110 results about "Genetic Evolution" patented technology

The present invention provides an automated system and method for laying out photovoltaic (PV) solar panels on one or more regions of a roof or ground installation. Automation includes a presentation of optimal comparative layouts, use of different panels (of the same or different manufacturers), and consideration of different energy production characteristics and different layout constraints. Diverse mathematical approaches are used to determine the optimal panel layout for a given geographic region and for particular goals, including, but not limited to, the objective of maximizing the solar production from the designated region(s). The application of two such mathematical algorithms are detailed as parts of an exemplary embodiment: a deterministic edge-aligned approach as well as a nondeterministic approach that employs a genetic evolution simulation.

The invention discloses a multiple input multiple output (MIMO) radar array position error self-correcting method based on a genetic algorithm, belonging to an MIMO radar array position error self-correcting method. The method comprises the following steps of: carrying out one-dimensional spectral peak search according to an ideal array element position by utilizing a MUSIC method to obtain the initial value theta k of direction of arrival (DOA); generating an initial group, carrying out individual fitness evaluation according to f(G), and finding out f max; carrying out genetic evolution operations of selection, crossing, heteromorphosis and the like to generate a new generation of search group, and carrying out fitness evaluation to reduce a deviation factor sigma; revising the theta k according to the optimal individual in the population; and carrying out genetic iteration until a greatest iteration number is set. By using the method, online estimation and correction of array position error can be finished while the DOA estimation is carried out, thereby the robustness of estimation of system parameters is improved.

The invention discloses a genetic evolution image rebuilding method based on a Ridgelet redundant dictionary, and the method is used for solving the problem that the image rebuilt by the existing L0 norm rebuilding technology is poor in visual effect. A rebuilding process comprises the following steps of: clustering all partitioning observation vectors according to the level of similarity by selecting a proper clustering algorithm; initiating clusters; carrying out the common genetic evolution on the initiated clusters; rebuilding an initial image; updating by means of filtering and convex projecting; judging whether evolution algebra reaches a maximum value or not; updating the sparsity; updating the clusters; carrying out the independent genetic evolution on the image blocks; and rebuilding the image. In the method, the similar clusters of the image blocks are used, and the optimal Ridgelet redundant dictionary base atom is found for each image block of each cluster by a genetic evolution computation thought, so that the time complexity of the algorithm is reduced, the blocking effect in the rebuilt image is removed by means of filtering and convex projecting, the search space of the optimal solution is shortened, the image is high in rebuilding precision, and the image is good in rebuilding effect, so that the method can be used for the fields of image processing and computer vision.

The invention discloses a tolerance optimization allocation method based on a tolerance grade and a genetic algorithm, comprising the following steps: (1) creating a tolerance optimization mathematical model; (2) creating a standard tolerance database; (3) encoding based on the tolerance grade; (4) determining an adaptability function of the genetic algorithm; (5) setting a genetic operator; (6) performing genetic evolution; and (7) decoding to obtain a corresponding tolerance grade and inquiring a corresponding tolerance value. The method disclosed by the invention realizes the tolerance optimization allocation in the product design by using a computer, thereby replacing the manual distribution, improving the efficiency and saving the time; meanwhile, the method performs the optimization allocation through the genetic algorithm, serves the minimum processing cost as an optimization target and reduces the processing cost of products. Therefore, the method has good practical value and extensive application prospect in the technical field of computer aided tolerance design.

The present invention discloses an adaptive coefficient genetic algorithm-based cloud manufacturing service resource matching method. According to the method, the value of the objective function of each individual in a population is calculated, and the capacity limit of each individual is judged; individuals which do not meet capacity requirements are discarded; an adaptive coefficient is calculated; the selection probability, crossover probability and mutation probability of iteration of a current round are calculated; genetic evolution is carried out according to the probabilities, so that anew population can be generated; and the population is supplemented with new individuals. According to the method of the invention, an optimal resource service combination matched with the task requirement of a cloud manufacturing user is solved according to the task requirement of the cloud manufacturing user; it can be ensured that the sum of the products of the cost and time of all tasks is minimum; the capacity limitation of resource services is satisfied, so that queuing and waiting can be avoided; and since an improved genetic algorithm has high robustness and fast convergence rate andwill not be trapped in local optimum, the diversity of the population can be significantly improved, and the accuracy of resource matching can be improved.

The invention relates to an urban traffic planning simulation method and system based on a multi-agent activity model. The method comprises the following steps: firstly acquiring urban road information to generate a road network information file; secondly, generating initial traffic requirements and an initial daily activity plan according to the acquired survey data; thirdly, loading the daily activity plan to the road network information file; fourthly, calculating a utility value and planned selective probability for each surveyed object; and finally, performing an iterative process to output the simulation result. The system comprises a client end and a server client which communicate in an http communication manner. According to the invention, a four-stage traffic simulation method is discarded; a traffic requirement generating and distributing method based on the activity model is completely adopted; a multi-agent technology and a genetic evolution algorithm are integrated for use; utility optimal limitation is used to generate and select an optimal path, so that maximum utilization efficiency of a vehicle and a road network is achieved, and traffic accidents and traffic jams are reduced.

The invention provides a decision-making method for maintaining middle and small span concrete bridges. The decision-making method provided by the invention comprises the following steps of: (2) determining a decision-making object and a predicated age limit, wherein the decision-making object is a bridge network OD (Outside Dimension) model; (2) indentifying the bridge network OD model: converting an image-formed OD model into a numerical-value-formed OD model and identifying a minimum path set matrix P which can be used for a decision-making calculation; (3) establishing a cost model of a maintenance policy and an effect model which can be used for processing an event tree analysis on the OD model to obtain the maintenance policy; (4) carrying out an improved non-dominated sorted genetic algorithm: defining a coding method of the maintenance policy and taking a connecting probability Rnw and a maintenance cost Cnw of the bridge connection as two targets to be participated in a genetic evolution; carrying out a Pareto optimality and searching for the globally optimal solution. The method provided by the invention has the advantages of simple principle, convenience in using, wide application range, good reliability and the like.

The invention discloses a warehouse logistics AGV path planning algorithm based on an ant colony algorithm and an improved genetic algorithm, relates to the ant colony algorithm and the genetic algorithm, and overcomes the defects that a traditional method is time-consuming in calculation, easy to premature and converge and easy to fall into local optimum. The invention comprises the following steps: 1, establishing gridding division and coding for a site; 2, generating an initial AGV path for genetic evolution by using an improved ant colony algorithm based on obstacle information; 3, iteratively selecting an AGV optimal path based on a three-stage genetic algorithm; 4, carrying out tail end intersection on different AGV paths with overlapping points; 5, performing favorable variation on the AGV path; and 6, recalculating the AGV path fitness, judging whether iteration is terminated or not, and carrying out trajectory smoothing processing on the terminated AGV path. The basic idea of the invention is that the improved ant colony algorithm and the improved genetic algorithm are combined, the iterative convergence speed is increased, the AGV path with higher operation efficiency is obtained, and the engineering applicability is high.

The invention relates to a layering collocation method of land utilization. According to the layering collocation method of the land utilization, basic data of the land utilization are firstly obtained and collected by a land utilization layering collocation model, land needing to be optimized in spatial arrangement is suitability evaluated, a specific genetic algorithm chromosome and a genetic evolution operator are constructed aiming at optimization problems of the land utilization spatial arrangement, mapping from a problem domain to the algorithm domain is accomplished and the spatial arrangement of various kinds of land is optimized by using the genetic algorithm under the guidance of a space optimization objective, and then land utilization competition between the optimized spatial arrangement of the various kinds of land and the land utilization in an existing state is solved by combining knowledge in the land planning field and the game theory. The layering collocation method of the land utilization is capable of well optimizing the spatial arrangement of the various kinds of land and good at coordinating the land utilization. The knowledge in the land planning field ensures that a land utilization coordination result is reasonable and the game theory is capable of solving the land utilization competition by introducing benefit factors, and feasibility of the land utilization coordination result is well guaranteed.

The invention discloses a rainwater pipe network system hydraulic design parameter optimization method. The method comprises the steps that firstly, according to the basic condition and data of a rainwater pipe network and rainwater pipe network hydraulic design parameter data, a dynamic method is adopted for building a rainwater pipe network hydraulic model for simulating the drainage effect of the rainwater pipe network under the given condition; then, node spillway discharge and the pipe network manufacturing cost minimum are adopted as an optimization object, a rainwater pipe network system hydraulic design parameter optimization model is built in MATLAB software, the rainwater pipe network system hydraulic design parameters are optimized, and the optimal solution of the rainwater pipenetwork hydraulic design parameters is obtained. A self-adaption genetic algorithm is adopted in the MATLAB software for optimizing the rainwater pipe network system hydraulic design parameters, according to the algorithm, a genetic operator can perform adjustment according to the individual adaptation value during genetic evolution, genetic evolution cannot standstill locally, the algorithm canbe rapidly converged to the global optimum, and the purposes of meeting the drainage requirement and saving the construction cost are achieved.

The present invention discloses a selective integrated human face recognition method using a genetic algorithm fused with differential evolution. The method comprises: firstly, extracting an HOG feature of a human face image; then, performing dimension reduction on the human face image by using a PCA algorithm to reduce calculation complexity; and finally, performing classified recognition by using data after dimension reduction and applying a GADESEN algorithm. According to the method, with a support vector machine used as a base classifier, N samples are extracted with replacement from an original training set, and according to the method, iteration is performed for T times, and a sample set generated each time is used to train a base classifier model, N generated base classifiers are real-coded to generate an initial population, and the difference vector is used to guide the mutation to produce the high quality individual. The crossover operation uses the parent individual and the mutated individual to produce the cross individual, which increases the individual's diversity, Retention strategy for genetic evolution.

The invention discloses a spider mitochondria genome complete sequence amplification primer and an amplification method. The method comprises the steps of extracting a general DNA of the spider genome, designing a mitochondria genome complete sequence universal amplification primer, and amplifying and sequencing the long PCR (L-PCR) of the DNA of the mitochondria genome. The spider mitochondria genome complete sequence amplification primer consists of 6 pairs of single strain oligonucleotides. Multiple spider mitochondria genome complete sequences can be quickly and efficiently amplified by adopting the universal amplification primer, and a material basis is established to spider species identification, population diversity analysis, genetic resource protection and utilization, systematic inheritance evolution and other researches.

The invention discloses a method for preparing bacteria 16S rDNA fingerprint spectrums. The method comprises the steps of PCR (polymerase chain reaction) amplification, SAP (super absorbent polymer) enzymatic digestion, transcription and nuclease digestion, purification, mass spectrometer detection and the like. A nucleic acid fingerprint spectrum database of common bacteria is created based on the method. According to the mass spectrum peak chart generated through experiments, the bacteria to be detected can be classified and identified and the results can be widely applied to the fields of bacteria classification and identification, genetic evolution analysis, drug resistance screening application, import and export inspection and the like.

The invention discloses a composite gas bearing with surface texture. The composite gas bearing comprises a half-circular-arc-shaped fixed tile block, half-circular-arc-shaped inclinable tile block units are arranged at the position directly facing an opening of the fixed tile block, the fixed tile block and the inclinable tile block units enclose a cylinder together, the inner surfaces of the inclinable tile block units are provided with the surface texture, and the outer surfaces of the inclinable tile block units are provided with inclinable tile block supporting bodies. The invention further discloses a surface texture design method for the composite gas bearing with the surface texture, by means of adoption of genetic evolution strategies and dynamic application of operations of selection, crossover, mutation and the like, optimal allocation of inclinable tile block surface texture shapes, dimension parameters and distribution positions is realized, so that the bearing performanceof the composite gas bearing with the surface texture is furthest improved, and the service reliability of the bearing is improved.

The invention provides a method for optimizing a structure of a shaft part based on an immune genetic algorithm. The method is implemented by using a computer and comprises the following steps of: converting a combination function correspond into antigens, wherein the combination function is formed by combining the volume of the shaft part with the sensitivity of reliability for a design variable by an image set method; converting the basic size of the shaft part into antibodies; performing immunoselection on the antibodies based on the expected reproductive rate of the antibodies; after performing cloning, crossing and variation operation on an antibody population, making the antibody population generate a progeny antibody population by adopting advantageous measures such as a mating strategy, an elitist strategy, new thought of comparing and substituting similar antibodies, a measure of dividing a memory pool into two parts and the like; and therefore, optimizing the basic size of the shaft part through repeated generation continuation genetic evolution. The method provided by the invention has the advantages of efficiently optimizing the shaft part, improving optimization efficiency and precision and reducing cost. At the same time, the invention provides an effective method for calculating the reliability of the shaft part and the sensitivity of the design variable.

The invention discloses a face recognition optimization method based on an SGASEN algorithm. First, the features of a face image are extracted. Then, recognition and classification are carried out using an SGASEN algorithm. In view of the problem that there are a large number of invalid cross operations on population in the SGASEN algorithm, a regression tree is used as a base classifier, and computationally simple Jaccard similarity detection is used before cross operation, so that cross operations on similar individuals are reduced, and the population is more diverse. In view of the fact that the number of base classifiers is not limited in the SGASEN algorithm, generalization error and the number of base classifiers are considered in a fitness function, and genetic evolution is carried out using an optimal retention strategy. Finally, a strong classifier with strong generalization ability and fewer base classifiers is obtained, and the accuracy of face recognition is improved.

The invention discloses a cross-layer optimization device based on spectrum aggregation in a wireless network and a method thereof. A genetic algorithm is utilized. Furthermore through mapping resource distribution to individual codes of the genetic algorithm, complexity in performing optimization settlement by means of a physical interference model is effectively prevented, and a requirement for large-scale wireless network optimization is satisfied. In a genetic evolution operation, because a fact that an individual satisfies an optimization restraining condition is always ensured in an individual initialization process and a group evolution process, thereby greatly improving convergence speed and improving optimal solution finding efficiency.

The invention discloses a method for preparing bacteria nucleic acid fingerprint spectrums. The method comprises the steps of PCR (polymerase chain reaction) amplification, SAP (super absorbent polymer) enzymatic digestion, transcription and nuclease digestion, purification, mass spectrometer detection and the like. A nucleic acid fingerprint spectrum database of common bacteria is created based on the method. According to the mass spectrum peak chart generated through experiments, the bacteria to be detected can be classified and identified and the results can be widely applied to the fields of bacteria classification and identification, genetic evolution analysis, drug resistance screening application, import and export inspection and the like.

The invention discloses double RT-PCR (reverse transcription-polymerase chain reaction) primer, kit and method for amplifying North America and European porcine blue ear disease viruses; the method has the advantages of high specificity, high sensitivity, low time and labor consumption and the like, and is of important guidance significance to molecular epidemiology, genetic evolution, vaccine research and development and the like for studying porcine blue ear disease. In addition, denaturing and annealing temperature in the method only takes 20 s, amplification can be performed as well just with one PCR apparatus, and the method may also act as a method for identifying and detecting North America and European porcine blue ear disease viruses and helps quickly classify porcine blue ear viruses.

Automating optimal placement of macro-blocks in the design of an integrated circuit. A first set of placements is generated and corresponding measures of optimalness for each placement is computed. A new set of placements is generated, with each placement being generated from multiple (“chosen placements”) of the first set of placements. The position of each macro in the new placement is made to be at least substantially identical to the position of the corresponding macro in one of the chosen placements. The placements having high values of optimalness are selected to be the chosen placements, thereby causing the properties of desirable placements to be propagated to new set of placements, as is common in genetic evolution. Another aspect of the present invention enables automatic removal of overlaps in a placement.

The invention relates to the technical field of smart city geographic information service, in particular to a satellite-ground space-time maximum coverage problem solving method based on an improved genetic algorithm. According to the method, the problems of low efficiency and high time expenditure when a traditional optimization technology is used for solving the space-time maximum coverage problem of the satellite-ground sensor are solved, and a novel cross operator taking the space-time coverage degree as a core is designed based on a genetic algorithm and used for solving the space-time maximum coverage problem. Comparing traditional optimization techniques, ain the method, facilities with relatively small space-time coverage are preferentially selected for cross transformation in a genetic evolution process; excessive redundant coverage is effectively reduced, the algorithm can be prevented from falling into a premature convergence state, the solving efficiency of the algorithm isremarkably improved, the time overhead is reduced, and it is proved that the method is a practical and reliable method beneficial to improving the calculation efficiency in solving of the satellite-ground space-time maximum coverage problem.