35 results about "Bacteria foraging" patented technology

An improved transformer oil-paper insulation medium response equivalent circuit parameter identification method comprises the steps that a return voltage method is adopted to measure return voltage peak, peak time and return voltage initial slope of a transformer oil-paper insulation system in a field; an extension Debye model is adopted to simulate an equivalent circuit of the transformer oil-paper insulation system; characteristic quantities of the return voltage peak, peak time and return voltage initial slope are established to solve improved mathematical models of a medium response equivalent circuit parameters, wherein the equivalent models of n relaxed branch circuits have 2n+2 model parameters to be solved, and accordingly a corresponding number of nonlinear equation systems are needed to be established through multiple return voltage testing cycles for solution; the solution of the nonlinear equation systems is converted into solution of the optimization problem of an objective function minimum value, a PSO mixed bacteria foraging optimization algorithm is adopted to identify the equivalent circuit parameters and calculate a return voltage curve, the return voltage curve is compared with a return voltage measurement curve to find that the main time constants of the two curves are identical, and the oil-paper insulation state of a transformer can be accurately judged.

The invention discloses a reactive power optimization method of an electrical power system. The bacterial foraging algorithm and the particle swarm optimization algorithm are combined to be applied to the reactive power optimization of the electrical power system. The reactive power optimization method of the electrical power system comprises the following steps: adopting a Newton-Raphson load flow calculation procedure to provide values of each state variable and a network loss value for optimal calculation; setting a reactive power optimization model; initializing a bacterial colony; invoking load flow iterative program appraisal to record an adaptation degree and an optimal value of bacteria; carrying out chemotaxis operation; remaining a good population and breeding the population; carrying out migration operation, and the bacteria die or are born again in a certain probability; and updating the bacterial colony, and outputting a reactive power optimization result after the rated number of iterations is reached. The reactive power optimization method of the electrical power system introduces the colony in an optimal solution, avoids blind and random problems, can exceed a locally optimal solution, combines an existing bacteria foraging optimization algorithm with the particle swarm optimization algorithm, lowers transmission losses by controlling an engine, reactive power output of reactive compensation equipment and tapping points of an adjustable transformer, has the advantages of being rapid in convergence, efficient and stable, and is suitable for resolving the problem of reactive power optimization in the electrical power system.

The invention discloses a hybrid intelligent optimization method and belongs to the technical field of artificial intelligence and data mining. A genetic optimization algorithm and a bacterial foraging optimization algorithm are organically combined by the hybrid intelligent optimization method. First, a preliminary superior solution is obtained through the breadth searching capacity of the genetic optimization algorithm and serves as an initial bacterial population in the posterior bacterial foraging algorithm, by fully utilizing chemotaxis, duplicating and dispersing operation of the bacterial foraging algorithm, excellent individuals are generated continuously, and finally the solution converges towards the optimal solution. On the basis of the technical scheme, further improvements are made in the four aspects of genetic selection operators, optimal joint pints, bacterial chemotaxis and duplicating operation. Compared with the prior art, the convergence speed and the convergence precision of an optimal solution set can be improved, and the hybrid intelligent optimization method is more extensive in applicability.

A processing scheme optimal selection method in a cloud manufacturing environment belongs to the technical field of manufacturing resource optimization. The aim of the invention is to acquire the basic attribute of the cloud manufacturing resources as an evaluation index of the selection of the cloud manufacturing resources, and to establish a processing scheme selection mathematical model based on the optimization of bacteria foraging and to produce an optimal selection method in the cloud manufacturing environment. In the invention, a multi-objective optimization mathematical model is constructed by a production cost objective function, a production time objective function, a processing quality objective function and other evaluation index objective functions, and then optimal selection of the processing scheme in the cloud manufacturing environment is carried out. The invention aims at designing a processing scheme selection method based on a bacteria foraging algorithm for manufacture resource selection in a machinery manufacturing process in the cloud environment so as to provide a reasonable suggest for an enterprise decider in processing scheme selection and to further raise the product quality and the enterprise profit.

The invention provides a mid-and-long term runoff forecasting method based on a bacteria foraging optimization algorithm, and belongs to the technical field of hydrologic forecasting. In the method, firstly a circulation index which has a large correlation coefficient and has physical influence on the runoff of a drainage basin to be forecasted is taken as a forecast factor, and the forecast factor value is subjected to normalization processing; then the historical samples of the drainage basin to be forecasted are selected and are divided into a training set and a testing set; a support vector regression machine (SVR) model is trained by means of the training set, the parameter value of the model is determined by means of the bacteria foraging optimization algorithm, and the bacteria with the maximum adaptability value is output; the bacteria is decoded, and the optimum value and the preliminary forecasting result of the SVR model parameter are obtained; the preliminary forecasting result is compared with the testing set, and the error is analyzed, if the error is within a set scope, the final forecasting result is outputted. According to the invention, the forecasting accuracy, the generalization ability and the practicality of the mid-and-long term runoff forecasting method employing the SVR model are improved, and the mid-and-long term runoff forecasting method can serve as an effective method for mid-and-long term runoff forecasting.

The invention provides a pet feeding method and system based on an internet of things, wherein the pet feeding method based on the internet of things comprises the following steps that types, sexes, ages, heart rates, respiratory rates, body temperature, active values, feeding types, feeding amounts, current images and current weight of pets are collected to form an influencing factor matrix X, and the influencing factor matrix X is uploaded to a server, wherein the feeding types and the feeding amounts form decision variables; an Elman neural network is used in the server to establish a complex nonlinear relationship between the influencing factor matrix X and pet health indexes to obtain pet feeding models; an MOPSO (Modified Bacteria Foraging Optimization) algorithm is used for optimizing the pet feeding models to obtain a set of optimal solutions of the decision variables; the set of the optimal solutions of the decision variables is taken as recommended decisions X<*> of the pets, and the recommended decisions X<*> of the pets are issued to terminal equipment of a user to be displayed through the server; the user feeds the pets according to the recommended decisions X<*> displayed by the terminal equipment. By using the pet feeding method and system based on the internet of things disclosed by the invention, an optimal pet feeding scheme can be determined to build better living environments for the pets.

The invention provides a dynamic source routing method based on a genetic-bacterial foraging optimization strategy. The method proposes a genetic-bacterial foraging optimization algorithm to perform route selection aiming at a DSR (Dynamic Source Routing) protocol and comprehensively considering node energy information, that is, after searching out multiple routes to a destination node, coding initialization is carried out on a path, a GA algorithm is firstly started, and the solving speed is fast by using the GA algorithm, and multiple groups of optimized paths are quickly searched out, thatis the location of the maximum probability of an optimal path, and the location is used as the initial position distribution of the flora of a BFO (Bacterial Foraging Optimization) algorithm; and in order to make up for the shortcoming of the low solving precision of the GA algorithm, the optimal path is searched out by using the characteristic that the BFO algorithm can search out an extreme value very easily. The optimization strategy proposed by the method improves the route selection efficiency and precision without changing the complexity of the DSR, and the characteristic that the convergence of the algorithm achieves the global optimal solution is proved. A simulation experiment shows that the method is feasible and applicable, and also has good experiment results.

The invention discloses a distribution center site selection method based on a bacterial foraging optimization algorithm. The method comprises the steps of 1) obtaining a distribution center and a historical distribution address set of a target city; 2) constructing a distribution center site selection model; and 3) implementing a bacterial foraging optimization algorithm. The method of the invention can perform modeling by using historical distribution addresses and a candidate distribution center and is more in line with distribution center site selection conditions in the presence of large-scale historical distribution addresses in reality, so that the site selection and the distribution can be combined to improve the accuracy of the site selection of the distribution center, reduce thedistribution cost, improve the timeliness of distribution, and improve customer satisfaction.

The invention discloses a hydropower station scheduling method and a system based on particle swarm-bacterial foraging. The method is applied to the hydropower station optimal scheduling field. The method aims at the characteristics of high-dimensional nonlinearity and dynamism of hydropower station optimization scheduling. one particle in the particle swarm algorithm is regarded as an operation strategy of a hydropower station; an optimal result is obtained by using a hybrid algorithm. The method comprises the following steps of: calculating and updating the positions and speeds of particlesby using a PSO algorithm; according to the method, the particle swarm optimization algorithm is adopted to complete the search of the whole space and memorize the optimal information of individuals and populations of particles, then part of particles in the particle swarm are regarded as a bacterium, the function of local search is completed through the tendency operation of the BFO algorithm, theadvantages of the two algorithms are brought into full play, and the precision and efficiency of hydropower station optimization scheduling solution are improved.

The invention discloses a network security resource distributing method based on a level bidding mechanism. The network security resource distributing method based on the level bidding mechanism comprises the following steps: collecting available bandwidth resources in a network, wherein the bandwidth resources are total flow bandwidth of wireless frequency resources in a heterogeneous network; collecting security resource service application of user equipment; distributing network safety resources by using a bacteria foraging optimization algorithm; distributing bacteria into swimming bacteria and rotating bacteria according to movement modes; calculating fitness values of the bacteria, and storing the current fitness values of the bacteria as the most suitable value; searching each bacterium at an original position and calculating a fitness value at a new position; if the fitness value of bacterium at the new position is greater than that of the bacterium at the original position, the original position is replaced; if searching times reach a threshold value, randomly generating a new position; if the searching times are smaller than or equal to the threshold value, carrying out a next step; recording the position where the fitness value is maximum and a corresponding fitness function value; and repeating for multiple times to obtain the distributed resource maximum value of network security resources.

The invention provides a fault diagnosis method and device for a cascaded H-bridge photovoltaic inverter, and belongs to the technical field of photovoltaic power generation. The method comprises the following steps: setting a cascaded H-bridge photovoltaic inverter topological structure, and controlling the grid-connected current of the cascaded H-bridge photovoltaic inverter; analyzing the fault mode of the cascaded H-bridge photovoltaic inverter; carrying out feature extraction by adopting wavelet packet energy entropy; an improved bacterial foraging optimization algorithm is adopted to solve the optimal kernel function bandwidth and bias; and according to the optimal kernel function bandwidth and bias, training data by using a support vector machine and carrying out fault diagnosis. Compared with an SVM fault diagnosis method, the improved bacteria optimization algorithm is used to optimize the SVM fault diagnosis method, so that feature extraction of the switch tube fault of the cascaded H-bridge photovoltaic inverter is realized, and diagnosis and classification of the switch tube fault of the cascaded H-bridge photovoltaic inverter are realized. And the accuracy of open-circuit fault diagnosis of the three-phase cascaded H-bridge photovoltaic inverter can be effectively improved.

The invention discloses an image steganography method based on bacterial foraging optimization edge detection and XOR coding, and belongs to the technical field of image information hiding, and the method comprises the following steps: 1, fuzzifying the USAN region area of a carrier image, carrying out the fuzzy enhancement, and carrying out optimization by employing a bacterial foraging algorithm; 2, performing image defuzzification through an adaptive threshold to realize edge detection; 3, encrypting secret information; and 4, embedding the secret information into the edge points and the non-edge points of the carrier image by using XOR coding, quartering all pixel points during embedding, and ensuring that the secret information can be embedded into the pixels embedded into each quartered group according to a minimum pixel change principle. The method has large steganography capacity, the imperceptibility of a secret-loaded image can be well kept, and the security is high.

With the development of science and technology and the advent of the era of big data, the problem of social information security receives much concern. For example, the public security department identifies suspects from the streets, banks need identity authentication of customers, and the customhouse identifies the identity of exit-entry people. The means of identity authentication emerges in endlessly, such as password, fingerprint, ID card, RFID card and other ways of recognition, wherein face recognition is a popular research area at present. In recent years, many excellent talents have conducted research in the field of face recognition, and have made great achievement. In view of the weaknesses and shortcomings of the methods put forward by predecessors, the traditional bacteria foraging method is improved in the invention, and the improved algorithm is applied to face recognition, namely, classification is performed on an original face database by the improved bacteria foraging method first, and then, a target face is recognized by a comparison method. Experiments show that a method of the invention has very high recognition rate. The method of the invention mainly comprises a face input module, a face image preprocessing module, a face feature extraction module, a bacterial foraging algorithm training module, a target face image input module, and a target face image recognition module.

The invention relates to a sparse mobile sensing node perception coverage method balancing the packet loss ratio and data transmission time delay. A monitoring area is divided into square grids in thesame size, from an initial position, a mobile sending node selects the center of a neighbor grid of a present grid as position in next time, and a mobile sensing node path selection optimization model of the packet loss ratio and data transmission time delay is established; a modified bacteria foraging method is used to solve the optimization model, and an optimal movement path of the mobile sensing node is obtained; and the mobile sensing node senses data along the calculated optimal movement path, and all the monitoring area is covered. According to information including the position of themobile sensing node, the data storage capacity and data transmission time, the optimal movement path that can cover the whole monitoring area can be found, and the packet loss ratio and data transmission time delay can be reduced.

The invention relates to a bacterial foraging optimization method based on gravity induction. The method comprises the following steps: selecting optimization parameters X = {x1, x2, ..., xp} of a to-be-studied object; Converting the optimization value, namely fitness, of the to-be-studied object into a fitness function with the optimal value of 0: J = fitness (X), and optimally, Jmin = 0; Initializing gravity sensing parameters and bacterial foraging parameters; and optimizing by using a gravity sensing bacterial foraging algorithm. The step length can be automatically adjusted according to small ball energy attenuation, and the defect of traditional bacterial foraging is overcome; The step length is reduced from large to small, the iteration convergence speed is high when the initial step length is increased, and when the iteration convergence speed is close to global optimum, the step length is reduced, and the optimization precision is improved.