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

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.

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 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 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 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 provides a'source-load-storage 'integrated low-carbon optimal scheduling method, which comprises the following steps of: coupling carbon emission of a power generation side with power flow through carbon emission flow modeling, and realizing transmission of carbon emission responsibility from a source side to a load side; carbon responsibility distribution is dynamically corrected in combination with the carbon emission characteristics of the energy storage system in the charging and discharging process; the scheduling strategy is divided into two stages, thermal power, wind power and energy storage optimization is carried out with cost minimization, and then the carbon responsibility is fairly and reasonably allocated and priced by using an extended Shapley value and a bidirectional stepped carbon cost mechanism; and meanwhile, an improved grey wolf optimization algorithm is introduced to improve global and local search capabilities of the model, so that economical efficiency and low-carbon targets are considered.

The invention discloses a vehicle path problem solving method, system and equipment and a medium, and particularly relates to a vehicle path problem solving method based on clustering decomposition and graph matching, which comprises the following steps: S1, receiving a large-scale vehicle path problem instance to be solved, modeling into a weighted undirected graph, and combining a series of constraint conditions and optimization targets; s2, constructing an offline knowledge base with a diversified structure, inputting a group of preset parameters in the step, and outputting a knowledge base stored in the memory; s3, aiming at the large-scale vehicle path problem instance to be solved, executing initialization operation to generate a global initial solution; and S4, by taking the global initial solution as a starting point, executing an iterative local search framework to carry out deep optimization on the solution until a preset termination bar is met. The technical problems that in the prior art, when a large-scale complex logistics network is processed, the calculation time consumption is long, the planning cost is high, and the result is unstable are solved.

The invention relates to the technical field of production scheduling and intelligent manufacturing, and provides an assembly line scheduling optimization method considering generalized priority constraints, which comprises the following steps: constructing an integer linear programming model, performing generalized priority relation enhancement and propagation on an input instance of the integer linear programming model, and improving the capacity of a workstation; calculating a simple lower bound based on task processing time, a lower bound based on the longest path of the priority graph and a lower bound based on a maximum flow algorithm, and taking the maximum value of the three as a final lower bound; defining an action space, defining a state space to represent the change of a current solution on an objective function value and a distribution balance degree relative to a global optimal solution and a local optimal solution, and designing a reward function to adaptively select a neighborhood operation; constructing an initial scheme based on the final lower bound and a Q-learning dynamic selection module, and performing batch movement iteration through neighborhood operation adaptively selected by the Q-learning dynamic selection module; and outputting an assembly line scheduling scheme meeting the generalized priority constraint based on a result of the batch movement iteration local search.

The invention discloses an unmanned aerial vehicle path planning method based on an improved snake optimization algorithm, and the method comprises the steps: introducing a self-adaptive random disturbance factor based on a sine function to improve an exploration mechanism of an exploration stage and a development mechanism of a development stage in a first half iteration stage, dynamically enhancing the randomness of position updating in a search process, and positioning a global optimal solution; in the latter half iteration period, the development mechanism of the development stage is improved by introducing a scaling factor and leader-based adaptive Levy flight strategy, the male snake leader is endowed with flight ability and the global exploration ability is enhanced, and the development mechanism of the development stage is improved by introducing an adaptive position updating strategy combining elite leader and Brownian motion; the female snake leader is endowed with the Brownian motion ability, the convergence speed is effectively accelerated while the precision is ensured, and the local search ability is enhanced. According to the method, the path which is low in cost, high in safety and short in arrival time can be quickly found, and the task execution efficiency of the three-dimensional unmanned aerial vehicle is improved.

The invention discloses a multi-logistics vehicle collaborative scheduling method based on a guided customer selection ant colony system, and belongs to the technical field of computing intelligence. According to the method, a feasible solution scheme is constructed for a multi-logistics-vehicle cooperative scheduling optimization problem by maintaining ant teams with the same quantity as the logistics vehicles, wherein each ant is responsible for constructing a service path of one logistics vehicle. After each ant randomly selects the initial customer of the respective service path, selecting the customer which is finally served for each ant based on a final customer selection method of the minimum service cost; then, the ant colony system fuses the current customer information and the customer selection mechanism of the customer information of the final service, and constructs the service path of the logistics vehicle path by path and customer by customer, so that the constructed path is more directional; and finally, in combination with 2-opt and a point insertion local search strategy, optimizing service paths of all logistics vehicles. According to the method, the optimal multi-logistics-vehicle cooperative scheduling scheme is constructed, and meanwhile, the service cost between the logistics vehicles can be well balanced.

The invention relates to the technical field of big data mining, and particularly discloses a discrete particle swarm optimization-based influence maximization method on a hypergraph, which comprises the following steps of: S1, constructing a hypergraph model and a propagation rule: defining a hypergraph which is a node set and a hyperedge set, and satisfying hyperedges; a threshold model is adopted to describe the propagation process, a seed node set is activated at the beginning, other nodes and all hyperedges are not activated, the non-activated hyperedges are traversed, if the proportion of the activated nodes in the hyperedges is larger than or equal to a threshold value, all the nodes in the hyperedges are activated, and the process is repeated until no new hyperedges are activated; s2, initializing a particle swarm; s3, performing two-layer local influence evaluation; s4, particle speed and position updating; s5, local search optimization; and S6, iteration is terminated. By adopting the technical scheme of the invention, the global search capability, the convergence speed and the evaluation precision can be balanced, and efficient and accurate identification of high-influence nodes in the hypergraph is realized, so that the influence propagation effect and the algorithm expandability are improved.

The invention discloses a flexible job shop integrated scheduling optimization method, device and equipment considering AGV electric quantity constraint, through the algorithm, process processing sorting and machine selection of a population individual solution are completed through two-segment coding, in order to improve the quality of an initial population solution, a population is initialized in a manner of combining heuristic generation and random generation, and the quality of the initial population solution is improved. During decoding, scheduling of the AGV is completed by using a heuristic rule, the problems of power consumption and charging of the AGV are considered, generation of an unreasonable solution caused by pre-designation of the AGV in a coding link is effectively avoided, and the problem that a genetic algorithm is prone to falling into local optimum is solved by designing multiple neighborhood structures for local search.

This invention discloses a signal transmission path optimization method for an integrated RF terminal chip, relating to the field of RF technology. The invention includes: S100 real-time parameter acquisition, calculation of mismatch loss factor, impedance deviation factor, and interference rise factor; S200 weighted fusion of the three factors to obtain a disabling coefficient, constructing a disabling coefficient curve, performing curve feature analysis to generate a reconstruction index, and comparing it with a threshold to determine whether reconstruction is necessary; S300 when reconstruction is required, querying the target control word of the matching network and filter based on a multi-dimensional lookup table, and performing priority correction based on interference and power occupancy; S400 executing control word writing, verifying the effect, and if it does not meet the standard, performing local search and fine-tuning until performance is restored. This invention achieves adaptive optimization of signal transmission paths in multiple frequency bands and scenarios through a closed-loop dynamic reconstruction process of perception, decision-making, execution, and verification, effectively reducing mismatch loss and interference impact, and improving the overall performance of the RF terminal chip.

The application discloses an image size calibration method based on an improved Zhang Zhengyou calibration method and a wood defect area calculation method. When the Zhang Zhengyou calibration method uses an L-M method to solve a maximum likelihood estimation minimum value, a problem of easily falling into a local extreme value is solved by using the advantage of globally searching for an optimal value of a genetic algorithm to optimize the defect that the L-M algorithm easily falls into a local minimum value. In the genetic algorithm, binary coding is selected as an encoding type according to a parameter type. On the basis of considering calculation efficiency, a function f(x) = e st is selected as a use function. A roulette method is selected as a selection operation method. A simple and effective one-point crossover is selected as a crossover operation method. Then, only an approximate solution is obtained near a global optimal solution by using the genetic algorithm. Then, the approximate solution is optimized by using an L-M local search algorithm. The advantages of the global genetic algorithm and the local optimization L-M method are effectively combined.

The invention discloses a wartime art element heuristic optimization algorithm inspired by ancient Chinese wartime. The war art algorithm simulates a stratified command structure of commander decision, samson command and soldier execution to solve the complex optimization problem. The warfare art algorithm is mainly characterized in that layering and space division are carried out, wherein a solution space is divided into a plurality of warfare areas, and an optimal individual is selected as a commander and a samson; according to the dynamic strategic decision, the commander is switched between a global exploration strategy (petal blooming) and a local development strategy (petal contraction) according to the dynamic switching probability. And dynamic distribution of military strength: dynamically adjusting search military strength according to the combat work of each legion group so as to balance global and local search capabilities. The soldiers are divided into commander parents, samson parents and ordinary soldiers, and the commander parents, the samson parents and the ordinary soldiers adopt hierarchical step lengths, so that multi-level and high-precision search execution is realized. Experimental results show that the algorithm is superior to various existing mainstream optimization algorithms in the aspects of convergence precision, stability and robustness.

The invention relates to the technical field of micro-grid dispatching, in particular to a micro-grid collaborative optimization dispatching method based on a layered bee foraging algorithm, which comprises the following steps of: constructing a dispatching model containing multiple targets of economy, environmental protection and stability, and designing a layered optimization algorithm consisting of a global exploration layer, a local development layer and a collaborative decision-making layer. The global exploration layer adopts a self-adaptive step length mechanism and chaotic mapping to initialize a food source, and the search breadth is improved; the local development layer performs neighborhood optimization through a self-adaptive crossover mutation operator to enhance the local search capability; a multi-subject benefit coordination mechanism is introduced into the collaborative decision-making layer, scheduling schemes with subject satisfaction degrees larger than 0.8 are screened from Pareto optimal solutions based on Nash equilibrium, fair and efficient unification is achieved, and the algorithm effectively balances the operation cost, the emission level and the system stability of the micro-grid through dynamic step length adjustment, chaos initialization and multi-target collaborative decision-making. And the scheduling precision and the convergence speed are improved.

The present application belongs to the technical field of workshop production scheduling optimization, in particular to a flexible workshop scheduling method and system based on a hybrid whale optimization algorithm. The method comprises S1, analyzing the flexible workshop scheduling problem and determining the mathematical model of FJSP; S2, continuously processing the discrete workshop scheduling; S3, initializing the position of the whale individual in the whale optimization algorithm through Tent chaotic mapping; S4, calculating the fitness of all whale individuals; S5, updating the whale position; S6, searching the neighborhood structure; S7, judging whether the number of whale position updates reaches the maximum iteration number, if not, returning to step S4, if yes, continuing to step S8; S8, outputting the best whale position; S9, outputting the maximum completion time of the found machine and drawing a Gantt chart. The present application has the characteristics of enhancing the local search ability of the whale optimization algorithm, thereby realizing the reasonable allocation of workshop scheduling and improving the production efficiency.

The present application relates to the technical field of intelligent manufacturing production scheduling and combination optimization, in particular to a distributed flexible job shop scheduling method considering process dependency, comprising: initializing algorithm parameters, alternately using a heuristic method and a random method to generate an initial population; selecting a parent solution from the current population through an adaptive adjustment strategy, sequentially executing a crossover operator, a mutation operator and a local search on the parent solution, updating the population; calculating the current optimal solution of the current population, judging whether the termination time is reached, if yes, terminating evolution, outputting the current optimal solution and the maximum completion time, otherwise, continuing iteration. The present application solves the problems of existing solving methods, such as significant time consumption increase and search into local optimum when the scale of the distributed flexible job shop scheduling problem considering process dependency is expanded, and achieves the positive effects of reducing the maximum completion time and improving the search efficiency and solution quality of large-scale instances.

The invention provides a design optimization method suitable for a variable geometry turbine with more than 100 variables, which comprises the following steps: acquiring control parameters of a three-dimensional blade model of a variable geometry turbine part, and establishing a design space; the method comprises the following steps: selecting a set number of uniformly distributed design samples in a design space as an initial population, and sequentially performing differential operation, interlace operation guided by an RBF proxy model and selection operation based on a parent target evaluation value on the samples in the population to generate a plurality of child samples; a local search strategy based on an RBF proxy model is used for an optimal sample in the population, and a child sample is generated; combining all generated filial generation samples, performing performance evaluation, and updating the current population; the number of iterations is set for each optimization, and knowledge migration from a low-fidelity population to a high-fidelity population is realized by adopting a knowledge migration strategy based on a probability distribution model; and performing updating iteration on the high-fidelity population until the number of high-fidelity sample evaluation times reaches a set upper limit.

The application discloses a prostate cancer patient pelvic floor muscle recovery evaluation system based on artificial intelligence, which comprises an original data collection module, an original data optimization module, a time sequence double-end recovery evaluation model establishment module, a recovery evaluation model performance optimization module and a whole-cycle intelligent recovery evaluation module. The application relates to the technical field of medical data processing, in particular to a prostate cancer patient pelvic floor muscle recovery evaluation system based on artificial intelligence. The scheme innovatively proposes a time sequence double-end recovery evaluation model combining an in-hospital baseline end and a home continuous time sequence end, improves the real-time performance of patient pelvic floor muscle recovery evaluation, extracts short-term time sequence recovery features by using a bidirectional long short-term memory network, extracts long-term time sequence recovery features by combining an improved convolution residual network with a parallel activation function, significantly improves the accuracy of the recovery evaluation results, and introduces an improved optimization algorithm combined with a dynamic local search strategy, thereby significantly improving the stability and accuracy of the output results of the model.

The application discloses a path optimization method and system without a special communication channel based on an intelligent cooperative path optimization algorithm, and relates to the technical field of communication networks.In the path optimization process, factors such as communication reliability index, relay node number and load balance degree are comprehensively considered, so that the selected path not only meets the low delay requirement, but also maintains high reliability under different network states, avoids the path instability problem caused by network fluctuations, and ensures the stability and robustness of communication.Through twice fusion of the intelligent path optimization algorithm and the genetic algorithm, the respective advantages of the two algorithms are fully brought into play.The global search capability of the ant colony algorithm and the local search capability of the genetic algorithm are complementary, so that in the path optimization process, the local optimal solution can be effectively avoided, the high efficiency of calculation can be ensured, and the overall path optimization performance is enhanced.

The application discloses a long-term multi-agent task allocation method based on a multi-objective multi-level cultural gene algorithm, which is used in a task continuous random arrival scene such as intelligent warehousing. In view of the problems that the existing static method cannot adapt to long-term tasks, the online method is frequently cold started, the historical knowledge is poorly reused, the convergence is slow, and the task period and the agent load balancing are difficult to balance, a double-objective model of minimizing cumulative task period and minimizing load deviation is established; a multi-objective multi-level cultural gene algorithm is designed, multi-strategy initialization, hierarchical population and genetic operation are adopted, adaptive multi-neighborhood and confusion degree pop-up search local search are combined, and the population and historical archives are updated by cooperating with the non-dominated solution sorting, congestion degree calculation and elite reservation strategy. Through the organic combination of the above strategies, the algorithm can realize the collaborative optimization of task response and load balancing without interrupting the system operation.

The application belongs to the technical field of intelligent manufacturing and production scheduling, and particularly relates to a flexible workshop intelligent scheduling method and system based on Q-Learning enhanced layered sparrow search, which adopts GLR strategy and Tent chaotic mapping to respectively generate two sequences of MS and OS to obtain a high-quality initial scheduling scheme population, and specifically improves the diversity of the population, reduces the risk of the algorithm falling into local optimization, and enhances the overall optimization performance and convergence speed by performing layered updating on the population; the key process concept composed of non-interval processes is introduced, three domain structures are designed based on the key process concept, and VNS is combined to perform local search on the existing scheduling scheme, so that the local search capability of the algorithm is further improved. The method is suitable for workshop scheduling instances of different scales, and compared with existing algorithms, a lower maximum completion time can be achieved on benchmark examples and actual cases.

The invention discloses a hydroelectric generating set overhauling and disassembling method based on an improved seat head whale migration algorithm, and aims to solve the problems of long period, high cost, poor safety and the like caused by complex structure, narrow space and dependence on artificial experience in large-scale hydroelectric generating set overhauling. The method comprises the following steps: constructing a three-dimensional visual model containing equipment and tools by using SolidWorks according to a ratio of 1: 1; constructing a geometric constraint matrix and a working area conflict matrix based on an octree to form a comprehensive priority constraint model; defining a disassembly time sequence and an operator allocation decision variable, and establishing a mathematical programming model for minimizing the total disassembly time; designing a hybrid coding mode, and proposing an improved seat head whale migration algorithm containing a self-adaptive strategy and a local search mechanism; and automatically solving an optimal disassembling sequence and a personnel allocation scheme. Verification shows that the solving quality and stability of the method are remarkably superior to those of a genetic algorithm and a particle swarm algorithm, the maintenance efficiency and safety are greatly improved, the operation and maintenance cost is reduced, and technical support is provided for stable operation of the hydroelectric generating set.

The invention discloses an 8-bit S box construction method based on greedy random adaptive search, which comprises the following steps of: firstly, introducing a nonlinear term power function, a trigonometric function and a logarithmic function to construct a two-dimensional hyper-chaotic system TDHM on the basis of a benchmark test function Himelblau function; secondly, constructing a bijection initial S box based on a random number sequence generated by a two-dimensional hyper-chaotic system TDHM; and finally, respectively carrying out linearity and differential uniformity optimization on the S boxes of which the cryptographic properties do not meet the requirements, setting corresponding cost function values as optimization constraint conditions, selecting an optimized exchange strategy according to corresponding greedy rules, carrying out local search on the S boxes to optimize the linearity and differential uniformity of the S boxes, and carrying out optimization on the S boxes. And the password S box meeting the target condition is constructed. According to the method, the success rate of searching the S box meeting the target requirement is improved, and the average iteration frequency of the algorithm is effectively reduced.