67 results about "Adaptive mutation" patented technology

A method of assigning web service requests to service providers includes searching for an optimal assignment from all possible assignments using a genetic algorithm (GA) that represents possible assignments as chromosomes, and converging towards an assignment of web service request to service providers that maximizes overall business value for all workflows to the service providers. An adaptive mutation scheme is used to introduce mutation into populations of chromosomes. The mutation scheme includes a mutation rate that increases when chromosomes under evaluation fail to improve its workload against the metric over a certain number of generations.

The invention discloses a photovoltaic power station power generation amount predication method based on an adaptive mutation particle swarm and a BP network. The method comprises the steps of acquiring and preprocessing a data sample, then constructing a BP neural network structure, optimizing the network by means of an adaptive mutation particle swarm algorithm, and introducing an optimal individual which is obtained after optimization into the BP neural network for predication. According to the method of the invention, the adaptive mutation particle swarm algorithm and the BP neural network are combined for performing real-time power generation amount predication on the photovoltaic power generation system; namely power generation state at next time period is predicated through power generation amount information of a former time period. According to the method of the invention, a relatively high global convergence capability of the adaptive mutation particle swarm algorithm is used for optimizing the initial weight and threshold of the network, thereby improving convergence speed and precision in power generation amount predication.

The invention discloses a method for recognizing road speed limit signs by optimizing an SVM (support vector machine) by adaptive mutation particle swarm optimization based on a GPU (graphics processing unit). The category of the road speed limit signs is quickly and accurately recognized by using PSO (particle swarm optimization) to optimize parameters of an SVM. Based on the characteristics of particle swarm in optimizing parameters of the SVM, such as high data processing capacity and long computation time, the operating speed of PSO algorithm is increased by parallel computing of the GPU. The method has the advantages that the method of optimizing the support vector machine by GPU-accelerated ALTM PSO (adaptive local tone mapping-particle swarm optimization) is superior to the traditional SVM in accuracy of recognizing road speed limit signs and is superior to the standard PSO-SVM in algorithm convergence and operating speed.

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

An augmented Lagrangian genetic algorithm that may be used to generate solutions for optimization problems subject to linear, bound, and non-linear constraints is discussed. The augmented Lagrangian genetic algorithm uses an adaptive mutation operator to separately handle the linear, and bound constraints, and uses an augmented Lagrangian framework to handle non-linear constraints. The non-linear constraints are handled by creating a sub-problem without the linear and bound constraints and solving the sub-problem using Lagrange parameter estimates and a penalty factor. The exclusion of the linear constraints and boundary constraints from the sub-problem allows the sub-problem to be resolved in a more effective manner than is possible using conventional techniques.

The invention discloses a regional comprehensive energy system optimization operation method based on a repeated game model. The method comprises the steps of firstly, performing steady-state modelingand power flow analysis on a power distribution network, a gas distribution network and a micro-energy network in a regional comprehensive energy system; then, considering the interaction influence between a power link and an energy coupling link in the regional comprehensive energy system; using a micro-energy network and a power distribution network as game participants; constructing a repetitive game optimization model of the regional comprehensive energy system by taking the daily operation cost of the micro-energy network and the comprehensive satisfaction of the power distribution network as respective utility functions, and solving the repetitive game optimization model by adopting an adaptive mutation particle swarm algorithm to obtain a game equilibrium optimization result of theregional comprehensive energy system; and finally, verifying the correctness and effectiveness of the regional comprehensive energy system optimization operation method based on the repeated game model. The method can give full play to the active regulation and control effect of the power distribution network, gives consideration to the benefits of the micro-energy network and the power distribution network, and achieves the cooperative economic optimization operation of the regional integrated energy system.

The invention discloses an optimal configuration scheme of distributed power supply. According to the method, the benefits of power distribution operators are mainly considered from a planning perspective; the randomness of wind power generation is considered; a multi-target optimal configuration model, with the minimal active electrical energy loss, the minimal total voltage deviation and the minimal risk cost, of a power distribution network is built; multi-target normalization is realized by a fuzzy set theory; the problem that various sub-targets are different in dimension is not generated any more; the built optimal configuration model is determined by an adaptive mutation particle swarm optimization, so that a mutation operation is introduced; the condition that basic particle swarm optimization easily falls into local optimum is improved; finally, as an example, an IEEE 33 node power distribution system validates that the optimal configuration model of DG and selected adaptive particle swarm optimization (AMPSO) disclosed by the invention; and the obtained simulation result shows that the model and the adaptive particle swarm optimization adopted by the scheme are feasible and effective.

Power loss of a direct-current microgrid is analyzed. The process includes initialization and statistics; mutation; competition and convergence test; adaptive mutation; and taboo search. Rearrangement of the microgrid can be figured out in a short time. Thus, power loss is minimized and service can be quickly recovered.

The invention discloses an immune clone quantum clustering-based SAR image segmenting method, which relates to the technical field of image processing, and mainly solves the problem of limitation on the application of the conventional quantum clustering technology in a large-scale data set. The immune clone quantum clustering-based SAR image segmenting method is implemented by the following steps: 1) extracting features of an SAR image to be segmented; 2) initializing an antibody population and coding antibodies; 3) calculating antibody affinity according to quantum mechanical characteristics, and dividing the antibody population into an elite population and a general population; 4) designing different immune clone optimization operators for the elite population and the general population respectively, and performing a cloning operation, a normal cloud model-based adaptive mutation operation, a uniform hypermutation operation, a clonal selection operation and a hypercube interlace operation orderly; and 5) outputting an SAR image segmentation result. The immune clone quantum clustering-based SAR image segmenting method has high iteration optimization speed and high stability, can effectively segment the SAR image which contains large-scale data volume, and is suitable for object detection and identification of the SAR image.

The present invention provides a Venezuelan equine encephalitis virus replicon RNA useful in the development of stable lines of mammalian, avian and insect cells in which these replicons will persistently replicate. Venezuelan equine encephalitis (VEE) virus replicons contain a number of unique adaptive mutations that make the replicons noncytopathic. The replicons remain resistant to IFN-α/β. Replicon replication leads to high-level production of heterologous proteins, which are encoded by the replicons' genome and are under the control of a viral subgenomic promoter. Also provided are methods of screening for inhibitory compounds of Venezuelan equine encephalitis virus replication and eastern equine encephalitis virus replication.

The invention discloses a pedestrian trajectory prediction method which integrates a social force model and a Kalman filter. The Kalman filter is divided into two parts: a time update part and a measurement update part. Adaptive mutation particle swarm optimization algorithm is used to identify the parameters of social force model by setting fitness function. The predicted pedestrian trajectory issimulated in the step 2, the pedestrian position value at the next time is calculated according to the Kalman time update formula in the step 1, and the prior estimate value shown in the descriptionis finally obtained; According to the updated formula of Kalman measurement, the measurement value Zk of pedestrian's current position is calculated, and the optimal estimation value is obtained by combining the prior estimation value shown in the description. The error threshold psi is set to judge the error between the predicted position of the social force model and the optimal estimated value,and the error is corrected to complete the trajectory prediction. It can predict the trajectory accurately when pedestrians take the initiative to avoid turning and walking in a straight line and effectively reduce the error between the actual trajectory and the predicted trajectory so as to meet the required prediction requirements.

The invention relates to a classification and construction method, in particular to a classification and construction method for a semi-supervised support vector machine (SVM) remote sensing image. The problems of incapability of easily finding an optimal classification parameter and low classification accuracy caused by the hard classification of mixed pixels and insufficiency of training samples during the application of the conventional SVM remote sensing image technology are solved. The method specifically comprises the following steps of: 1, optimizing an SVM remote sensing image parameter by utilizing an adaptive mutation particle swarm algorithm; and 2, constructing a PS3VM semi-supervised classification model by utilizing a self-training method. The method is used for constructing the semi-supervised SVM remote sensing image.

The invention discloses a cost-sensitive support vector machine locomotive wheel detecting system and a detecting method thereof. The system comprises a data preprocessing module, a cost-sensitive support vector machine training module, a parameter optimizing module, an optimal cost-sensitive support vector machine classifying module, a determining module and a wheel state output module. The detecting method comprises eight steps totally. The parameter optimizing step is an adaptive mutation particle swarm optimization algorithm and has advantages of high robustness, global searching characteristic, etc. A space which continuously reduces in iteration is enlarged, and searching is performed in a larger space. Furthermore high population diversity is kept. Additionally, a possibility of finding out an optimal value by the algorithm is improved.

The invention discloses a circuit security constraint-considering provincial grid AGC (automatic generation control) unit dynamic optimization scheduling method. The method comprises the following steps of firstly, inputting provincial grid basic data, i.e., acquiring a network structure and related data of a provincial grid, acquiring AGC related data, and setting an evolutionary programming parameter; secondly, randomly generating an initial population of control variables (a regulating instruction and a regulating rate), and performing adaptive mutation operation on antibodies in the population; thirdly, correcting the antibodies according to a regulating instruction constraint, a regulating rate constraint and the minimum continuous climbing time constraint, and comprehensively considering target functions of a CPS1 (control performance standard) index and AGC regulation ancillary service charge, and inequality constraints of tie-line power deviation, system frequency deviation, CPS indexes, unit output and circuit safety, and calculating the adaptive value of each antibody in the population; finally, on the basis of adaptability, evaluating and selecting the antibodies, and performing termination judgment to realize decision on the regulating instruction and the regulating rate of an AGC unit.

The present inventors have developed a culture system for genotype 3a, which has a high prevalence worldwide. Since intergenotypic recombinant genomes exploiting the replication characteristics of JFH1 will be a valuable tool for the genotype specific study of the replaced genes and related therapeutics, the present inventors constructed a genotype 3a/2a (S52/JFH1) recombinant containing the structural genes (Core, E1, E2), p7 and NS2 of strain S52 and characterized it in Huh7.5 cells. S52/JFH1 and J6/JFH viruses passaged in cell culture had comparable growth kinetics and yielded similar peak HCV RNA titers and infectivity titers. Direct genome sequencing of cell culture derived S52/JFH1 viruses identified putative adaptive mutations in Core, E2, p7, NS3 and NS5A; clonal analysis revealed, that all genomes analyzed exhibited different combinations of these mutations. Finally, viruses resulting from transfection with RNA transcripts of five S52/JFH1 recombinant containing these combinations of putative adaptive mutations performed as efficiently as J6/JFH viruses in Huh7.5 15 cells and were all genetically stable after viral passage. In conclusion, the present inventors have developed a robust and genetically stable cell culture system for HCV genotype 3a.

The invention discloses an intelligent optimization method and system for ecological operation of cascade hydropower stations, belonging to the technical field of efficient utilization of water resources and optimal operation of hydropower systems. The method comprises the following steps: establishing a cascade hydropower station reservoir operation model which takes the minimum ecological watershortage as an objective function and considers a complex constraint set; Knowledge rules are used to dynamically generate the initial scheduling process, and the initial population is generated by random disturbance in its neighborhood. All individuals are modified to feasible search space in turn, individual fitness value is calculated by penalty function method, individual position and velocityare updated dynamically, and neighborhood search is carried out by adaptive mutation strategy. The search process described above is repeated until the termination condition is met. The invention hasthe advantages of clear and simple principle, fast convergence speed and the like, and the obtained result is reasonable and feasible, and can provide strong technical support for rational allocationof hydropower resources.

The invention discloses an improved genetic algorithm-based travel itinerary planning method. The method comprises the following steps of firstly performing arrangement according to a sequence of visited cities to form codes; secondly initializing a population by adopting a two-way greedy selection policy; calculating a fitness value of each individual in the population; by adopting roulette wheel selection, selecting the individuals with high fitness from the old population to a new population; performing crossover operation according to an adaptive crossover probability Pci, and selecting multiple parents to perform pairing to generate new individuals; performing mutation operation according to an adaptive mutation probability Pmi, and determining mutant individuals; and finally judging whether a predetermined stop condition is met or not, and if yes, stopping heredity and obtaining an optimal solution, otherwise, calculating the fitness value of each individual in the population. According to the method, a travel itinerary route is planned for users by adopting an improved greedy adaptive genetic algorithm based on a travel itinerary planning model; and through the method, the itinerary planning speed is increased and the algorithm is prevented from falling into local optimal solution.

The present inventors developed hepatitis C virus 1a/2a and 1b/2a intergenotypic recombinants in which the JFH1 structural genes (Core, E1 and E2), p7 and NS2 were replaced by the corresponding genes of the genotype Ia reference strain H77C or TN or the corresponding genes of the genotype Ib reference strain J4. Sequence analysis of recovered 1a/2a and 1b/2a recombinants from 2 serial passages and subsequent reverse genetic studies revealed adaptive mutations in e.g. p7, NS2 and/or NS3. In addition, the inventors demonstrate the possibility of using adaptive mutations identified for one HCV isolate in generating efficient cell culture systems for other isolates by transfer of mutations across isolates, subtypes or major genotypes. Furthermore neutralization studies showed that viruses of e.g. genotype 1 were efficiently neutralized by genotype Ia, 4a and 5a serum, an effect that could be utilized e.g. in vaccine development and immunological prophylaxis. The inventors in addition demonstrate the use of the developed systems for screening of antiviral substances in vitro and functional studies of the virus, e.g. identification of receptors required for HCV entry

The invention discloses a dual-ring signal timing optimization method based on an adaptive genetic algorithm. The method comprises the steps of acquiring crossing traffic flow data when a crossing utilizes a dual-ring signal for timing; processing by means of a true-value coding method; generating an initial parent group; calculating individual adaptability; determining whether the individual adaptability satisfies a preset terminating condition; if not, utilizing a random tournament selection model for performing selection processing on the group; performing single-point intersecting processing on the group by means of an adaptive intersected probability method; performing inhomogeneous mutation processing on the group by means of an adaptive mutation probability method; performing an optimal storage strategy on the group; and recalculating individual adaptability. According to the dual-ring signal timing optimization method, minimizing average delaying of vehicles at the crossing isutilized as an optimizing target; a dual-ring signal timing optimization model with green light time in each flow direction as an optimizing parameter is established; and furthermore the adaptive genetic algorithm is utilized for solving the model, thereby reducing the average delaying time of the vehicles at the crossing and improving operation efficiency of the crossing.

The invention relates to a PID parameter optimization method based on an adaptive genetic algorithm in a PID control algorithm for dissolved oxygen content in aquaculture. Firstly, the encoding of KP, KI, KD adopts binary encoding and multi-parameter connection mode, and the three parameters are represented by 10-bit binary code, and the three parameters are serially connected together to form a solution string. Then the design of the adaptation function should be carried out. The design of the adaptation function should compromise the feedback deviation e(t) formed by the optimal prediction, the rate of change of the deviation and the control amount u(t). The selection, crossover and mutation algorithms in the genetic operator all adopt the genetic operation strategy that introduces the adaptive mechanism, that is, adaptive selection, adaptive crossover and adaptive mutation. The final output result is the KP, KI, KD values ​​corresponding to the individual; otherwise, a new round of selection, crossover and mutation genetic operations are required.

The present inventors developed hepatitis C virus 6a/2a intergenotypic recombinants in which the JFH1 structural genes (Core, E1 and E2), p7 and the complete NS2 were replaced by the corresponding genes of the genotype 6a reference strain HK6a. Sequence analysis of recovered 6a/2a recombinants from 2 transfection experiments and subsequent reverse genetic studies revealed adaptive mutations in E1 and E2. Conclusion: The developed 6a/2a viruses provide a robust in vitro tool for research in HCV genotype 6, including vaccine studies and functional analyses.

The invention discloses an adaptive mutation particle swarm optimization algorithm. According to the adaptive mutation particle swarm optimization algorithm, a mutation operation is introduced into PSO (Particle Swarm Optimization), namely a mutation probability factor is introduced into a whole swarm position; the mutation operation usually serves as a trigger applied to generation stagnancy (premature convergence) under control of each generation or a prefix interval or an adaptive strategy. The algorithm is capable of jumping out the current searched local optimal position and searching again in a larger solution space, so that the solution space searching range is expanded while the swarm diversity is retained; the algorithm is capable of effectively carrying out global search, so that the capability of searching global optimal solution of the swarm can be improved. The adaptive mutation particle swarm optimization algorithm is capable of finding the global optimal solution to a maximum extent under the precondition that the global search is not carried out; and the classifier performance can be improved.

The invention discloses an AOD (argon oxygen decarburization) furnace energy consumption optimization method based on an energy carrier, and belongs to the field of data processing. The AOD furnace energy consumption optimization method includes the steps: determining an AOD furnace energy consumption optimization model parameter set according to a production process; building energy balance constraint conditions of the parameter set based on material balance; building an energy consumption optimization model for an AOD furnace energy carrying value according to the energy balance constraint conditions; solving the energy consumption optimization model by an adaptive mutation rate genetic algorithm to obtain the optimized value of an objective function of the AOD furnace energy consumption optimization model; optimizing/adjusting the burdening amount or the burdening proportion of an AOD furnace and adjusting/controlling material consumption of the AOD furnace according to the optimized value of the objective function of the AOD furnace energy consumption optimization model; and greatly reducing the comprehensive average energy consumption of the AOD furnace based on the energy carrier on the premise of ensuring indexes such as various processed and product performances of the AOD furnace to meet requirements by the aid of existing equipment conditions. The AOD furnace energy consumption optimization method can be widely used for the field of comprehensive average energy consumption control for the AOD furnace.

Genotype 7a has been identified recently, thus not much is known about the biology of this new, major HCV genotype. The present inventors developed hepatitis C virus 7a/2a intergenotypic recombinants in which the JFH1 structural genes (Core, E1 and E2), p7 and the complete NS2 were replaced by the corresponding genes of the genotype 7a strain QC69 and characterized them in Huh7.5 cells. Sequence analysis of 7a/JFH1 recombinants recovered after viral passage in Huh7.5 cells following 4 independent transfection experiments revealed adaptive mutations in Core, E2, NS2, NS5A and NS5B. In reverse genetic studies the importance of these mutations for improved growth kinetics was shown. Adapted 7a/JFH1 viruses showed growth kinetics, infectivity and RNA titers comparable to a previously developed 3a/JFH1 reference virus. Conclusion: The developed 7a/JFH1 viruses provide a robust in vitro tool for research in HCV genotype 7, including vaccine studies and functional analyses.