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607 results about "Differential evolution" patented technology

In evolutionary computation, differential evolution (DE) is a method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Such methods are commonly known as metaheuristics as they make few or no assumptions about the problem being optimized and can search very large spaces of candidate solutions. However, metaheuristics such as DE do not guarantee an optimal solution is ever found.

System and method for designing a free form reflector

A system for designing a free form reflector includes a user input interface (1), a free form reflector design unit (2), and a free form reflector output unit (3). The user input interface is configured for receiving various data associated with a desired free form reflector, via an input device. The free form reflector design unit is installed in a computer and configured for generating an optimum free form surface according to the input data by performing a non-uniform rational basis splines (NURBS) algorithm, a merit evaluation function, and a differential evolution (DE) algorithm. The free form reflector output module is configured for generating a free form reflector according to the optimum free form surface and outputting the free form reflector, in the form of a computer-aided design (CAD) drawing, to a display and / or a printer. A related method is also disclosed.
Owner:TSINGHUA UNIV +1

Quick feedback analyzing system in tunnel constructing process

InactiveCN102155231AOvercoming the blindness of pre-designDynamic information construction improvementMining devicesTunnelsEngineeringAlgorithm optimization
The invention discloses a quick feedback analyzing system in a tunnel constructing process. The system adopts a scheme: understanding currently adopted designing construction parameters; establishing a tunnel excavation three-dimensional finite element numerical grid calculation model; acquiring surrounding rock layering and convergent displacement monitoring information after a tunnel is excavated; establishing a non-linear support vector machine model; fixing an anchoring parameter according to the actual construction parameter, and optimally identifying rock mechanic parameters by adoptinga differential optimization algorithm; optimizing the construction parameter of an anchoring scheme by adopting a differential evolution algorithm; and optimizing the rock mechanic parameters by calling the differential evolution and optimization algorithms to further solve the construction parameter of the anchoring scheme, and outputting the construction parameter of the optimized anchoring scheme as a construction scheme through a computer display screen to guide the constructors to construct. The quick feedback analyzing system ensures that the monitoring information is used for optimizing the anchoring parameter while being used for identifying the surrounding rock parameters, so that the dynamic information construction is improved to a level of quantitative analysis.
Owner:DALIAN MARITIME UNIVERSITY

Related logistics transportation optimized dispatching method with time-varying time window

The invention relates to a related logistics transportation optimized dispatching method with a time-varying time window. The related logistics transportation optimized dispatching method with the time-varying time window comprises the specific following steps of 1 obtaining demand information of a reservation client through a distribution center; 2 performing initial path planning on the known client through a II insertion method; 3 improving a bee algorithm and optimizing an initial path based on a differential evolution algorithm; 4 collecting time varying information through the distribution center, obtaining new time window information of the client and recording application time of the new time window of the client; judging a key point at the time when the new information comes; 6 inserting the time-varying client to the initial path which is to be solved in the step 3 through the improved II insertion method to achieve optimization of the time-varying path; 7 ending. According to the related logistics transportation optimized dispatching method with the time-varying time window, a final dispatching scheme for related logistics transportation dispatching with the time-varying time window is obtained.
Owner:GUANGDONG YIFU NETWORK TECH

Method and system for multi-target reactive power optimization of electric power systems

The invention discloses a method and system for multi-target reactive power optimization of electric power systems. The method comprises the following steps of: establishing a multi-target reactive power optimization model; generating positions of N initial bird nests by utilizing Kent chaotic mapping, taking the positions of the N bird nests as initial populations, calculating a fitness value of each bird nest, establishing an external file set according to a Pareto dominance relation, updating the positions of the bird nests according to self-adaptive weights, updating the external file set according to the dominance relation and calculating a congestion distance to control the capacity of the file set; carrying out a differential evolution operation on each bird nest and updating the external file set; and when an iteration termination condition is satisfied, outputting an optimum Pareto optimal solution set. According to the method and system, a plurality of target functions are considered, so that the disadvantages that the traditional method is used for converting a plurality of targets into a single target and is difficult to determine the weight coefficients are optimally overcome; an improved cuckoo search algorithm is high in convergence rate, high in precision and good in individual diversity; and the obtained optimal solution set has favorable diversity and uniform distributivity, and can be well adapted to solving the multi-target reactive power optimization problems of the electric power systems.
Owner:GUANGDONG UNIV OF TECH

Active power distribution network double-layer planning method considering demand side response uncertainty

The invention discloses an active power distribution network double-layer planning method considering demand side response uncertainty. The content includes building a time sequence model of a wind turbine generator and a photovoltaic cell with consideration given to uncertainty of a wind power generation and photovoltaic power generation; building a time sequence model of a load with consideration given to randomness of power utilization of the load; making an analysis of demand side response uncertainty of a direct load control type, and building a cost function; building an upper layer planning model of active power distribution network double-layer planning considering the demand side response uncertainty, and adopting a differential evolution algorithm to solve the upper layer planning model; and building a lower layer planning model of the active power distribution network double-layer planning considering the demand side response uncertainty and adopting a non-dominated sorting genetic algorithm to solve the lower layer planning model. The active power distribution network double-layer planning method performs planning of an active power distribution network, can effectively reduce the comprehensive cost of the power distribution network, better meets the running characteristic of the active power distribution network, and is high in practicability.
Owner:YANSHAN UNIV

Sensor target assignment method and system for multi-objective optimization differential evolution algorithm

The invention discloses a sensor target assignment method for a multi-objective optimization differential evolution algorithm. The method includes the steps that objective importance degree calculation is carried out according to objective information, a sensor target assignment constraint multi-objective optimization function is built, distribution scheme codes and initial population chromosomes are generated, offspring scheme populations are generated through the differential evolution algorithm, population combination and screening are carried out, and a distribution scheme Pareto front-end solution set is obtained. The method is combined with the differential evolution algorithm, is easy to use in terms of population difference heuristic random search, is good in robustness and has the advantages of being high in global search ability and the like. A Pareto set multi-objective optimization assignment strategy is provided. A sensor utilization rate function is added on the basis of a sensor target monitoring efficiency function, an assignment problem is converted into a multi-objective optimization problem, sensor resources can be saved as much as possible on the condition that monitoring precision requirements are met, and reasonable and effective assignment of the sensor resources is achieved.
Owner:NO 709 RES INST OF CHINA SHIPBUILDING IND CORP

Scheduling method and system based on improved variable neighborhood search and differential evolution algorithm

The present invention relates to a production batch scheduling method and system based on improved variable neighborhood search and differential evolution algorithms as well as a storage medium. The method is executed by a computer, and includes: setting algorithm parameters; building a neighborhood structure; initializing a population; determining an initial solution; calculating a fitness value; locally searching; selecting a male parent; performing individual recessive variation; regenerating the population; updating the initial solution; updating a neighborhood structure of algorithmic search; and judging whether a terminal condition of algorithm execution is met, outputting a global optimal solution of the algorithmic search if so, otherwise, returning to Step 6. The present invention can obtain an approximate optimal solution in regard to a coordinated batch scheduling problem of production and transportation in the case of a manufacturer single machine based on a differential workpiece.
Owner:HEFEI UNIV OF TECH

Automatic identification method of foliar disease image of greenhouse vegetable

Provided is an automatic identification method of a foliar disease image of a greenhouse vegetable. The automatic identification method of the foliar disease image of the greenhouse vegetable comprises the steps of carrying out image collection on a foliar disease of the greenhouse vegetable, automatically generating a threshold, carrying out estimation by using a two-dimensional maximum entropy principle and combining the average grey degree grade and the intra-neighborhood grey degree grade of an image, optimizing the automatically-generated threshold by using a differential evolution algorithm, using an average value of results obtained through more than 30 times of differential evolution algorithm optimization which is independently carried out to serve as a threshold for image segmentation, carrying out segmentation on the known foliar disease image of the greenhouse vegetable by using the threshold, obtaining an image of the area of a disease speck, analyzing features of the disease speck, obtaining feature parameters such as the color, the texture and the shape of the disease speck of the foliar disease image of the greenhouse vegetable, carrying out fusion on the features of the disease speck, and carrying out disease type feature identification. The automatic identification method of the foliar disease image of the greenhouse vegetable can achieve rapid and effective diagnosis of the foliar diseases in a greenhouse without damage to sick leaves of the greenhouse vegetable, and can be well applied to disease monitoring of the greenhouse vegetable.
Owner:TIANJIN AGRICULTURE COLLEGE

Abstract convex lower-bound estimation based protein structure prediction method

Disclosed is an abstract convex lower-bound estimation based protein structure prediction method. The method includes: firstly, aiming for high-dimensional conformational spatial sampling problems for proteins, adopting a series of transform methods to transform an ECEPP / 3 force field model into an increasing radial convex function in unit simple constraint conditions; secondly, based on an abstract convex theory, proving and analyzing to give out a supporting hyperplane set of the increasing radial convex function; thirdly, constructing a lower-bound underestimate supporting plane on the basis of population minimization conformation subdifferential knowledge under a differential evolution population algorithm framework; fourthly, by the aid of a quick underestimate supporting plane extreme point enumeration method, gradually decreasing a conformational sampling space to improve sampling efficiency; fifthly, utilizing the lower-bound underestimate supporting plane for quickly and cheaply estimating an energy value of an original potential model to effectively decrease evaluation times of a potential model objective function; finally, verifying effectiveness of the method by methionine-enkephalin (TYR1-GLY2-GLY3-PHE4-MET5) conformational spatial optimization examples. The abstract convex lower-bound estimation based protein structure prediction method is high in reliability, low in complexity and high in computation efficiency.
Owner:ZHEJIANG UNIV OF TECH

Method for predicting protein three-dimensional structure based on Monte Carlo local shaking and fragment assembly

The invention discloses a method for predicting a protein three-dimensional structure based on Monte Carlo local shaking and fragment assembly. The method comprises the following steps that firstly, according to the difficult problem that search space of protein high-dimensional conformation space is complex, the effectiveness of fragment replacement is judged under a Rosetta force field model through the Monte Carlo statistical method according to a protein database configuration fragment bank; under a differential evolution group algorithm framework, the complexity of the search space is reduced through fragment assembly, meanwhile, false fragment assembly is removed through the Monte Carlo statistical method, and the conformation search space is gradually reduced through the diversity of an evolutionary algorithm, and therefore the searching efficiency is improved; meanwhile, a module with coarseness is adopted, a side chain is ignored, and cost of a search is effectively reduced. The method for predicting the protein three-dimensional structure based on Monte Carlo local shaking and fragment assembly can effectively obtain optimal local stable conformation and is high in predicting efficiency and good in convergence correctness.
Owner:ZHEJIANG UNIV OF TECH

Comprehensive evaluation method for peak shaving schemes of gas pipe network and gas storage

The invention relates to the technical field of natural gas peak shaving operation, in particular to a comprehensive evaluation method for peak shaving schemes of a gas pipe network and a gas storage. The method comprises the steps of 1, predicting a city gas load: building a city gas load prediction model by adopting an artificial neural network model, and predicting the city gas load subjected to peak shaving by using a differential evolution extreme learning machine algorithm, thereby determining a peak shaving quantity; 2, performing peak shaving optimization on the gas storage: according to previous peak shaving operation experience of the gas storage, fitting out a relational expression of operation parameters of the gas storage and the peak shaving quantity, and obtaining a gas recovery rate of the gas storage under a certain peak shaving quantity; 3, simulating a peak shaving quantity of the pipe network, and obtaining preselected peak shaving schemes; and 4, comprehensively evaluating the peak shaving schemes: comprehensively evaluating different peak shaving schemes to obtain an optimal peak shaving scheme. According to the method, the conditions such as peak gas consumption of users, peak shaving capability of a pipeline, peak shaving capability of the gas storage and the like are comprehensively considered, so that the optimality and scientificity of making and arranging the peak shaving schemes are effectively improved.
Owner:CHINA PETROCHEMICAL CORP +2

Differential evolution random forecast classifier-based photovoltaic array fault diagnosis method

The invention relates to a differential evolution random forecast classifier-based photovoltaic array fault diagnosis method. The method comprises the steps of firstly, collecting photovoltaic array voltages under various working conditions and currents of photovoltaic strings, and performing identification on various working conditions by different identifiers; secondly, determining a quantity range of decision trees in a random forest model by adopting an out-of-bag data-based classification misjudgment rate mean value; thirdly, performing global optimization on the quantity range of the decision trees by utilizing a differential evolution algorithm to obtain an optimal decision tree quantity value; fourthly, substituting the calculated optimal decision tree quantity value into a randomforecast classifier, and training samples to obtain a random forecast fault diagnosis training model; and finally, performing fault detection and classification on a photovoltaic array by utilizing the training model. According to the method, the model training speed can be greatly increased while the optimal model classification accuracy is ensured, so that the fault detection and classificationof the photovoltaic power generation array are realized more quickly and accurately.
Owner:FUZHOU UNIV

Path coverage software test-based automatic test case generation method

The invention provides a path coverage software test-based automatic test case generation method. The method comprises the following steps of: analyzing a code of a tested program to obtain a controlflow graph (CFG) of the program and a path of the program; obtaining and calculating a fitness function of a group of corresponding test cases according to the control flow graph obtained in the laststep and a branch distance-based method; carrying out iterative updating on a randomly initialized population by using the fitness function through a differential evolution algorithm and a convergencespeed controller; and recording the first test case of paths corresponding to a coverage program until the test cases of all the paths are obtained or the iteration frequency of the algorithm exceedsa limit, and outputting a test case set of the paths corresponding to the coverage program. The method is capable of effectively utilizing the tested program to cover heuristic information of the test cases on the basis of the paths, and can be applied to practical software test work environment.
Owner:微科智检(佛山市)科技有限公司

Combined optimization method applied to irregularly-arranged sub-array four-dimensional antenna array

The invention discloses a combined optimization method algorithm to an irregularly-arranged sub-array four-dimensional antenna array. The definition of information entropy is introduced into a four-dimensional array, the originally-complicated optimization problem is divided into two sub-problems, optimization is performed according to two steps, in the first step, an information entropy-based genetic algorithm is employed, the array topology structure with maximum information entropy value is optimized according to a sub-array arrangement algorithm, in the second step, information such as a static excitation phase of each sub-array of the sub-arrays, the closing continuous time of a switch and the initial closing time of the switch is optimized by a differential evolutionary algorithm according to the requirement of low sideband and low side lobe, so that the whole optimization problem can be more efficiently solved. The maximum innovation lies in that the intrinsic characteristic ofthe original optimization problem is dug, optimization is performed by combining the information entropy-based generic algorithm and the differential evolutionary algorithm, the complexity of the original optimization problem is reduced, T / R modules are saved by half, and meanwhile, the characteristics of low sideband and low side lobe under large-angle scanning are ensured.
Owner:扬州市宜楠科技有限公司

Intelligent pipeline arrangement optimization method and system based on differential evolution algorithm

The invention discloses an intelligent pipeline arrangement optimization method and system based on a differential evolution algorithm. The method includes the steps of conducting mathematical modeling on a pipeline to be arranged and arrangement space so as to determine an arrangement object, the constraint condition and the evaluation criteria, coding the arrangement object through polar coordinates and posture vectors, conducting optimization solution on a pipeline arrangement optimization mathematical model according to the differential evolution algorithm, and conducting constraint condition verification and arrangement adjustment on the arrangement optimization scheme obtained through the solution so as to obtain a final arrangement scheme. By means of the intelligent pipeline arrangement optimization method and system based on the differential evolution algorithm, the design cycle can be greatly shortened, optimization performance can be enhanced, the purpose of arranging pipelines on a large scale within limited time can be achieved, and the method and the system have the advantages of being short in arrangement design time, high in optimization accuracy, capable of quantitatively evaluating the arrangement scheme and the like.
Owner:INST OF AUTOMATION CHINESE ACAD OF SCI

Scheduling method and system based on improved variable neighborhood search and differential evolution algorithm

The embodiments of the present invention relate to a scheduling method and system based on improved variable neighborhood search and a differential evolution algorithm. The method includes the following steps that: 1) the parameters of an algorithm are set; 2) neighborhood structures are constructed; 3) a population is initialized; 4) an initial solution is determined; 5) a fitness value is calculated; 6) local search is performed; 7) male parent selection is performed; 8) individual inversion variation is performed; 9) the population is updated; 10) the initial solution is updated; 11) a neighborhood structure for algorithm search is updated; and 12) whether the termination condition of the execution of the algorithm is satisfied is judged, the termination condition of the execution of the algorithm is satisfied, a global optimal solution for algorithm search is outputted, otherwise the method returns to step 6). With the method provided by the embodiments of the present invention adopted, an approximate optimal solution can be obtained according to the collaborative batch scheduling of production and transportation of a difference workpiece-based manufacturer under a stand-alonesituation, and therefore, an enterprise can make full use of its production resources to the greatest extent and reduce production cost, the service level of the enterprise can be improved, and customer satisfaction can be enhanced.
Owner:HEFEI UNIV OF TECH

RNN-based gene regulatory network construction and dynamic differential analysis method

An RNN-based gene regulatory network construction and dynamic differential analysis method comprises the first step of gene dynamic regulation network construction based on a deepRNN, the second step of time-sequence variation and evolution analysis based on an intra-subtype dynamic regulation network, and the third step of network differential evolution analysis based on an inter-subtype dynamic regulation network, wherein the evolution analysis of different subtype networks comprises kinetic analysis, differential analysis and perturbation analysis. The RNN-based gene regulatory network construction and dynamic differentiation analysis method is high in precision.
Owner:ZHEJIANG UNIV OF TECH

Method for dynamically re-dispatching job shop multi-process routes in batches based on two-stage differential evolution algorithm

InactiveCN101609334ASolve batch divisionSolving Scheduling Optimization ProblemsGenetic modelsTotal factory controlJob shopComputer science
The invention provides a method for dynamically re-dispatching job shop multi-process routes in batches based on a two-stage differential evolution algorithm, relating to the evaluation of a re-dispatching parameter, the construction of a dispatching model and the compilation of a dispatching proposal. The method comprises the following steps: setting up a multi-process routes batching dispatching model based on a period and event driven re-dispatching strategy, and providing the two-stage differential evolution algorithm to solve the model, so as to solve the problems of batching dividing and dispatching optimizing. Considering the condition that each work procedure of work pieces possibly has uneven capability in real environment, the method divides the each work piece in batches at the each work procedure, and the designed two-stage differential evolution algorithm solves the problems of batching dividing and dispatching optimizing, so as to effectively reduce the free time of a machine, shorten a production period, and be completely suitable for complex environment change in the process of machining and real-time treatment.
Owner:ZHEJIANG UNIV OF TECH

Batch reactor optimal control method based on single population and pre-crossed differential evolution algorithm

An optimal control method for a batch reactor of a differential evolution algorithm based on a single population and pre-crossover comprises the following steps: 1), an evolutional generation g is caused to be equal to 1, and an individual number i is caused to be equal to 1, and a parameter is initialized; 2), the population is initialized; 3) the highest fitness and the lowest individual fitness in S1 are respectively fmax and fmin; if |fmax minus fmin| is smaller than or equal to an eps, then the algorithm is stopped, and a final result is output; if not, g is caused to be equal to g plus 1, and i is caused to be equal to 1; whether an evolutional generation is maximized or not is judged; and if yes, the algorithm is stopped and a change curve of the yield of a target product is output, and if not, the algorithm is continued; 4) a pre-crossover operation is conducted; 5) i is caused to be equal to i plus 1; if i is equal to a pop, step 3) is returned, and if not, step 4) is returned; and 6), three individuals are selected from S1 at random to conduct an mutation crossover operation, and an acquired experimental individual is yi; if f(yi) is smaller than f(xi), yi is used for replacing xi; and if f(yi) is larger than f(xi) and f(yi) is smaller than f (xi'), yi is used to replace xi', and step 5) is returned. The optimal control method has the advantages of simple operation, high convergence rate and high searching ability.
Owner:ZHEJIANG UNIV OF TECH

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

Forecasting method for multi-stage differential evolution protein structure based on abstract bulge estimation

The invention relates to a forecasting method for a multi-stage differential evolution protein structure based on abstract bulge estimation. The method comprises the following steps: firstly, calculating the distance from each conformation individual in a current colony to a new conformation and performing ascending sorting according to the distance; then selecting the part of the new conformation individual close to a abstract bulge lower-limit estimation support surface of the conformation individual, thereby acquiring an energy lower-limit estimation value of the new conformation individual; calculating an average estimation error between the energy lower-limit estimation value of all the new conformation individuals and a practical energy value; dividing the whole algorithm into a plurality of optimizing stages according to the change in the average estimation error; judging the stage of the present iteration according to the average estimation error in the last iteration; and designing different strategies for all the stages and generating the new conformation individual. The forecasting method for the multi-stage differential evolution protein structure based on the colony abstract bulge estimation provided by the invention is high in forecasting precision and low in calculation cost.
Owner:ZHEJIANG UNIV OF TECH

Addressing and capacity configuration method used for unified power flow controller (UPFC)

The invention discloses an addressing and capacity configuration method used for a UPFC. A UPFC addressing and capacity configuration model with transient stability constraints is established, a difference evolution algorithm serves as a framework, and the installation position and the capacity of the UPFC are optimized; in addition, a centrality correction interior point method is adopted for carrying out optimization and fitness assessment on continuous variables of each difference evolution unit. The difference evolution algorithm is simple in operation, high in searching capability, fewer in adjusting parameters and suitable for mixed optimization, and the interior point method is good in convergence and strong in robustness, so that the provided mixed algorithm can well solve the problems of UPFC addressing and capacity configuration, the method is highly efficient and convenient, and the application prospect is good.
Owner:STATE GRID CORP OF CHINA +3

Self-tuning PID energy-saving temperature control method and module

The invention relates to a self-tuning PID energy-saving temperature control method. The method comprises the steps that a target temperature value, a control period, a sampling period and a thresholdtemperature difference are set first; load heating power is increased; the current regional temperature sampling value of a controlled object is acquired, and the sampling period is combined to calculate the temperature rise rate; a PSO-DE algorithm combining particle swarm optimization (PSO) and differential evolution (DE) is used to adjust and calculate PID control parameters; the duty cycle ofthe output pulse of a control period is adjusted according to the operation result; heating power output is adjusted, so that the sampled temperature sampling value is approximately equal to the actual temperature value of the controlled object; a temperature error caused by the heating inertia is reduced; heating power is accurately controlled; and the energy saving effect is obvious. A self-tuning PID energy-saving temperature control module comprises the self-tuning PID energy-saving temperature control method, can accurately control the heating power, and has an obvious energy-saving effect.
Owner:广东国颂能源科技有限公司

Active power distribution network dynamic reconstruction method containing distributed power supply

The invention relates to an active power distribution network dynamic reconstruction method containing a distributed power supply. The method comprises the following steps: 1) establishing different power flow calculation models corresponding to different node types according to the characteristics of different distributed power supplies; 2) establishing an active power distribution network reconstruction model, and establishing an optimized objective function based on the minimum sum of the feeder loss; 3) carrying out clustering on the equivalent load of the active power distribution networkon the basis of an improved optimal fuzzy C-means clustering method, so that the dynamic reconfiguration problem of the active power distribution network is converted into a static reconstitution problem of C representative load data as the clustering center; and 4) according to the model characteristics, using a differential evolution invasive weed mixed algorithm to solve a power distribution network reconstruction model containing a distributed power supply to obtain an optimal reconstruction scheme.
Owner:SHANGHAI MUNICIPAL ELECTRIC POWER CO

Method for intelligently optimizing production scheduling of production process of flow industry enterprise

The invention relates to a method for intelligently optimizing production scheduling of the production process of a flow industry enterprise, relating to the construction of scheduling model and the design of solution algorithm. The method comprises the following steps: building an intermittent and continuous mixed production process scheduling model with finite intermediate storage for the flow industry by adopting a unified time discretization method; and providing a new coding mode, and designing an optimization method for differential evolution with the corresponding evolutionary operation as the model based on a new coding mode. The provided model and algorithm have simple operation and fast calculation speed, can determine the flow velocity in each time segment for each continuous device on a production line and determine the handling capacity (batch quantity) in each time segment for each intermittent production device, thereby optimizing the target and improving the production efficiency.
Owner:ZHEJIANG UNIV OF TECH

APDE-RBF neural network based network security situation prediction method

The invention belongs to the technical field of network security and particularly relates to an APDE-RBF (Affinity Propagation Differential Evolution-Radial Basic Function) neural network based network security situation prediction method. The APDE-RBF neural network based network security situation prediction method comprises the steps of dividing and clustering sample data by utilizing an AP clustering algorithm to obtain the number of nodes of hidden layers of the center and network of the RBF; obtaining population diversity by using AP clustering, changing a zoom factor and a crossover probability of a DE algorithm adaptively and optimizing the width and connection weight of the RBF; meanwhile, performing chaotic search on elite individuals and population diversity center of each generation of population in order to avoid falling into local optimization and jumping out of a local extreme point; and determining a final RBF network model, inputting a test dataset and outputting a situation prediction value. The APDE-RBF neural network based network security situation prediction method aims at improving the prediction precision for the network security situation while enhancing the generalization ability.
Owner:CHONGQING UNIV OF POSTS & TELECOMM

Wavelet domain color image watermark encryption algorithm based on differential evolution

The invention discloses a wavelet domain color image watermark encryption algorithm based on differential evolution. The operating method comprises the following steps: performing color space transformation on an original image to obtain a YIQ color format suitable for a human vision system and extracting a brightness component Y; adopting Arnold scrambling on a watermark image; performing three-stage discrete wavelet decomposition on the component Y of the original image to generate four sub-bands of different frequencies by utilizing discrete wavelet transform, thereby obtaining a sub-band image; representing a sub-band image matrix by using A, and performing singular value decomposition on the image subjected to three-stage wavelet decomposition; performing primary discrete wavelet decomposition on the watermark image subjected to scrambling encryption processing; performing watermark embedding; adaptively optimizing and selecting embedding strength by adopting differential evolution, and adding watermark information into the original image subjected to singular value decomposition in a multiplicative addition form. The invention has the beneficial effects that the watermark algorithm has high invisibility and is high in robustness.
Owner:QUFU NORMAL UNIV

Unmanned combat aerial vehicle maneuvering gaming method with intuition fuzzy information

The invention discloses an unmanned combat aerial vehicle maneuvering gaming method with intuition fuzzy information. The method comprises steps of firstly, establishing a strategy set of an unmannedcombat aerial vehicle maneuvering decision; then, carrying out multi-attribute intuition fuzzy estimation on selectable maneuvering strategies according to the threatening size so as to obtain an intuition fuzzy payment matrix of unmanned combat aerial vehicle maneuvering gaming; establishing a unmanned combat aerial vehicle maneuvering gaming planning mode with intuition fuzzy information under an undetermined environment; and finally, using an improved differential evolution algorithm, solving the mode so as to obtain the optimal maneuvering gaming strategy of the unmanned aerial vehicle under the undetermined environment. According to the invention, the optimal decision problem of the unmanned combat aerial vehicle maneuvering under the undetermined environment is mainly solved.
Owner:NANJING UNIV OF AERONAUTICS & ASTRONAUTICS

Forward kinematics solving method of parallel robot

ActiveCN102968665AImprove convenienceStrong global exploration abilityGenetic modelsHigh rateOptimization problem
The invention discloses a forward kinematics solving method of a parallel robot. When the method is applied to solve the forward kinematics problem of the parallel robot, the characteristic of easily solving inverse kinematics of the method is utilized, the forward kinematics solving problem of the parallel robot is converted into the equivalent minimal optimization problem, and the numerical optimization method is used for solving the problem. The method is integrated with the advantage of differential evolution having excellent global exploration performance on the solution space and the characteristic of pattern search having good local development capability on the solution space; and the mutual mechanisms of the two optimization algorithms are merged so that the search performance of the algorithms is improved. Compared with the traditional method, the method provided by the invention has high optimal solution searching accuracy and high rate of convergence while solving the forward kinematics problem of a 6-SPS type parallel robot; and simultaneously, the method also can be popularized to solve the forward kinematics problems of the parallel robots of other types.
Owner:SUZHOU SUXIANG ROBOT INTELLIGENT EQUIP CO LTD

Workpiece attitude adjustment method based on measuring point and adaptive differential evolution algorithm

The invention discloses a workpiece attitude adjustment method based on a measuring point and adaptive differential evolution algorithm. The method includes the steps of initial matching and accuratematching. Firstly, reference points are selected on a theoretical workpiece curved surface and an actual curved surface to build corresponding local coordinate systems, and initial matching is realized by rough alignment of the local coordinate systems. In accurate matching, a target function based on a least-square distance is built through a point set after initial matching, an optimal solutionenables a value of the target function to be minimum and is searched by the adaptive differential evolution algorithm, so that an optimal accurate matching matrix is acquired. By the two steps, a final space transformation matrix can be acquired, adjustment amount corresponding to a machine tool can be calculated by combining a structure of the machine tool, the machine tool is adjusted, so that aworkpiece attitude is adjusted, a workpiece is accurately positioned, and allowances are uniformly distributed. According to the method, attitude adjustment and accurate positioning of testing partsmachined by an ISO (international standardization organization) five-axis numerical control machine tool are achieved, and effectiveness of the method is verified.
Owner:SOUTHWEST JIAOTONG UNIV
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