16 results about "Artificial bee colony algorithm" patented technology

The present application relates to the technical field of astronomical satellite mission planning, in particular to a method and system for solving short-term mission planning of astronomical satellite. The method comprises: step 1) constructing a short-term mission planning mathematical model of the astronomical satellite to be planned, and abstracting the mission planning problem as a maximized optimization problem; step 2) solving by using a hybrid search strategy artificial bee colony algorithm, searching for excellent solutions by the employed bees based on an "elite solution guided search" strategy, and searching by the follower bees based on a "neighborhood optimal solution update" strategy, so as to accelerate the solution and improve the solution accuracy. Compared with the basic artificial algorithm, the method has the advantages of fast convergence speed, high solution accuracy and strong optimization ability, and has fewer control parameters than other swarm intelligence algorithms; in the short-term mission planning problem of the astronomical satellite, the method can obtain higher task completion degree and greater observation benefit.

The application discloses a kind of underwater defect detection robot path planning and tracking control method based on multi-data fusion, to solve the problem of poor positioning accuracy, unstable path tracking and insufficient defect approaching ability in GPS denial environment.The method uses improved artificial bee colony algorithm to optimize BP neural network assisted Kalman filter, fuses INS, DVL, electronic compass and depth gauge data, realizes high-precision integrated navigation;Secondly, combined with global "bow" shape coverage and local pure tracking approaching strategy, generate full coverage scanning path and fine approach to high priority defect points;Finally, based on pre-defined time sliding mode controller, cooperate disturbance observer and adaptive mechanism, realize the fast robust tracking of path.Experiments show that the positioning error is only 0.15m after DVL failure, the local approaching accuracy is 0.05m, the path tracking convergence time is 5s, the steady-state deviation is 0.03m, which is significantly better than the traditional method.The application is suitable for automatic defect detection of underwater structures such as bridge pier, dam, submarine pipeline, etc.

The application discloses a pre-hospital care helicopter optimization deployment method fusing weighted K-means and IABC, first, patient data is acquired and the geographical position coordinates thereof are standardized pretreated, the patients are classified according to the injury degree and corresponding weight values are given, and a weighted data set is formed; subsequently, a weighted clustering square error and a silhouette coefficient are calculated, the elbow rule and the silhouette coefficient method are cooperatively decided, and the optimal helicopter deployment number k is determined; then, the weighted data set is taken as input, and the initial deployment position coordinates of the k helicopters are solved through a weighted K-means algorithm; then, a single-target fitness function integrated by three sub-functions including service coverage, rescue response time and economic cost is constructed, and a multi-target optimization problem is integrated into a single-target optimization problem; finally, an improved artificial bee colony (IABC) algorithm with a directional learning mechanism is adopted, the initial deployment position of the helicopter is taken as an initialization population for iterative updating, and the final pre-hospital care helicopter deployment coordinates are output.

The application discloses a kind of for ship section painting plan scheduling method, consider VOCs exhaust treatment equipment consumption electric energy and the carbon emission caused by LNG gas, consider the constraint of human resources and establish the multi-schedule plan scheduling model of two kinds of tasks including sand washing and spraying.A kind of improved artificial bee colony algorithm is proposed to effectively obtain approximate optimal solution within reasonable time, and a three-dimensional coding solution mechanism is designed based on the model.In the algorithm, the search efficiency of the algorithm is increased by mixing greedy random adaptive search algorithm and variable neighborhood search algorithm, which can be well used for ship section painting plan scheduling problem.

The application discloses a method for cooperative jamming based on intelligent optimization algorithm, comprising: constructing a jamming decision model, the jamming decision model comprising a cooperative jamming decision matrix, a gain matrix, a jamming matrix, a jamming gain matrix, a jamming bandwidth ratio factor, a jam-to-signal ratio, and a jamming benefit; establishing an objective function and a constraint condition of the jamming decision model according to the jamming benefit; and using an artificial bee colony algorithm to take the jamming benefit as a fitness function and optimize the cooperative jamming decision matrix A. In different complex electromagnetic spectrum environments such as limited spectrum resources and the same frequency band shared by jamming devices and illegal users, the limited jamming resources are reasonably distributed under the condition that the jamming device of the own side can normally communicate, so that greater jamming benefit is achieved; the algorithm convergence speed and search ability are improved, and the method is helpful for making a decision with higher jamming benefit in a shorter time.

The application provides a new energy automobile waste battery recycling box site selection planning method, system and terminal equipment, comprising: dividing a planning area and obtaining relevant information of each area; predicting the number of new energy automobile waste battery recycling in each area through a smoothed grey model; determining a network simulation model, mapping each area parameter information to bee colony parameter information in an improved artificial bee colony algorithm; performing simulation analysis through the improved artificial bee colony algorithm, solving the simulation model, and obtaining recycling box site selection layout site information; and determining the number of new energy automobile waste battery recycling boxes configured at each site according to the site service area and the predicted recycling amount of waste batteries in the area. The application can standardize the site selection planning process of new energy automobile waste battery recycling boxes, reduce the early-stage fund investment before the installation of waste battery recycling boxes, and promote the efficient recycling of retired waste batteries.

This invention belongs to the field of hybrid DC and AC microgrid optimization configuration technology, specifically involving a method and device for hybrid DC and AC microgrid optimization configuration based on a chaotic artificial bee colony algorithm. It establishes a system model including photovoltaic, wind turbine, battery, and AC / DC loads, setting minimization as the objective. Multiple constraints are set. An artificial bee colony algorithm incorporating chaotic mapping is employed to iteratively search within the feasible solution space: diverse solutions are generated through chaotic initialization, high-quality solutions are retained through a roulette wheel selection mechanism, and stagnant solutions are replaced promptly through a scout bee mechanism to escape local optima. Finally, the configuration quantity and capacity that minimize the levelized cost of electricity (LCOE) are determined from all feasible schemes. The method enhances the algorithm's global search capability through a chaotic mechanism and ensures the feasibility and safety of all intermediate and final solutions based on constraint verification. The obtained optimal configuration scheme directly corresponds to the optimal long-term operating economy, providing a basis for design.

This invention discloses an improved near-infrared spectral wavelength selection and modeling method for artificial bee colony algorithms, comprising the following steps: data preparation, chaotic initialization, fitness evaluation, establishing a PLS model based on wavelength subsets, using the regularized fitness function of RMSECV as the fitness Fitnessxi of nectar source xi; searching for foraging bees; searching for observation bees; searching for scout bees; outputting the optimal wavelength subset Sbest and establishing the final PLS model. The beneficial effects of this invention are: the method effectively improves convergence speed and accuracy; chaotic initialization improves the quality of the initial population, avoiding the algorithm from prematurely falling into local extrema; the adaptive perturbation factor balances the algorithm's global search and local exploitation, improving the ability to find the optimal wavelength combination in high-dimensional discrete space, and the selected wavelength subset has stronger representativeness, resulting in a final PLS model with lower RMSEP (root mean square error of prediction) and better robustness.

This invention discloses a human-machine collaborative method for balancing locally destructive dismantling lines. The method includes: dividing decommissioned electromechanical products into dismantling tasks; determining the dismantling priority relationship between tasks, as well as the dismantling profit, dismantling direction, selectable operators, and selectable dismantling modes for each task; determining the dismantling time and cost for each task by different operators under different dismantling modes; establishing a bilateral human-machine collaborative dismantling line model for locally destructive dismantling guided by workstations, smoothing indicators, robot numbers, and profit, and converting it into a mixed-integer linear programming model, while setting dismantling constraints; constructing an encoding / decoding strategy based on the dismantling constraints, and solving it using an improved multi-objective artificial bee colony algorithm based on reinforcement learning to obtain the dismantling scheme. This invention can obtain a dismantling line balancing scheme with superior overall performance in a short time, significantly improving dismantling efficiency and economic benefits, while reducing energy consumption and environmental impact.

The present application relates to the technical field of crane unstacking, and discloses an unmanned crane unstacking method and equipment fusing height limit constraint, which comprises the following steps: obtaining the initial state of the warehouse area, target demand and height limit threshold; generating an initial population with the target steel plate moving sequence code; in the decoding stage, if there is an obstacle steel plate above the target steel plate to be moved, then according to the current height of the candidate moving-in stack position, whether it is an idle stack position, whether it is over the limit after moving in and other state information, the moving difficulty evaluation value is calculated; the obstacle steel plate is moved into the stack position with the minimum evaluation value, and the unstacking step sequence is generated; the population is iteratively updated by using the artificial bee colony algorithm, and the unstacking scheme with the optimal fitness is output. By introducing the height limit constraint and the moving difficulty evaluation mechanism, the present application effectively avoids the risk of stack overheight, reduces the redundancy of unstacking steps, and significantly improves the operation efficiency of the unmanned crane under strict height limit constraint.

The invention discloses a stability control method for continuous reconstruction motion of a metamorphic robot based on nonlinear analysis, which comprises the following steps of: 1, establishing a continuous reconstruction kinematics model of the metamorphic robot by using a spinor theory so as to obtain homogeneous coordinates of centroids of all motion components; 2, establishing a pitch angle-vertical displacement two-degree-of-freedom nonlinear vibration model for continuous reconstruction of the metamorphic robot, and deducing an improved ZMP criterion containing foot wheel nonlinear vertical stiffness; and 4, converting the complex continuous reconstruction nonlinear dynamic model of the metamorphic robot into a simple linear system model for adjusting a centroid mechanism through a sliding block expected displacement deviation solver, and further converting the motion control of each leg joint with continuous reconstruction stability into the displacement control of a sliding block in the centroid mechanism. Therefore, stability control over the metamorphic robot in the continuous reconstruction process is achieved through the LQR controller optimized based on the artificial bee colony algorithm.

The application relates to the field of information technology and discloses a mobile edge computing task offloading method and device based on an artificial bee colony algorithm. The initial position of the artificial bee colony algorithm is optimized by adopting a reverse learning method, the initial position of the honey source is determined by selecting the optimal solution from the forward solution and the reverse solution by using a greedy strategy, the search range is expanded, the optimal solution of a problem is found, and the position of the honey source is improved. Then, the cosine algorithm is introduced into the position updating strategy of the employed bee, the global optimal solution of the cosine algorithm is used to guide information to enhance the global search capability of the algorithm, the oscillation characteristics of the cosine algorithm are used to balance the global exploration and local development capability, the local search capability is enhanced, the search precision is improved, the calculation task offloading strategy of the system is ensured to be optimal, the time delay and energy consumption of the mobile edge computing task offloading are reduced, and the like.

The present application relates to the field of unmanned ship control, and provides a collision avoidance path tracking control method, system, device and medium, comprising calculating a forward distance, calculating a heading error according to the forward distance, and performing heading correction; calculating a measurement step length according to a curvature change rate, calculating a predicted path error using the measurement step length, and obtaining optimal parameters according to the predicted path error to obtain optimized navigation parameters; calculating a collision avoidance radius according to a dangerous radius, constructing an unmanned ship obstacle avoidance constraint according to the collision avoidance radius, and obtaining an obstacle avoidance route according to the unmanned ship obstacle avoidance constraint; constructing an optimization vector using artificial bee colony algorithm parameters, optimizing the optimization vector using an optimization objective function, obtaining optimized algorithm parameters, and constructing an optimized artificial bee colony algorithm; calculating a dynamic weight coefficient and an adaptive scaling factor, solving the optimal control instruction in the optimized artificial bee colony algorithm, and finally manipulating the unmanned ship through the optimal control instruction and the heading correction data.

The application discloses a kind of grain quality, safety and storage risk automatic scoring service implementation method.The method is input with multi-source index, first adopt decimal scaling standardization unified dimension, and obtain characteristic vector by independent component analysis (ICA) and time sequence derivation construction;Subsequently, two layers of random forest integration are constructed: the first layer is trained on bootstrap sampling data by multiple random forest individuals and outputs probability / score, and the performance of the individuals is screened out by joint optimization of feature subset and hyperparameter using artificial bee colony algorithm (ABC) combined with K-fold cross-validation;The output of the retained individuals is spliced with the key original / derived index vector before learning in the second layer, and finally the original grain quality score (QG) / safety risk score (SR) / storage risk score (WR) and comprehensive score (CS) are given.The application improves generalization and robustness while unifying data caliber, and the scoring results can be directly used for business decisions such as collection and storage release, re-inspection interception and storage disposal.

The present application belongs to the technical field of dispatching control, and particularly relates to a kind of dispatching control method for building heat pump unit.The method first collects building historical heat load time series data and meteorological data, selects core meteorological influence factor, obtains future heat load demand prediction value through LSTM model combined with meteorological correction;Then build a scheduling model with optimal energy consumption as the target, introduce unit operation constraints, and generate pre-scheduling feasible solution by using artificial bee colony algorithm global optimization;Subsequently, an improved deep Q network with time sequence attention mechanism is constructed, and the final operating parameters of each unit are output;Finally, a hierarchical integral PID controller is built, and the PID parameters are optimized by improving the double heuristic algorithm to realize accurate control of the unit.The present application solves the problems of existing heat pump scheduling without pre-judgment, high energy consumption, unbalanced load distribution and poor control stability, and can realize low consumption, stable and high precision operation of heat pump unit, and adapt to the energy management and control needs of intelligent building and green building.