57 results about "Artificial bee colony optimization" patented technology

The invention discloses an aquaculture water quality short-time combination forecast method on the basis of multi-scale analysis. The method includes the steps that water quality time sequence data are acquired online and repaired; through empirical mode decomposition, the selected water quality time sequence sample set data are decomposed into IMF components and residual rn components, wherein the IMF components and the residual rn components are different in frequency scale; the IMF components and the rn components are classified, a manual bee colony optimization least square support vector regression machine, a BP neural network and an autoregressive sliding average model are respectively selected for forecast according to classifying features, and finally, all results are weighed and summed to obtain a water quality time sequence forecast result. According to the method, the original water quality time sequence data are decomposed into the components different in time frequency through the empirical mode decomposition, and change conditions in original water quality sequences can be mastered more accurately; advantages of the manual bee colony optimization least square support vector regression machine, advantages of the BP neural network and advantages of the autoregressive sliding average model are complemented and combined, and thus performance of a combined forecast model is effectively improved.

The invention discloses a diagnostic method for gas path faults of an aero-engine based on the sliding mode theory. The method comprises following steps: intelligently correcting a non-linear part-stage model of the aero-engine based on an artificial bee colony algorithm; obtaining a self-adaptive liner model of the aero-engine based on error feedback sliding mode control; designing an expansion interface estimation sliding mode observer, and achieving fault diagnosis, separation and reconstruction of an engine sensor; achieving fault diagnoses of gas path faults of the engine. The diagnostic method for gas path faults of the aero-engine based on the sliding mode theory has following beneficial effects: as for the gas path faults of aero-engine, a correction method for a high-precision part-stage mode for the engine is put forward with the purpose of improving reliability and safety of a system; a new way of thinking for a self-adaptive linear model for the engine is put forward for providing a new strategy of gas path faults of the aero-engine; difficulties of gas path faults of the aero-engine and sensor faults are solved so that an effective and highly-reliable fault diagnoses method is put forward.

The invention brings forward a constraint multi-target optimization method based on an improved artificial bee colony algorithm, for solving the defect of solving a multi-target optimization problem by use of a basic artificial bee colony optimization method. The constraint multi-target optimization method based on the improved artificial bee colony algorithm takes the material scheduling problem at a first emergency rescue phase as an application background and brings forward a food source initialization process integrated with reverse learning on the basis of the definition of a reverse solution for improving the quality of 50% of an initial solution. At the same time, for the purse of balancing the development capability and the exploration capability of an optimization process, the method integrates a reverse learning strategy and an extensive learning strategy into a honeybee search process so as to improve the search efficiency. The method constructs a non-linear deletion loss based many-to-many disposable consumption emergency material scheduling constraint multi-target optimization model, and embodiments are formed. Numerous test results indicate that compared to a conventional artificial bee colony optimization method, the method provided by the invention has the following advantages: more non-dominated leading-edge solutions are solved, the distribution on a solution space is wider and more uniform, and the solution are closer to a Pareto optimal solution.

The invention relates to an under-actuated UUV (unmanned underwater vehicle) depth control method and provides an under-actuated UUV depth control method based on artificial bee colony optimized model predication to solve the problem of UUV depth control under complex horizontal angle constraint conditions. The method includes: firstly, acquiring an incremental vertical plane predication model of an under-actuated UUV; secondly, unifying horizontal angle control input constraint conditions as H delta U (k) </=gamma; thirdly, by model predication control, turning the UUV depth control problem into a secondary planning problem under the constraint conditions; fourthly, obtaining a global optimum position of a nectar source; fifthly, obtaining control input at a moment k; sixthly, guaranteeing that the UUV reaches an appointed UUV diving depth R (k+1) to complete diving. The under-actuated UUV depth control method is applicable to the field of UUV depth control.

The invention discloses an extreme learning machine method for improving artificial bee colony optimization. The method is characterized by comprising the following steps: 1, generating initial solutions for SN individuals; 2, carrying out global optimization on a connection weight Omega and a threshold b of an extreme learning machine; 3, carrying out local optimization on the connection weight Omega and the threshold b of the extreme learning machine; 4, if food source information is not updated in a certain time, converting employee bees into investigation bees and returning to the step 1 to re-initiate the individuals; and 5, extracting the connection weight Omega and the threshold b of the extreme learning machine from the optimum individual, and carrying out verification by using a test set. The method disclosed in the invention can be used for better overcoming the defect that the traditional extreme learning machines are relatively bad in results when being applied to classification and regression; and compared with the traditional extreme learning machines and SaE-ELM algorithms, the method has relatively strong robustness and can be used for effectively improving the results of classification and regression.

The invention relates to a planetary gear box fault diagnosis method. The method comprises the following steps: firstly, decomposing and reconstructing a signal by utilizing salp swarm group optimization-variational mode decomposition (SSO-VMD); then, extracting fault features from multiple domains, and carrying out dimension reduction processing by adopting improved supervised self-organizing incremental learning neural network landmark point isometric mapping (ISSL-Isomap); and finally, using an artificial bee colony optimization support vector machine (ABC-SVM) classifier to carry out diagnosis and identification. According to the method, the problem of parameter selection in the VMD algorithm is solved, and the problem of information redundancy of multi-domain features is solved. A planetary gear box fault diagnosis experiment result shows that the method can effectively identify each fault type and has a great practical value.

A ducted unmanned aerial vehicle anti-sway method based on optimized quadratic form control of artificial bee colony includes eight steps: 1, the mathematical model for pendulum oscillation is built; 2, control structure and control law are designed; 3, the parameters of the artificial bee colony algorithm and the employed bee colony are initialized; 4, the performance indicator function of the linear quadratic form is calculated according to individual parameters; 5, worker bees select bee individuals with better fitness as leading bees according to the fitness value of each employed bee, and each worker bee continues to seek for honey sources near the leading bee solution space and the fitness value is calculated; 6, if the number of searches Bas is greater than the set threshold, the employed bees seek for new honey sources again, namely parameter values are initialized again; 7, by the greedy selection method, the positions of the employed bees are updated with larger fitness values, and searches proceed near solution spaces; and 8, the Step 4 is carried out repeatedly until T > Tmax, and the optimum component weighted value parameter, the optimum feedback gain matrix and the optimum fitness value are output.

The invention relates to a fault diagnosis method for a photovoltaic array based on a semi-supervised extreme learning machine, which comprises the steps of firstly, acquiring an output voltage-current curve of the photovoltaic array through an acquisition device; then performing feature extraction on the current-voltage curve, and constructing a fitting feature output equation with an adjustmentcoefficient; secondly, solving the adjustment coefficient by adopting a nonlinear least square method based on particle swarm-trust region reflection optimization; obtaining a feature standardizationequation by performing item shifting and standardization on the feature output equation; furthermore, carrying out fault identification on the photovoltaic array in combination of a small number of label samples and a large number of label-free samples by adopting a semi-supervised extreme learning machine based on artificial bee colony optimization as a classifier; and finally, regularly measuring a current-voltage curve of normal operation of the photovoltaic array to update the standardized equation so as to adapt to natural aging of the photovoltaic array.

The invention discloses a crowd evacuation simulation method and a device based on optimizing positive emotion infection. For the trust relationship between individuals, an emotional contagion networkis constructed; For the emotional contagion network, a positive emotional contagion model is constructed. The process of emotional infection was analyzed in a parameterized manner, and the infectionprobability of each node in the emotional infection network was calculated by using continuous-time Markov chain. Construct the optimization model of maximizing positive emotional infection; Using artificial bee colony optimization emotional infection algorithm and the infection probability of each node, the optimal objective function value of a given groomer in the model of maximizing positive emotional infection optimization problem is calculated. Using the optimal objective function value of the given dredger, the optimal position of the safety dredger can be calculated. The present disclosure provides guidance for crowd evacuation by leveraging optimized positive emotional contagion, provides new solutions to public safety issues, and provides guidance for emergency management.

The invention discloses a method for identifying key protein using an artificial bee colony optimization algorithm of a foraging mechanism. The method comprises: converting a protein-protein interaction network to an undirected graph, obtaining ribonucleic acid genetic expression values corresponding to protein, preprocessing edges and nodes of the protein-protein interaction network, establishing a dynamic protein-protein interaction network, selecting known key protein as a honey source, honey bees searching neighbourhood of the honey source, following bees searching neighbourhood of the honey bees, updating the honey sources, investigating bees searching new honey sources in a global manner, updating the honey sources, and generating the key protein. The method can accurately identify the key protein. Results of simulation experiments show that sensitiveness, specificity, positive predictive values, negative predictive values and other performance indexes are relatively excellent. Compared with other key protein identification method, the identification process of key protein realized by combining optimizing characteristics of artificial bee colony with characteristics of the protein-protein interaction network improves identification accuracy rate of the key protein.

The invention relates to the technical field of bioinformatics and provides a method for mining epistasis loci of an artificial bee colony optimized Bayesian network, including four steps S1 to S4. The method for mining epistasis loci of the artificial bee colony optimized Bayesian network, comprises firstly using three stages of expansion, contraction, and symmetry detection to calculate the Markov blanket of nodes through conditional mutual information so as to construct an initial nectar source network structure; then, based on the initial nectar source, randomly adding, subtracting and reversing edges to generate new nectar source until the maximum number of initial nectar sources is reached. The three operations (collecting bees, observing bees, and reconnoitering bees) of artificialbee colony algorithm and the BIC and MIT scoring method of the Bayesian network are configured to evolve the structure of the Bayesian network, find the optimal network structure, quickly and accurately obtain the epistasis gene loci that affect phenotypic traits, and assist the gene function mining.

The invention discloses an abnormal traffic detection device of an energy internet information support network, which realizes identification of abnormal traffic by performing traffic sampling, feature extraction and classification on the information support network. According to the method, frequency-domain features of uplink traffic and downlink traffic data between a server and a core switch are extracted based on a wavelet packet analysis theory, an energy Internet information support network traffic time-frequency domain mixed feature set is constructed, and an artificial bee colony optimized support vector machine is used as a classification model. According to the device, support is provided for accurate and rapid verification of traffic anomaly and security situation awareness of the communication network of the intelligent substation and guarantee of stable operation of an information physical tight coupling power system.

The invention discloses an ore rock strength soft sensing method based on adaptive artificial bee colony optimization. The method of the invention adopts a three-layer perception neural network as a soft sensing model for ore rock strength, and utilizes the adaptive artificial bee colony algorithm to optimize a connection weight and an offset value of the designed neural network. In the adaptive artificial bee colony algorithm, search scaling factors are generated adaptively according to the feedback information of adaptive values, and a Gaussian mutation strategy based on neighbor optimal individuals and global optimal individuals is designed to adaptively generate new individuals. The method of the invention can raise the precision of soft sensing of the strength of ore rocks, and raises the efficiency of measuring the strength of the ore rocks.

The invention discloses a rare earth ore district underground water ammonia nitrogen concentration prediction method with Gauss artificial swarm optimization. A support vector machine is used as a rare earth ore district underground water ammonia nitrogen concentration prediction model; a Gauss artificial swarm algorithm is used for optimizing and designing a penalty factor C of the support vector machine, a radial base core parameter g and a parameter Epsilon in a non-sensitive loss function. In the Gauss artificial swarm algorithm, an outstanding individual in each individual neighborhood and an individual average value in a neighborhood thereof are merged into a Gauss mutation strategy to generate new individuals; in addition, a reverse study strategy using the optimum individuals as reference points is executed in a bee searching process. The method has the advantage that the prediction precision of the rare earth ore district underground water ammonia nitrogen concentration can be improved.

The invention discloses a self-adaption artificial swarm optimization method based on historical information in the running process. The parallel research is conducted through various strategies, and the use ratio of the strategies is unceasingly regulated and guided according to the historical information in the running process. The Gaussian distribution which the important parameter Limit used for balancing the overall search cost in the method belongs to is unceasingly regulated according to the historical information in the running process. The use ratio of the search strategies is dynamically distributed through the self-adaption artificial swarm optimization method according to the historical information in the running process, and the important parameter for balancing the overall search cost is dynamically regulated. Under the condition that the numbers of the calculation times of fitness functions are the same, the method has a more superior and efficient function optimization effect.

The invention discloses a wine quality discriminating method based on reABC-SVM. The method comprises the steps of 1, extracting a sample from the wine, and measuring contents of certain materials in a wine sample for forming a training sample set; and 2, performing dynamic adjustment on a penalty factor and a nuclear function parameter of a support vector machine by means of an improved artificial bee colony optimization algorithm, outputting an optimal parameter, and establishing a wine discriminating model by means of an optimal parameter, thereby realizing quality discriminating on the wine. According to the wine quality discriminating method, an SVM parameter can be optimized by means of an improved ABC algorithm, thereby obtaining a most appropriate classification model for realizing wine quality classification, and furthermore settling a wine quality discriminating problem.

The invention discloses a blind source separation main lobe interference resisting method and device based on an improved artificial bee colony, and relates to the technical field of signal processing, and the blind source separation main lobe interference resisting method based on the improved artificial bee colony comprises the steps: firstly receiving a to-be-processed mixed signal; carrying out decentration preprocessing on the to-be-processed mixed signal to obtain a preprocessed signal; further, determining a separation matrix corresponding to the preprocessed signal according to a preset artificial bee colony optimization algorithm, a preset blind source separation algorithm and a preset cost function; and finally, according to a blind source separation algorithm, the separation matrix and the preprocessed signal, calculating a main lobe interference resistant target signal, so that the main lobe interference resistant processing of the signal can be realized, the iteration speed is high, and the main lobe interference resistant effect is good.

The invention discloses a simple and efficient optimization method for improving an artificial bee colony. The method comprises the following steps of (1), initializing an algorithm; (2), calculating honey amount of each honey source according to an improved honey source initializing formula; (3), making bees search a new honey source according to a searching strategy based on normal distribution, and performing preferential selection on the new honey source and the old honey source; (4), calculating probability at each honey source position, and selecting a guiding bee for being followed by following bees according to the probability; (5), after number of searching times of one honey source reaches a cycling upper limit and updating does not occur, changing the guiding bee to a spy bee for searching; and (6), if a highest number of evolution generations is reached, outputting the position coordinate of an optimal honey source, and otherwise, returning to the step (3). According to the method of the invention, a normal distribution theory is introduced into an initializing process and a searching optimizing process of the artificial bee colony algorithm, thereby effectively improving global optimization solving capability and optimization precision.

The invention relates to the technical field of image processing, and provides a pantograph-catenary image registration method and device optimized by chaotic heuristic search, aiming at solving the technical problems of slow convergence speed and insufficient registration precision when artificial bee colony optimization algorithm is applied to pantograph-catenary image registration. For this purpose, the chaotic heuristic search optimized pantograph-catenary image registration method of the invention comprises the following steps of generating an initial population according to a preset reference pantograph-catenary image, wherein each individual in the initial population stores a predetermined number of registration parameters; optimizing the registration parameters of pantograph-catenary images by a chaotic heuristic search algorithm; obtaining the pantograph and catenary images to be registered by spatial transformation of the registration parameters. The invention can enhance thelocal search ability of the algorithm, accelerate the convergence speed of the algorithm, and improve the accuracy of pantograph-catenary image registration.

The invention discloses a gesture recognition method based on variational mode decomposition and a support vector machine, which comprises the following steps: firstly, performing decomposition and noise reduction on surface electromyogram signals by using a variational mode decomposition algorithm, and meanwhile, realizing optimal selection of parameters of the variational mode decomposition algorithm by using an improved artificial bee colony optimization algorithm, thereby avoiding blindness of manual selection; then, on the basis of the decomposed variational mode components, 4-order autoregression model parameters and fuzzy entropy are extracted, a multi-scale feature set is constructed, and surface electromyogram signal features can be effectively extracted; and finally, performing classification and gesture recognition by using a multi-classifier constructed by a support vector machine optimized by an improved cuckoo algorithm, and realizing a relatively good classification effect under the condition that the number of samples is relatively small. According to the method, the parameter selection problem of the variational mode decomposition algorithm is solved, the characteristics of the electromyographic signals can be more effectively obtained, and the accuracy and speed of gesture recognition based on the surface electromyographic signals are improved.