605 results about "Differential evolution" patented technology

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.