326 results about "Local search (optimization)" patented technology

The invention relates to the technical field of medicine purchasing, and provides a hospital easy-to-expire medicine purchasing decision optimization method, which comprises the following steps of: firstly, extracting medicine inventory, consumption records and purchasing management parameters in a hospital information system, and constructing a complete inventory state data set; then, calling a demand prediction model constructed based on a time sequence and external factors, and outputting drug prediction demand values in a plurality of periods in the future; thirdly, constructing a multi-cycle purchase optimization model according to the current inventory and the prediction demand; then, dividing a planning period into rolling windows, and gradually generating an ordering scheme meeting conditions by using a heuristic greedy and local search algorithm; then, the scheme is pushed to a purchase decision-making interface, and a formal order is submitted and generated after the scheme is confirmed by a manager; after a supplier delivers goods and stores the goods, the system updates inventory information and records scrapping and shortage data; and finally, adjusting the model through a feedback mechanism so as to improve the accuracy of subsequent prediction and decision.

The invention discloses a sprint learning path optimization method based on an improved whale optimization algorithm. The problem that existing sprint path planning is unreliable is solved. The method comprises the following steps: constructing a total learning cost objective function; using a random key coding algorithm based on chaotic mapping to represent the learning paths in a sequence mode, and using serial scheduling to obtain a plurality of candidate sprint learning paths; introducing an adaptive weight mechanism, an examination driving offset item and a Gaussian disturbance item, and adopting an improved white whale optimization algorithm to carry out iterative optimization on the candidate sprint learning paths to obtain a plurality of iteratively optimized sprint learning paths; in any iterative optimization, if the current candidate sprint learning path violates a first repair relation or / and a time window or / and a daily learning duration budget in the learning path, performing feasibility repair on the current candidate sprint learning path; performing neighborhood local search on the iteratively optimized sprint learning path based on a forgetting risk index; and outputting an optimal sprint learning path.

The invention discloses a shipborne radar oil spill detection method and system based on an improved whale optimization algorithm, and relates to the technical field of image processing and mode recognition. The method is characterized by comprising the following steps: preprocessing an acquired shipborne radar image; extracting saliency boundary ratio features of the preprocessed image; extracting an ROI image based on the saliency boundary ratio feature; obtaining an optimal segmentation threshold value by using an improved whale optimization algorithm, and segmenting the ROI image by using the optimal segmentation threshold value to obtain an optimal segmentation image; and performing post-processing on the optimal segmentation image to obtain a final oil film image. According to the method, the difference between the oil spill area and the background is highlighted by extracting the significant features; by improving the whale optimization algorithm, the local search capability of the algorithm is enhanced, and premature convergence is avoided. The method achieves the efficient and accurate recognition of the oil film region, can reduce the false detection rate, and is suitable for the detection scenes of different sea conditions and oil spill types.

The invention discloses a motor equipment intelligent supervision method and system based on big data. The method comprises the following steps: collecting initial multi-dimensional data; extracting semantic features and spatial-temporal features in the initial multi-dimensional data by using a pre-trained BERT language model and an STGN spatial-temporal convolutional network, performing weighted fusion on the physical parameters, the semantic features and the spatial-temporal features through an attention mechanism, constructing a dynamic graph network based on multi-dimensional information feature representation, and performing time sequence modeling in combination with a TCN time convolutional network; and carrying out multi-objective optimization by using an NSGA-III algorithm, carrying out local search by using a quantum annealing algorithm, outputting an equipment operation state report, and carrying out motor equipment fault early warning according to the equipment operation state report. And the fault detection accuracy and the early warning response time are improved.

The invention provides a battery electrochemical parameter identification method, system and device and a program product, and the method comprises the steps: carrying out the discharge test of a plurality of discharge rates on a battery, and obtaining the experimental data of the discharge test; constructing a battery electrochemical model based on experimental data; performing cross-working-condition sensitivity analysis on the model input parameters of the battery electrochemical model by adopting a global sensitivity analysis method to obtain global sensitivity parameters; constructing a target function based on key region constraint based on experimental data; and alternately adopting a constraint Bayesian optimization method based on a trust domain and a granular self-adaptive local search method to explore the optimized target function, and iteratively optimizing the target function to obtain an optimal solution of the global sensitivity parameter. According to the method, a set of parameter identification system with high precision, cross-working-condition robustness and calculation efficiency is constructed, and cross-working-condition high-precision identification of the electrochemical parameters of the high-capacity lithium ion battery is realized.

The invention relates to the technical field of wireless communication, and discloses an unloading method in an ultra-dense millimeter wave MEC network. Aiming at the problems of insufficient terminal capability, high energy consumption of an ultra-dense base station, millimeter wave coverage limitation, communication security risk and the like under the condition of sharp increase of a calculation-intensive task, the method comprises the following steps: firstly, obtaining network basic information, constructing a network architecture containing communication, calculation unloading and a security model, and establishing a multi-constraint optimization problem; based on the optimization problem, initializing a multi-strategy black-wing plinuary optimization algorithm population through logic mapping and elite selection; updating individual positions through global and local search in algorithm iteration, and screening historical optimal individuals; and finally, configuring and unloading resources according to the configuration. According to the method, the NOMA technology, a multi-step unloading framework and a hybrid communication mode are combined, the optimization efficiency is improved through a multi-strategy black-wing optimization algorithm, the local energy consumption is reduced, the communication rate is improved, the safety is guaranteed, the optimal scheme meeting time delay and safety constraints is rapidly converged, and the user experience is improved.

The invention relates to the technical field of flexible job shop scheduling in intelligent manufacturing and production scheduling, in particular to a flexible job shop scheduling optimization method for solving and adjusting resource constraints. Comprising the steps of initializing parameters and randomly generating an initial population; sequentially using a crossover operator and a mutation operator to evolve the current population; executing a hybrid decoding strategy on the current population; sorting the individuals in the current population from small to large according to the maximum completion time to form an elite population, and updating the elite population through question specific local search; and judging whether an evolution condition is met or not, if so, executing CP-based mathematical evolution, and outputting a final solution when the running time reaches the total running time. The method has the positive effects of reducing the resource waiting time, improving the machine utilization rate and improving the resource utilization efficiency and the scheduling performance of the whole workshop production.

The invention discloses a shale gas well fracturing parameter optimization design method and system based on a depth Q network, and relates to the technical field of shale gas development, and the method comprises the following steps: S1, collecting geological parameters, fracturing construction parameters and productivity data of a shale gas well in advance; s2, constructing an agent model and performing environment simulation; and S3, constructing a DQN model. According to the method, a LightGBM algorithm is adopted to construct a data-driven proxy model to simulate a real fracturing environment, an optimization model is constructed based on a deep Q network (DQN), state, action, reward and epsilon-greedy strategies are defined, and technologies such as a variable step size search mechanism, experience playback and target network soft update are combined, so that the real fracturing environment is simulated. The problems that a traditional optimization method is weak in local search capability and low in convergence speed are effectively solved, multivariable synchronous optimization of the unit perforation length proppant dosage and the fracturing fluid dosage is achieved, and a global optimal parameter combination can be rapidly explored.

The invention relates to the technical field of wireless communication, in particular to a reachable rate optimization method based on an intelligent reflecting surface, which comprises the following steps of: firstly, constructing a channel model in an application scene, namely designing a system reachable rate optimization model on the basis of establishing a base station-user, base station-IRS and IRS-user three-link model; then, a phase shift matrix of the IRS and an emission covariance matrix of a base station serve as joint optimization variables, a population is divided into an elite subpopulation, a suboptimal population and a common population through a subpopulation grouping strategy of an improved particle swarm algorithm, each particle represents the composed variable, a dynamic cooperation strategy is proposed to calculate the speed in each subpopulation, and the speed in each subpopulation is calculated; and updating individual optimization and group optimization, and continuously carrying out iteration to search a reachable rate optimal solution of the user. According to the method, local search can be finely carried out while large-range global search can be carried out, the calculation complexity is remarkably reduced, and the IRS transmission effect is improved.

The invention relates to the field of control, in particular to a reaction kettle operation control method and system for resin production, real-time operation parameters of a reaction kettle are obtained, a fuzzy neural network model is iteratively trained by adopting a hierarchical collaborative hybrid optimization strategy, a preceding member membership function of the fuzzy neural network model is composed of a Gaussian mixture model, and the preceding member membership function of the fuzzy neural network model is obtained. According to the optimization strategy, an improved quantum particle swarm optimization algorithm is used for carrying out global search to determine Gaussian mixture model parameters, a recursive least square algorithm is used for carrying out local search to determine consequent coefficients after each time of iteration, and in the training process, the parameters of the Gaussian mixture model are subjected to global search to determine the parameters of the Gaussian mixture model. And calculating an importance index according to the average activation degree of the fuzzy rule and the contribution of the fuzzy rule to the prediction error, removing the rule of which the importance is continuously lower than a preset threshold value, and after training is completed, generating and executing a control instruction for controlling the heating system power and the material feeding rate of the reaction kettle at the next moment according to the real-time parameters.

The invention discloses a PCB double-sided four-flying-probe test path optimization method, and belongs to the technical field of path optimization, and the method comprises the following steps: importing double-sided PCB data containing a test network and test points; obtaining an initial matching scheme of a network internal test point corresponding to each test network through the matching strategy, and optimizing each initial matching scheme to obtain a network internal test point path; constructing an initial test sequence between test networks through a recent insertion heuristic algorithm, and then introducing a local search algorithm into an adaptive large-scale neighborhood search algorithm to carry out iterative optimization on the initial test sequence to obtain a test sequence between the networks; and combining the test point paths in the networks and the test sequence between the networks, and outputting a double-sided four-flying-probe test path. According to the PCB double-sided four-flying-probe test path optimization method, the problem that the overall moving cost is difficult to further reduce in an existing method is solved.

The invention relates to the technical field of intelligent manufacturing, in particular to an efficient and energy-saving flexible job shop scheduling method considering adjustment time. Comprising the steps of 1, initializing an initial population of an MTCP-SPEA algorithm; 2, executing a spatial awareness environment selection strategy; step 3, judging whether the current running time of the MTCP-SPEA algorithm reaches half of the total running time, if so, executing step 6, and otherwise, executing step 4; 4, a crossover operator and a mutation operator are adopted to evolve the current population, and current non-dominated individuals are selected to form a Pareto solution set; 5, executing local search operation and returning to the step 2; and step 6, executing a multi-thread-based constrained programming auxiliary optimization method to generate an improved elite set, screening out all non-dominated individuals from the improved elite set to form an optimal solution set, and outputting the optimal solution set. According to the method, the problem of efficient and energy-saving flexible job shop scheduling optimization considering time adjustment is effectively solved.

Certain aspects of the disclosure provide unified in-context prompt optimization for large language models that achieves joint optimization of prompt instruction and examples. A multi-phase approach is provided that includes multiple mutation operations. Further, the approach alternates between optimization strategies for exploration for global search and exploitation for local search. Global initialization creates a diverse set of candidate prompts based on the availability of data and utilizing Lamarckian or semantic mutation. Local feedback mutation, global evolution mutation, and local semantic mutation can subsequently be employed iteratively to generate a revised set of candidate prompts. A prompt from the revised set of candidate prompts can be selected based on an evaluation of the candidate prompts. Subsequently, the selected prompt can be output for a machine-learning task.

The invention provides an image segmentation method based on an improved parrot optimization algorithm and cross entropy multiple thresholds, and the method comprises the following steps: S1, converting a color image into a gray image, and carrying out the noise reduction processing; obtaining a corresponding image gray level histogram according to the gray level image; s2, constructing a target function of cross entropy multi-threshold segmentation based on the image gray histogram; independent variables of the objective function are a plurality of threshold values; s3, optimizing each threshold value by adopting an improved parrot optimization algorithm (IPO), and determining an optimal threshold value combination; wherein in the improved parrot optimization algorithm, the positions of individuals in the foraging behavior stage are updated by adopting a Leid flight strategy or particle swarm optimization (PSO); disturbing the current globally optimal solution by adopting a simulated annealing mechanism; and S4, segmenting the color image based on the optimal threshold combination. According to the method, the local search capability and the convergence speed are improved, so that the precision and robustness of image segmentation are improved.

The invention discloses an unmanned aerial vehicle area coverage flight path planning method and system based on graph segmentation, and relates to the technical field of unmanned aerial vehicle path planning. According to the method, a coverage point pool with high quality, comprehensive coverage and robustness is constructed for subsequent greedy selection through a strategy of adaptively generating candidate points; then, a greedy selection mechanism based on effective scores is adopted, the current optimal coverage point is accurately selected in each round of iteration, the new coverage area can be increased to the maximum extent, the overlapping area can be effectively controlled, and finally the whole target area is completely covered with the minimum number of circles. In the aspect of a path planning algorithm, a hybrid initialization mode of a greedy algorithm and a random generation strategy is innovatively combined, and the quality of an initial solution and population diversity are ingeniously balanced; meanwhile, in each generation of evolution of the genetic algorithm, a 2-opt local search strategy is embedded into a new non-elite individual, so that the convergence speed of the algorithm is increased, and the path optimization efficiency is remarkably improved. In conclusion, more efficient and more reliable planning of the unmanned aerial vehicle area coverage flight path can be realized.

The invention discloses a wireless sensor network (WSN) target coverage method and device based on a chaos adaptive bat algorithm, and belongs to the technical field of wireless sensor network coverage. Aiming at the problems of limited communication range, poor adaptive capability and the like of the existing WSN, the method comprises the following steps of: initializing parameters, constructing a monitoring relation matrix to determine a fitness value, designing an adaptive inertia weight strategy, updating various parameters of a bat individual, and carrying out operations such as local search, adaptive adjustment of loudness and pulse emissivity and the like. And judging a termination condition to obtain an optimal solution in combination with an elitist strategy and chaos updating. The device comprises a network model construction module and the like and is used for executing the method. According to the method, the adaptive bat algorithm is adopted to establish the target coverage model, the elite strategy and chaos updating are combined, the algorithm is prevented from falling into local optimum, the stability is improved, the sensor node coverage area is effectively increased, the convergence speed is higher, the optimization capacity is stable, and the method is suitable for a WSN target coverage scene.

The invention provides a hybrid level enhanced random algorithm based on double-loop optimization and a cross-platform implementation method thereof, and belongs to the technical field of modeling and parameter, and the method comprises the following steps: S1, initialization; s2, setting algorithm parameters; s3, executing internal circulation, and executing local search under a fixed threshold value; s4, judging constraint conditions; when there is no constraint condition, generating a candidate solution; s5, calculating a target function value; s6, judging updating of the matrix according to the target function value; s7, returning to an outer loop, and adjusting a threshold value Th according to the frequency for receiving the new design and the number of iterations; s8, after M iterations are completed, the acceptance rate and the improvement rate are calculated; s9, updating the threshold value Th; and S10, judging a condition for stopping iteration. According to the method, the problems that a general construction method is lacked, local optimum is easy to sink and cross-platform deployment is difficult to realize in a multi-factor mixing level test design are solved.

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 discloses an automatic loading and unloading scene multi-camera visual guidance self-adaptive stacking posture planning method, which relates to the technical field of robot automation, and sequentially comprises the following steps: generating an initial posture, including analyzing posture parameters and generating a preliminary placement posture; visual verification, including multi-camera data acquisition, target area idle state verification and pose rationality evaluation; accurate positioning, including virtual box construction, space matching optimization and final coordinate generation; and target verification and re-planning: based on a real-time feedback closed-loop mechanism, re-verifying whether the target area is in an idle state, and if not, re-planning the attitude. According to the method, the planning time is shortened from the second level of a traditional method to the millisecond level by setting a local search and optimization algorithm; through point cloud completion and space matching optimization, the pose error is controlled within + / -2mm, and environment shielding and stacking changes can be coped with through visual verification; the method can support a complex stacking scene and is wide in applicability.

The invention discloses an intelligent main plan scheduling system oriented to the chemical industry, and the system comprises a data center layer which is used for aggregating order, equipment, materials, inventory and process constraint data; the MPS optimizer operates the tabu search module, the step-counting hill-climbing module and the flood algorithm module in sequence or in parallel in the same optimization period, and automatically switches algorithms under the following conditions: when the continuous iteration improvement rate is lower than a set threshold value, the tabu search is switched to step-counting hill-climbing, and the step-counting hill-climbing is switched to step-counting hill-climbing; when the local search step number is not improved, starting the flood algorithm to re-initialize the candidate solution; and the process scheduler is used for splitting the coarse-grained plan output by the MPS optimizer into minute-level process schedules, and transmitting the execution deviation back to the data center layer in real time through a bidirectional feedback loop so as to trigger incremental rearrangement. According to the intelligent main plan scheduling system for the chemical industry, through a hybrid algorithm dynamic switching mode, the thousand-level order rearrangement time is greatly shortened, and the plan response speed is remarkably improved.

The invention provides a day-ahead electricity market-oriented multi-virtual power plant transaction decision-making method and device, and relates to the technical field of virtual power plant optimization operation. The method comprises the following steps: establishing a power distribution network operator-virtual power plant master-slave game double-layer structure according to a power distribution network operator and a multi-virtual power plant system; constructing an objective function and constraint conditions of the power distribution network operator pricing game model; constructing an objective function and constraint conditions of the robust transaction model of the deterministic virtual power plant; the uncertainty of wind power, photovoltaic and controllable loads is considered, the deterministic virtual power plant robust transaction model is converted into an uncertainty virtual power plant robust transaction model, and then a distributed robust scheduling model is obtained; solving by adopting a dynamic Kriging meta-model and introducing a local search mechanism to obtain a transaction decision; wherein the column and constraint generation algorithm is adopted to solve the distributed robust scheduling model. The method can help the virtual power plant aggregator to significantly improve the earnings in the day-ahead transaction.

The invention relates to the technical field of intelligent production and manufacturing, in particular to an energy-saving fuzzy cascade scheduling method and system for regional gathering cooperative production, and aims to solve the composite problems of supply chain cascade scheduling heterogeneous factory resource allocation, multi-stage time accumulation effect, uncertainty interference and the like in regional cooperative transformation in the manufacturing industry. According to the method, a double-layer collaborative optimization framework is assisted through integrated learning, the uncertainty of quintuple interval fuzzy quantization processing, transportation and assembly time is adopted, an initial Q value matrix is generated through a pre-training layer, self-adaptive operator selection is achieved in combination with a dynamic decision-making layer, and local search, damage recombination and genetic operation are executed by multiple sub-groups. According to the method, the energy-saving second-class fuzzy distributed flow shop and multi-flexible job shop cascade scheduling problem model is effectively defined, three-segment coding and full-process energy consumption calculation are supported, and the overall scheduling efficiency and the energy efficiency balance capability of the regional aggregation industry are remarkably improved.

The invention belongs to the technical field of ship scheduling optimization, and particularly relates to a container branch transport ship scheduling optimization method and system. The method comprises the steps of obtaining the number of ports and the number of ships for container transportation, setting algorithm parameters, initializing a population by utilizing Logistic chaotic mapping, decoding each individual, recording a historical optimal solution and a global optimal solution, entering a main loop to iteratively update individual positions, and starting an eagle positioning fishing mechanism and adaptive variation. A dynamic reverse learning mechanism calculates new fitness and an individual optimal position, and when the number of continuous stagnation times of the individual exceeds a threshold value, an escape mechanism is started, a golden section method is adopted to adjust departure time, 2-opt local search is performed on each ship route, a new position fitness value is evaluated, and an individual optimal solution is updated; if the maximum number of iterations is reached or a convergence threshold is met, outputting a global optimal solution; otherwise, returning to the main loop to continue iteration. The method can improve the performance of the algorithm in solving the scheduling optimization problem of the container branch transport ship.

The invention discloses a method for stopping a spacecraft target access optimization task based on local search and exploration enhancement, and belongs to the technical field of spacecraft control. In order to solve the problem of optimization termination control with robustness and self-adaptability, an optimization termination criterion is designed to terminate iterative optimization of Bayesian optimization when the maximum prediction standard deviation of a Gaussian process regression agent model in a current local area is lower than a preset threshold value; the design optimization termination criterion is embedded into the BO; measuring the cognitive uncertainty of the GPR agent model to the target function at a certain point by using the prediction standard deviation of the GPR agent model; and setting a judgment condition for judging whether to enhance the exploration enhancement capability of the acquisition function and whether to trigger local evaluation. According to the invention, optimization termination control with robustness and self-adaptability is realized.

The invention discloses a low-carbon cold-chain logistics path optimization method based on an improved marine predator algorithm. The low-carbon cold-chain logistics path optimization method comprises the steps of establishing a multi-target distribution path optimization model according to cold-chain logistics distribution task requirements and urban traffic network conditions; minimization of total distribution cost and maximization of customer satisfaction are taken as a comprehensive optimization objective function, and constraint conditions are determined; applying a Tent chaotic mapping mode to randomly generate an initialized population, wherein the population represents a set composed of a plurality of path planning schemes; performing iterative optimization on the initial population, updating each path planning scheme in the population according to global search and local search mechanisms of a marine predator algorithm in each iteration, evaluating the fitness of each path planning scheme according to a comprehensive optimization objective function, and updating the population according to an evaluation result; and when a preset termination condition is reached, outputting the path planning scheme with the highest fitness in the population as an optimization result.

The invention discloses an unmanned aerial vehicle path planning method based on an improved snake optimization algorithm, and belongs to the technical field of path planning. Comprising the steps of setting parameters and population quantity of an improved snake optimization algorithm, determining environment temperature and food quantity, and selecting to enter a global search mode or a local search mode according to a relationship between the food quantity and the environment temperature; when the local search mode is entered and the environment temperature is higher than a temperature threshold value, executing an exploration development balance strategy to obtain a first position update quantity, and updating a population position based on the first position update quantity; when the local search mode is entered and the environment temperature is lower than a temperature threshold value, executing an adaptive variation mechanism to obtain a second position update quantity, and updating the population position based on the second position update quantity; based on the updated population position, a plurality of paths of the target unmanned aerial vehicle are obtained, and the shortest path is selected as the current target path; and repeating the steps until a preset number of iterations is reached, and outputting the target path. The method is accurate in unmanned aerial vehicle path planning.

The invention belongs to the cross technical field of computational intelligence and machine learning, and discloses a particle swarm optimization method for a high-dimensional expensive problem fusing a DBN and an agent model, and the method comprises the steps: extracting the features of a high-dimensional optimization problem through an IDBN dynamic dimension reduction module, and carrying out the dynamic dimension reduction of the features; filling a multi-stage collaborative screening mechanism of the samples, and selecting a plurality of representative samples; according to the local search strategy of the elite solution, local development is carried out on the elite solution so as to improve the convergence speed and the precision of the algorithm. According to the particle swarm optimization method for the high-dimensional expensive problem fusing the DBN and the proxy model, through the IDBN dimension reduction module, the search efficiency of an algorithm in a high-dimensional space is remarkably improved, and the construction cost of the proxy model is reduced; through a multi-stage collaborative screening mechanism of filling samples, the precision of the agent model in a desired area and a potential area is enhanced; a local search strategy of an elite solution is introduced, so that the local development capability of the algorithm is improved, and the precision of an optimal solution is improved.

The invention discloses a distributed heterogeneous flexible flow shop batch processing scheduling method and system, relates to the technical field of distributed production scheduling in the manufacturing industry, and aims to solve the problems that an existing scheduling method is not comprehensive in constraint consideration, poor in energy consumption optimization and low in algorithm efficiency. According to the method, a mixed integer linear programming model containing multiple constraints such as release time and sequence-related preparation time is constructed, a learning-assisted dual-objective co-evolution framework is established, and the maximum completion time and the total energy consumption are synchronously optimized by combining mixed initialization, global-local search collaboration, decision reinforcement learning operator selection and a collaborative energy-saving strategy. The release time, the sequence-related preparation time, the inter-stage transportation time and the batch processing scheduling are simultaneously considered in the distributed heterogeneous flexible flow shop scheduling for the first time, the established mixed integer linear programming model better fits the actual production scene, and the method fits the actual production scene, is good in energy consumption optimization effect and can be adapted to the non-ferrous metal metallurgy aluminum production process.

The invention relates to the technical field of unmanned aerial vehicle path planning in a complex environment, and provides a complex environment unmanned aerial vehicle path planning method based on Chebyshev chaotic mapping sparrow optimization. Under the constraint of flight distance and height, a total cost function is constructed, Chebyshev chaotic mapping is utilized to initialize a sparrow population, population diversity is improved, global search and local development capabilities in an exploration rate balance position updating process are introduced, a producer and follower step length adjustment strategy based on the exploration rate is adopted, and a sparrow swarm optimization algorithm is established. According to the method, the self-adaptability of the step length in the position updating process of the producer and the follower is improved, the minimization of the total cost function is realized in the process from the early global search of the minimization of the total cost function to the later focusing local search, the optimal solution of the total cost function is sought, and the flight path which is short in distance, low in energy consumption and capable of avoiding the threat source is planned for the unmanned aerial vehicle.

The invention discloses a heterogeneous cluster parallel multi-stage fuzzy job scheduling method, which comprises the following steps of: receiving a processing request which is submitted by a user and contains multi-stage jobs, and modeling task execution time, data transmission time and request deadline into triangular fuzzy numbers, constructing a scheduling model taking the minimization of the total lease cost and the minimization of the request tardiness as targets; carrying out global exploration by adopting an improved second-generation non-dominated sorting genetic algorithm to generate parent and offspring populations; based on population similarity threshold judgment, dynamically triggering local search guided by a multi-agent near-end strategy optimization algorithm, and adaptively selecting a local search operator for each individual to generate an adjacent population; combining various populations, performing non-dominated sorting and crowding distance calculation, and screening out a new generation of populations; and iterating the process until convergence, and outputting a Pareto optimal solution set. According to the method, the problem of multi-target scheduling with fuzzy time variables in a heterogeneous environment is effectively solved, and the quality of a scheduling scheme and the algorithm search efficiency are improved.