159 results about "Artificial bee colony algorithm" patented technology

The invention discloses an end-side cloud collaborative intelligent fire-fighting fire monitoring system based on image fusion, and relates to the technical field of intelligent fire-fighting, the system is composed of a plurality of functional modules, and the system comprises a multi-modal image fusion module which generates a dynamic scanning priority map based on prior data, distinguishes a natural heat source from an abnormal fire by using a dual-light fusion algorithm, and sends an image fusion result to a cloud server; a scanning area is divided according to the thermal risk grade, and the thermal imaging resolution is dynamically adjusted; the distributed edge computing module is used for carrying out space-time synchronization on cross-modal data through a multi-modal feature alignment network, and carrying out dynamic allocation on a CUDA core and CPU resources through adaptive computing scheduling; an improved artificial bee colony algorithm is adopted, the bandwidth of the multi-sensor data flow is dynamically allocated through a time-sharing multiplexing protocol, and three-dimensional path planning is carried out; and the end-side cloud collaborative decision module constructs a federated learning driven model sharing network, and each edge node trains a lightweight YOLOv5s pruning model based on local data.

The invention provides an unmanned aerial vehicle path planning method and system and a storage medium, and the method comprises the steps: constructing a three-dimensional path planning model of an unmanned aerial vehicle in a target flight region; solving the three-dimensional path planning model by using an improved artificial bee colony algorithm, obtaining the optimal flight path of the unmanned aerial vehicle under each target function, and summarizing the optimal flight path into a Pareto optimal solution set; constructing a plurality of decision intelligent agents in one-to-one correspondence with the plurality of objective functions, performing multi-dimensional scoring under different objective functions on each flight path in the Pareto optimal solution set by using the plurality of decision intelligent agents, obtaining a comprehensive score of each flight path, and determining a global optimal unmanned aerial vehicle flight path based on the comprehensive score; according to the method, through refined multi-dimensional constraint modeling, an improved multi-target artificial bee colony algorithm and multi-agent collaborative decision based on a near-end strategy optimization algorithm, full-process optimization from path generation to intelligent decision is realized.

The invention discloses a multi-unmanned aerial vehicle search and rescue task planning method based on ABC-DQN hierarchical optimization, and belongs to the technical field of multi-unmanned aerial vehicle cooperative search and rescue. According to the method, task planning is carried out on a search and rescue area applying multiple unmanned aerial vehicles based on an artificial bee colony algorithm and a deep Q network, and adaptive cooperative search and rescue of the multiple unmanned aerial vehicles is completed. According to the method, global planning of a search and rescue task and path planning of a local area are both constrained and optimized, scene analysis of a search and rescue area and selection of the number of unmanned aerial vehicles with different performances are firstly carried out, and the unmanned aerial vehicle of a central control node is determined; and then the central unmanned aerial vehicle completes static global region division by using an ABC algorithm, then unmanned aerial vehicles in a local region perform adaptive search and rescue path planning based on a DQN algorithm, and in a local search and rescue process, the local unmanned aerial vehicles and the central unmanned aerial vehicle perform information interaction. Once the target is determined to appear, the central unmanned aerial vehicle further reduces the global area and performs ABC algorithm division again until the search and rescue target is determined to be within the required range.

The invention discloses a high-pressure jet grouting pile construction optimization control method and system, and belongs to the technical field of pile foundation construction. According to the method, the pile body strength is predicted based on the XGBoost regression model, the HSIO hybrid swarm intelligent optimization algorithm is combined with the artificial bee colony algorithm and the seathead optimization algorithm, the construction parameter combination is dynamically adjusted, and optimal matching of the target strength and the real-time construction state is achieved. The method solves the problems of parameter setting deviation and non-uniform pile forming quality caused by the fact that traditional construction depends on artificial experience, is particularly suitable for high-precision construction requirements under complex geological conditions (such as karst areas and deep and thick sludge layers), remarkably improves the pile body strength stability, construction efficiency and quality controllability, and reduces the construction cost. And meanwhile, material waste and personal error risks are reduced.

The invention provides a multi-bucket wheel machine collaborative operation scheduling system and method based on a swarm intelligence algorithm, and relates to the technical field of electronic information. Inputting the equipment information, the stacking information and the environment information into a first model to generate constraint conditions; performing global search based on an artificial bee colony algorithm introducing harmony search, and screening solutions according to constraint conditions in each iteration to obtain a group of feasible solutions; performing local search in each feasible solution neighborhood by adopting mixed integer programming to obtain a globally optimal solution; and generating working parameters of the equipment according to the globally optimal solution, and performing job scheduling. The artificial bee colony algorithm is improved by introducing a harmony search mechanism, the global search capability is enhanced, and premature convergence is avoided; and meanwhile, local optimization is carried out in combination with mixed integer programming, so that the solution accuracy is improved, the algorithm can adapt to yard scheduling requirements of different scales and different constraint conditions, and the overall operation efficiency is improved.

The invention relates to the field of new energy and oil gas fusion development, and discloses a wind and light storage capacity optimal configuration method and system based on rod-pumped well group load, and the method comprises the steps: obtaining annual meteorological data on a well site and energy consumption data of a pumping unit, substituting the data into a fan and photovoltaic panel output model, and carrying out the processing to obtain a first set of output data and energy consumption data; increasing random fluctuation within a set range for the meteorological data and the energy consumption data, and processing the changed meteorological data to obtain a second set of output data and energy consumption data; an artificial bee colony solving algorithm is designed based on the established energy storage charging and discharging state model and the wind and light storage capacity optimization configuration model, two sets of output data and pumping unit energy consumption data are substituted into the artificial bee colony solving algorithm for solving to obtain two configuration schemes, and a scheme with high total investment cost is selected as an optimal scheme; and carrying out secondary capacity optimization configuration solving by adopting a genetic algorithm, and comparing with an optimal scheme solved by an artificial bee colony algorithm to obtain an optimal capacity optimization configuration scheme.

The invention discloses a multi-rotating-speed flexible job shop scheduling method applied to double-resource constraint, and the method comprises the steps: taking the maximum completion time and minimum total energy consumption of a minimum machine as target functions, and constructing a flexible job shop scheduling model considering the rotating speed energy consumption of the machine and the production demands of a fine process; a machine speed gear constraint, a fine process constraint, a process sequence constraint, a completion time constraint, a machine processing constraint and a worker operation constraint are established as constraint conditions of the model; the flexible job shop scheduling problem is solved by adopting an improved artificial bee colony algorithm, bee colony search guided by excellent genes is adopted in bee learning operation in the improved artificial bee colony algorithm, and nectar source optimization is carried out based on the searched excellent genes; the following bee operation adopts a neighborhood structure which considers machine speed change and balances the working time of workers to carry out dynamic neighborhood search so as to optimize a nectar source. The effectiveness of the improved strategy is verified through experiments, and the superiority is verified through comparison of different algorithms on expansion standard examples.

The invention provides an oxidation kettle temperature prediction control method. The method comprises the following steps: S1, establishing a differential equation model; s2, converting the differential equation model into a discretized space prediction model, enabling the space prediction model to be equivalent to a first-order inertia plus lag system, determining state transition matrix parameters through system identification, and generating an oxidation kettle temperature prediction model; s3, obtaining a target function of a model prediction controller according to an error between the predicted temperature and the reference temperature; s4, improving an artificial bee colony algorithm by adopting a variation strategy of a differential evolution algorithm; s5, in the rolling optimization process of model prediction control, the improved artificial bee colony algorithm is adopted to update the optimal control input sequence in a rolling mode until the target function is minimized, and the optimal control input sequence is obtained; and S6, applying the optimal control input sequence to an oxidation kettle control system in real time, and dynamically adjusting the temperature of the oxidation kettle. The selection of the control input sequence is optimized, and the temperature control in the reaction process is more accurate and efficient.

The invention discloses a cascade reservoir scheduling method and system based on an artificial bee colony algorithm, and the method comprises the following steps: the system collects multi-source hydrological data in real time through a hydrological perception and preprocessing module, and introduces a large language model to carry out the semantic judgment and anomaly labeling of an abnormal hydrological time sequence; inputting the processed high-quality data into a reservoir model construction module, establishing a cascade reservoir optimal scheduling system, and setting corresponding boundary conditions and operation constraints in combination with reservoir scheduling regulations; the scheduling optimization module receives model input, adopts a variable structure taking a water level as a core to construct an optimization individual, and completes population initialization, disturbance generation and fitness evaluation based on a potential solution guide mechanism in an improved artificial bee colony algorithm; the system transmits the scheduling sequence optimized and output by the scheduling optimization module into an LLM intelligent auxiliary module; and the intelligent text interpretation generated by the LLM and the scheduling optimization solution enter a result evaluation and visualization module together. According to the scheduling method and system, a high-quality and physically feasible scheduling scheme can be output within reasonable calculation time.

The invention relates to the field of parameter identification, and provides a photovoltaic model parameter identification method and system based on an improved artificial bee colony algorithm, and the method specifically comprises the steps: obtaining a to-be-identified photovoltaic model, and constructing an optimization objective function which is based on a root-mean-square error; initializing an artificial bee colony algorithm and performing terrain complexity evaluation to obtain a terrain type, the terrain complexity evaluation being based on the population fitness difference matrix; and according to the terrain type, adaptive search is carried out to obtain a final identification result, and the adaptive search is based on a smooth terrain processing mechanism and a rugged terrain processing mechanism. According to the method, the defect of a traditional artificial bee colony algorithm facing a complex terrain area is avoided, and the accuracy and efficiency of photovoltaic model parameter identification are improved.

The invention discloses a test case automatic generation method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a to-be-tested program; analyzing the to-be-tested program to generate an initial population; determining a fitness function; performing iterative optimization on the initial population based on the fitness function to obtain an optimal individual; and taking the optimal individual as a target test case for the to-be-tested program. According to the technical scheme, the boundary value use case generated based on the program control flow diagram is fused with the candidate use case to generate the initial population, the coverage rate of critical paths in the population can be effectively increased, the proportion of redundant use cases can be effectively reduced, and under the same test target, the scale of the use case set is reduced, and the test execution time is shortened. The hierarchical fitness function design reduces the proportion of high-time-consumption operation cases and improves the test efficiency. All stages of operation of the genetic algorithm are combined with all stages of operation of the artificial bee colony algorithm, and algorithm efficiency is improved.

The invention provides a robot path planning method and system based on an artificial bee colony and reinforcement learning, and relates to the field of path planning, and the method specifically comprises the following steps: initializing an artificial bee colony algorithm population according to known environment information, a starting point and a target point; and performing iterative optimization on the population based on a search mechanism of an artificial bee colony algorithm. And when a preset optimization process termination condition is met, selecting a batch of high-quality solutions from the final population to form an elite solution set. And encoding the elite solution set into priori knowledge of a reinforcement learning agent to initialize a value function of reinforcement learning, and setting a reward function fusing a path length and a smoothness target. And based on the initialized value function, running a reinforcement learning algorithm to carry out strategy search and optimization so as to carry out fine adjustment on a path strategy. And after the reinforcement learning algorithm converges, outputting an optimal path. According to the technical scheme, the problem that a short and smooth path cannot be quickly generated in a complex environment in the prior art is solved.

The invention discloses a PMSM fuzzy PI controller optimization method based on an improved artificial bee colony algorithm. The method comprises the following steps: constructing a fuzzy PI controller to output a control quantity in real time; and an improved artificial bee colony algorithm is adopted to optimize six-dimensional parameters. In the algorithm, all individuals are initialized as worker bees, the first half iteration period is explored by using a traditional formula, and the second half iteration period is searched by using an improved formula integrated with global optimal information; screening 30% of worker bees by combining fitness sorting with a roulette method, and converting the worker bees into observation bees for local exploration; the forgetting threshold Limit is dynamically adjusted along with the number of iterations and is adjusted along with the change of the population fitness value, when the number of individual forgetting times exceeds the Limit, a reconnaissance bee is converted into a reconnaissance bee, and an improved formula integrated with global optimal information is adopted for searching again. And calculating an individual fitness value through an improved fitness function, transmitting the individual fitness value back to the algorithm to perform advantage and disadvantage comparison of a solution, and outputting a global optimal parameter after iterating to the maximum number of times. By improving the search strategy, the selection mechanism and the fitness function of the algorithm, efficient parameter optimization is realized, the control precision, the response speed and the robustness of the PMSM fuzzy PI controller are remarkably improved, and the application prospect is good.

The invention discloses a green robust independent parallel locomotive scheduling method with uncertain processing time. The method comprises the following steps: acquiring a to-be-scheduled parameter set; constructing an irrelevant parallel machine scheduling model taking worst scene completion time WC and scene average energy consumption MTEC as double targets based on the parameters; a scene-driven double-population discrete artificial bee colony algorithm is adopted for solving, and the method comprises the steps of population initialization, employed bee global search based on ternary championics and two-point crossing, division into two sub-populations according to MTEC, MN local search based on a mean value scene, WN local search based on a worst scene, LN observation bee self-adaptive neighborhood search based on Q-learning and scout bee disturbance. And finally, outputting a robust scheduling solution set with both robustness and low-carbon property according to a Pareto criterion. And a plurality of scheduling schemes considering robustness and energy consumption optimization are provided for decision makers.

The invention provides an underwater vehicle measuring point layout optimization method and system, and belongs to the technical field of experimental measuring point layout optimization. Comprising the following steps: obtaining simulation data of an underwater vehicle structure through finite element simulation, and extracting strain data of a unit capable of arranging a strain gauge area according to actual engineering constraints; centroid coordinates of the units, sensitivity values of strain response values relative to working conditions and strain gradients are calculated by taking centroids of the units in the areas where the strain gauges can be arranged as nodes; mapping the strain response values of all the nodes to a unified interval to obtain a normalized strain response value; obtaining sensitivity coverage and strain gradient coverage, and calculating space coverage of all nodes; integrating the normalized strain response value, sensitivity coverage, strain gradient coverage and space coverage as optimization indexes, and performing optimization by adopting an improved discrete artificial bee colony algorithm; and a final layout scheme is decided by outputting the relationship between the number of the measuring points and the information amount.

The invention discloses a resource scheduling method and device, equipment, a storage medium and a program product, and the method comprises the steps: intercepting Pods created in a Kubernetes cluster, and obtaining a plurality of to-be-scheduled Pods; screening out a plurality of nodes to be deployed from the Kubernetes cluster according to the resource types and the resource requirements of the plurality of Pods to be scheduled and the resource information of the nodes in the Kubernetes cluster; according to a resource scheduling strategy based on an improved artificial bee colony algorithm, determining a target node of each to-be-scheduled Pod from the plurality of to-be-deployed nodes, and deploying corresponding target nodes for the plurality of to-be-scheduled Pods; balanced deployment of resources can be realized, and the resource utilization rate and the load balancing degree of the whole cluster are improved.

The present invention relates to an optimization method for integrated joint scheduling of production and logistics in a flexible job shop (FJSP) having a plurality of different types of automated guided vehicles (AGVs). The invention belongs to the field of assembly workshop production scheduling. Comprising the following steps: 1) according to a special assembly workshop machine and AGV combined scheduling process, Tent chaotic mapping is adopted to initialize a scheduling scheme and encode the scheduling scheme; 2) performing iterative optimization adjustment on the scheduling scheme through an improved multi-target artificial bee colony algorithm; and 3) carrying out production scheduling by using the optimized scheduling scheme. According to the method, the maximum completion time and the total energy consumption are optimized at the same time, the production efficiency is concerned, the requirements of green manufacturing and sustainable development are also considered, and enterprises are helped to achieve cost reduction and efficiency improvement, especially in the production process sensitive to energy consumption.

The invention relates to the technical field of production scheduling, in particular to a hybrid flow shop self-learning scheduling method considering machine deterioration, and aims to solve the problem of hybrid flow shop scheduling considering machine deterioration, a self-learning artificial bee colony algorithm is adopted to optimize the maximum completion time. In the hired bee stage, populations are divided into P1, P2 and P3 groups according to fitness to realize diversified learning, in the bee observation stage, the food source selection probability is improved based on knowledge, multi-neighborhood search is combined, the investigation bee stage is improved, and a nectar source diversity enhancement strategy is added. The SLABC is superior to PPSOGA, DWSA and other algorithms under different workpiece numbers, stage numbers and deterioration rates, the influence of machine deterioration on the construction period can be effectively handled, the scheduling performance is remarkably improved, and the actual production requirements are met.

The invention relates to the technical field of power systems, in particular to a comprehensive energy optimization scheduling system and method considering source-load coordination. An economic dispatching mathematical model is established by taking variables such as fire coal cost of a thermal power generating unit, operation cost of a steam extraction and energy storage device, system operation and maintenance cost, solution loss cost in carbon capture equipment, system wind and light abandoning cost and stepped carbon transaction cost as basic objective functions through an operation decision framework taking a comprehensive energy system as a dispatching basis; basic constraint conditions such as a power balance constraint, a spinning reserve constraint, a wind and light output constraint, a carbon capture device operation constraint and an energy storage constraint are comprehensively considered, and an improved single-target artificial bee colony algorithm is utilized to solve the economic dispatching mathematical model to obtain an optimal solution; therefore, an optimal balance point is found among economy, sustainability and system operation requirements.

The invention provides an inspection robot trajectory planning method suitable for a compact obstacle environment of a power distribution room, and solves the problems that keys are compact and an obstacle is close to a target key in the power distribution room environment. According to the method, a bee colony and a Q table are initialized, a fitness function is constructed, robot motion and environment constraints are introduced, and an exploration or development strategy is dynamically selected according to a population state. A particle swarm algorithm with control parameters is introduced in a scout bee stage, and the global search capability and convergence efficiency are enhanced. The optimal solution is updated through an elitist strategy and neighborhood search, and the Q value is adaptively adjusted by means of feedback, so that the strategy flexibility is improved. According to the method, the global search advantage of the particle swarm and the local development advantage of the artificial bee colony are integrated, the trajectory precision and the operation safety are guaranteed, the efficiency and the real-time performance are also improved, the success rate reaches 98.16%, the time is reduced by 10.11% compared with that of an artificial bee colony algorithm, and the method is suitable for popularization and application. The method is suitable for a patrol robot track planning task with a complex distribution room structure, a compact control panel arrangement and a closer obstacle to a target.

The invention provides a path optimization method for cleaning an unmanned aerial vehicle swarm and application. The method comprises the steps that position initialization is conducted on multiple unmanned aerial vehicles in a task area, a fitness function is set, and a variable Trials is recorded; the stages of employed bees, observation bees and reconnaissance bees are executed in sequence through an artificial bee colony algorithm, and path searching, probability selection, boundary processing and position updating are carried out; a reverse learning strategy, disturbance control frequency and a global factor are introduced into track initialization to improve search performance, and meanwhile, a nectar source regeneration mechanism in a scout bee stage is set. Through dynamic task allocation optimization, global collaborative path planning and a distributed elastic control architecture, the cleaning resource utilization rate, the operation efficiency and the energy consumption balance are improved, the fault-tolerant capability of the system is enhanced, and the cleaning requirement of the complex building facade is met.

The invention relates to the technical field of intelligent inspection and cooperative control, and particularly discloses a robot dog and unmanned aerial vehicle cooperative inspection system and method based on an artificial bee colony algorithm, and the system comprises an unmanned aerial vehicle, a robot dog and a cooperative decision system. The robot dog comprises a navigation module, an environment sensing module, a first communication module and a charging platform; the unmanned aerial vehicle comprises a flight control module, a visual identification module, an image acquisition module, an energy management module and a second communication module; the collaborative decision module is used for operating an artificial bee colony algorithm to realize intelligent collaboration of the robot dog and the unmanned aerial vehicle; according to the invention, through dynamic conversion of roles, active exploration of an unknown area and autonomous precise charging on the mobile platform, all-weather, self-organization and full coverage of the inspection process are realized, and the inspection efficiency and the intelligent level in a complex environment are significantly improved.

The invention provides a micro-seismic positioning method and device based on an artificial bee colony algorithm, equipment and a storage medium. Relates to the technical field of dry micro-seismic data processing. The method comprises the following steps: acquiring micro-seismic data, identifying the micro-seismic data by adopting a long-short time window ratio method to obtain micro-seismic data of a micro-seismic event, and performing noise reduction processing and first arrival pickup to obtain an arrival time sequence of the micro-seismic event; taking the cosine similarity as a target function, taking the coordinates of each sensor as initial parameters, randomly selecting a plurality of initial points, starting inversion based on a set iteration number, calculating a time difference sequence from each initial point to each sensor as an arrival time sequence of an inversion waveform, and calculating the arrival time sequence of the inversion waveform; and if the cosine similarity of the arrival time sequence of the inversion waveform is higher than the value before iteration, replacing the arrival time sequence of the previous inversion waveform with the arrival time sequence of the current inversion waveform, otherwise, continuing to search until the set number of iterations is reached, and outputting the optimal seismic source point. According to the invention, the accuracy of micro-seismic positioning can be obviously improved.

The invention provides an ultrasonic echo signal processing method and system and electronic equipment, and relates to the technical field of ultrasonic signal processing, and the method only carries out parameter optimization on the sampling delay time and attenuation coefficient of an ultrasonic echo signal, and reduces the calculation complexity; a non-linear iteration process is introduced into an artificial bee colony algorithm, and the performance of the artificial bee colony algorithm is improved by using a particle swarm optimization algorithm, so that efficient signal reconstruction is realized, and noise interference in an ultrasonic echo signal is effectively suppressed.

The embodiment of the invention provides a routing method and system under the coverage of a low-orbit satellite, and the method comprises the steps: obtaining a static topological graph of a current low-orbit satellite network; acquiring a source satellite and a target satellite which need to communicate in the current low-orbit satellite network; an improved artificial bee colony algorithm is adopted, and an optimal link from the source satellite to the target satellite is selected from the static topological graph; wherein the improved artificial bee colony algorithm is an algorithm obtained by improving the artificial bee colony algorithm by adopting a multi-target link cost function and a genetic algorithm; and establishing a communication link between the source satellite and the target satellite based on the optimal link. According to the invention, efficient optimization of the communication link can be realized.

The application provides a robot path planning method and system based on artificial bee colony and reinforcement learning, and relates to the field of path planning, and the method specifically comprises the following steps: initializing an artificial bee colony algorithm population according to known environment information, a starting point and a target point; iteratively optimizing the population based on a search mechanism of the artificial bee colony algorithm; when a preset optimization process termination condition is met, selecting a batch of high-quality solutions from the final population to form an elite solution set; encoding the elite solution set as prior knowledge of a reinforcement learning agent, initializing a value function of the reinforcement learning, and setting a reward function that combines path length and smoothness objectives; based on the initialized value function, running a reinforcement learning algorithm for policy search and optimization to fine-tune the path strategy; and outputting an optimal path after the reinforcement learning algorithm converges. The technical scheme of the application overcomes the problem in the prior art that a short and smooth path cannot be quickly generated in a complex environment.

This invention discloses an edge computing federated aggregation optimization method for multi-regional distribution networks, belonging to the field of distribution network collaborative optimization and federated learning technology. This method is based on a three-tiered "cloud-edge-device" architecture, integrating an improved artificial bee colony (ABC) algorithm and an optimal transport (OT) mechanism to construct an AOT aggregation optimization scheme: the edge side collects and preprocesses runtime sequence data, and edge nodes complete local model training and upload parameters; the cloud searches for optimal aggregation weights using the improved ABC algorithm, adapting to differences between node data and models, and uses the OT mechanism to achieve parameter distribution alignment and weighted fusion; efficient aggregation is ensured through dual-trigger collaborative communication scheduling. This invention solves the problems of fixed weights and insufficient parameter alignment in traditional federated aggregation, significantly improving the global model accuracy, stability, and convergence speed in scenarios where data is not independent and identically distributed, reducing communication overhead, and providing reliable support for intelligent collaborative scheduling of multi-regional distribution networks.

The invention discloses a composite material pultrusion frame data acquisition and management system based on reinforcement learning. The system comprises a real-time process parameter acquisition and filtering unit, a digital twinborn simulation unit, a reinforcement learning control unit, a hyper-parameter optimization unit, an abnormity early warning unit and a data management unit. The system obtains a process parameter data stream through a sensor array and nonlinear Kalman filtering, generates a control strategy through a digital twin model and a SoftActor-Critic algorithm, optimizes hyper-parameters through an artificial bee colony algorithm, identifies and early warns process abnormal parameters in real time, and constructs a data feature library and auxiliary decision data. According to the invention, the data acquisition precision of the pultrusion process, the intelligent level of the production process and the product quality stability are improved.

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 invention discloses a wind power-containing electric power system source load cooperative scheduling method considering a high energy load, and the method comprises the steps: analyzing the scheduling demands of a wind power-containing electric power system, building an electric arc furnace load and wind power output uncertainty model based on the schedulable characteristics of the electric arc furnace load, generating a scene through employing a data driving method, and carrying out the calculation of the scene. The method comprises the following steps: analyzing uncertainty superposition influence of electric arc furnace load and wind power output, determining reserve capacity required by an electric power system to cope with uncertainty fluctuation, proposing a smelting type high-energy load adaptive electricity price mechanism considering wind power output change and source load fluctuation, establishing an optimal scheduling model, and solving the optimal scheduling model by adopting an artificial bee colony algorithm; according to the method, powerful theoretical support is provided for the smelting type high-energy load to participate in power grid dispatching and wind power absorption, the accuracy of power grid dispatching is guaranteed, and absorption of new energy and safe operation of a power system are promoted.