONG UNIV OF SCI & TECH
Drilling trajectory design method and system based on bat algorithm and borehole wall stability
ActiveCN108894768ASpecial data processing applicationsBorehole/well accessoriesTrial and errorBat algorithm
The invention discloses a drilling trajectory design method and system based on a bat algorithm and borehole wall stability. The optimization work is carried out in three steps, firstly, analytic modeling is carried out on a three-dimensional drilling trajectory, and a target function and equality constraint conditions of a drilling trajectory optimization model are obtained; then inequality constraint conditions of the drilling trajectory optimization model are obtained through borehole wall stability analysis; and lastly, an intelligent bat search algorithm is utilized to optimize parameters. The method overcomes the defects that the key formation environment parameter of the borehole wall stability and the intelligent bat search algorithm are not formed into a unified framework in a previous drilling trajectory design optimization method, and massive calculation is carried out by using a manual trial-and-error method. Compared with a traditional manual calculation and genetic intelligence algorithm, the drilling cost index and design time of the borehole trajectory design are reduced, the design precision is improved, and a good foundation is laid for trajectory optimization control in the process of geological exploration drilling.
Owner:CHINA UNIV OF GEOSCIENCES (WUHAN)
Active distribution network reliability fast evaluation method based on improved AdaBoost. M1-SVM
The invention discloses an active distribution network reliability fast evaluation method based on an improved SVM. The method introduces the improved AdaBoost. M1-SVM algorithm into the distributionnetwork reliability evaluation. The improved AdaBoost. M1-SVM algorithm uses AdaBoost technology to integrate multiple SVM weak classifiers. In the improved AdaBoost. M1-SVM algorithm, the bat algorithm is used to optimize the c-parameter and g-parameter of SVM in the training process, and the local search is introduced, which has better searching ability. The invention weakens the error weight ofmissed judgment samples, minimizes the total number of misjudgment samples, and overcomes the defect that a single classifier can not achieve effective balance in classification accuracy and generalization ability.
Owner:国网山东省电力公司聊城供电公司 +2
Complex network cell discovering method under adaptive evolution bat algorithm for self-media network data
ActiveCN106157154AAchieving Adaptive EvolutionThe division result is accurateData processing applicationsWeb data indexingAlgorithmLabel propagation
The invention provides a complex network cell discovering method under an adaptive evolution bat algorithm for self-media network data. The method includes the following steps that S1, mass data is acquired, a network structure model is established, according to the bat algorithm, a modularity function serves as a fitness function, a coding mode based on characters is adopted, and an initialized population is improved through a label propagation method; S2, the individual speed of the bat algorithm is converted into a mutation probability value, position update is calculated through a cross operator and a mutation operator, therefore, adaptive evolution of the common bat algorithm is achieved, the adaptive evolution bat algorithm is used for dividing a network, and a more accurate network cell division result is obtained. Compared with other intelligent algorithms for cell discovery, the algorithm has the advantages of being high in convergence rate and solution precision, and is more suitable for cell discovery under the large-scale network.
Owner:CHONGQING UNIV
Improved bat algorithm optimization ELM-based fault diagnosis method of engine fuel system
InactiveCN109163911AExpress clearly and accuratelyThe fault diagnosis model is reasonable and effectiveInternal-combustion engine testingFeature vectorDiagnosis methods
The invention relates to an improved bat algorithm optimization ELM-based fault diagnosis method of an engine fuel system, and belongs to the technical field of mechanical engineering automation. Thefault diagnosis method comprises the steps of acquiring a vibration signal of the fuel system in real time by a sensor, and extracting a characteristic vector of the sensor; optimizing a weight and athreshold of an extreme learning machine by employing a bat algorithm, and building a network structure of the optimized extreme learning machine; and inputting the characteristic vector obtained by extraction to the optimized extreme learning machine for training to obtain a fault diagnosis model of the engine fuel system, and performing fault diagnosis of the engine fuel system. The fault diagnosis method has the advantages that the classification speed is rapid, and the fault diagnosis accuracy of the engine fuel system is improved.
Owner:KUNMING UNIV OF SCI & TECH
Transfer function model parameter recognition method and device based on improved particle swarm algorithm
InactiveCN111428849AImprove search accuracyOvercome the shortcoming of easy to fall into local optimumArtificial lifeDesign optimisation/simulationTransfer function modelBat algorithm
The invention discloses a transfer function model parameter recognition method and device based on an improved particle swarm algorithm. A coevolution idea and a Gaussian disturbance strategy are introduced into a basic particle swarm optimization algorithm, a hybrid algorithm is formed with a bat algorithm under a coevolution framework, and a Gaussian disturbance term is added in the optimizationprocess to form a hybrid coevolution Gaussian particle swarm optimization algorithm; sampling the input and output of the to-be-identified object model and the estimation model, and solving the standard deviation between the actual output value of the system and the output value of the estimation model at the moment k; feeding back the standard deviation to an HCGPSO algorithm to obtain an optimal result of the current parameter; and replacing the original value with the optimal value of the current model parameter, updating the estimation model, and sequentially iterating until the requirement of an output recognition criterion is met, thereby realizing parameter recognition of the transfer function model.
Owner:NARI TECH CO LTD +4
Active distribution network optimal dispatching method taking into account the interests of different agents
InactiveCN109217351ASafe and stable operationImprove economySingle network parallel feeding arrangementsAc network load balancingTOPSISTap changer
This paper discloses an active distribution network optimal dispatching method which takes into account the interests of different agents, includes the following steps: considering schedulable distributed generation, Energy storage, on-load tap changer, active control and management of packet switching capacitors and demand side resources, While considering environmental benefits, Taking into account the interests of distribution companies and distributed generation companies, an active distribution network day-ahead optimal dispatching model is established to minimize the operating cost of distribution companies, maximize the net income of distributed generation companies and minimize the emission of pollutants, and an improved bat algorithm based on diversity strategy is proposed to solve the problem. The entropy-weighted TOPSIS method is used to make comprehensive decision, and the optimal scheduling scheme is selected from the Pareto solution set.
Owner:NORTH CHINA ELECTRIC POWER UNIV (BAODING)
Features
- R&D
- Intellectual Property
- Life Sciences
- Materials
- Tech Scout
Why Patsnap Eureka
- Unparalleled Data Quality
- Higher Quality Content
- 60% Fewer Hallucinations
Social media
Patsnap Eureka Blog
Learn More Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.
© 2025 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com