88 results about "Population diversity" patented technology

The invention discloses a multi-mechanical-arm accurate assembly method for double-population evolution reinforcement learning, and the method comprises the steps: constructing a partially observable Markov game model, designing a mixed reward function, and building a double-population mechanism: an Exploit main population is responsible for optimizing the performance, and an Explore auxiliary population maintains the diversity; extracting unified feature representation through a shared observation embedded network, optimizing a main population by adopting conjugate gradient differential evolution, and optimizing an auxiliary population by adopting novelty driven evolution; promoting information interaction between the double populations through an elite exchange mechanism, and utilizing parameter migration fusion to reinforce learning and evolutionary optimization results; and finally, an optimal strategy set for balancing accumulated rewards and novelty is selected through Pareto optimization, and the optimal strategy set is deployed to a multi-mechanical-arm system to execute a cooperative task after convergence conditions are met. According to the method, the population diversity is effectively maintained and the cooperation robustness is improved while the high performance of the strategy is ensured; and a powerful technical support is provided for automatic assembly in a complex industrial scene.

The invention discloses an unmanned aerial vehicle cluster task allocation method based on a large language model optimization genetic algorithm, and belongs to the field of computers. The method comprises the following steps: setting a specific chromosome coding mode; generating a multi-constraint initial population; calculating fitness to quantify the advantages and disadvantages of individual genes of the population; when the optimal individual meets the requirement or the maximum iteration round is reached, ending; retaining the optimal individual as a filial generation; generating a batch of new filial generation individuals by the large language model, and fusing the new filial generation individuals with the current filial generation population; calling an optimized large language model to analyze individual chromosome semantics, and outputting an evolutionary potential score; obtaining an individual comprehensive selection probability by integrating the fitness and the evolution potential score, and executing a selection operation; and selecting individuals based on the individual comprehensive selection probability to carry out crossover and mutation operation to generate offspring. The large language model is embedded into the core link of the genetic algorithm, and the algorithm efficiency is improved by improving the population diversity of the genetic algorithm in the unmanned aerial vehicle cluster task allocation scene.

The invention belongs to the field of water-wind-light multi-energy complementary optimal scheduling, and particularly discloses a water-wind-light optimal scheduling method and system based on a two-stage dual-population evolutionary algorithm, and the method comprises the steps: taking the water level of a reservoir as a decision variable, taking the maximum power generation amount and the minimum residual load mean square deviation as objective functions, and constructing a multi-objective scheduling model; the two populations are initialized, and the water-wind-light multi-target scheduling model is solved; the two populations are initially in a fixed division stage, and operators A and B are respectively adopted for iterative updating; when a switching condition is met, switching to a self-adaptive cooperation stage for iterative updating, determining the selection probability of the operators A and B according to population performance at the moment, and selecting the operators based on the selection probability for iterative updating; the switching condition is that after the population is iteratively updated each time, if the population diversity is smaller than a threshold value or the evaluation frequency reaches the threshold value, stage switching is carried out. According to the method, the contradiction between convergence and diversity in water-wind-light multi-objective optimization can be solved, and accurate optimization is realized.

The invention discloses an electromagnetic pulse coupling signal separation method based on optimized variational mode decomposition, and belongs to the field of signal processing. The method comprises the following steps: carrying out injection test on an engine rotating speed control system shielding wire harness, and constructing an electromagnetic pulse coupling signal data set; a population initialization process of a parrot algorithm is improved by adopting spatial pyramid matching chaotic mapping, population diversity is improved by utilizing uniform ergodicity and initial value sensitivity of a chaotic sequence, and premature convergence is avoided; a mirror reflection learning strategy is introduced to improve the optimization precision and convergence speed of the parrot algorithm; optimization is carried out by using input parameters of improved parrot optimization variational mode decomposition, and mode decomposition is carried out on electromagnetic pulse coupling signals of a rotating speed control system; and calculating a kurtosis factor of each intrinsic mode function, and selecting a reconstruction target signal of the intrinsic mode function with the minimum kurtosis factor. According to the method, efficient and accurate electromagnetic pulse coupling signal separation can be realized, and the stability and reliability of the system in a strong electromagnetic interference environment are improved.

The invention belongs to the technical field of ship block two-dimensional irregular layout, and discloses a ship block layout method based on a critical polygon and a dynamic niche. The method comprises the following steps: constructing an NFP solving method based on an edge-vertex contact point convex hull to obtain a critical polygon, constructing a BL positioning stacking strategy on the basis to determine a feasible region, realizing accurate positioning of the irregular polygon, and calculating the fitness; designing a multi-stage adaptive genetic algorithm parameter collaborative optimization method, and dynamically adjusting genetic algorithm parameters based on population diversity and fitness variance; a dynamic niche management and crowding punishment mechanism is introduced, premature convergence of populations is avoided, and diversity of solutions is maintained; and carrying out refined optimization on the polygon rotation angle through genetic post-processing, and preferentially selecting a layout scheme. According to the method, the space utilization rate of the two-dimensional irregular layout can be effectively increased, the complex shape characteristic of the segments is fully reserved in the stacking process, and the method is suitable for the segmented stacking scene of ship construction.

The invention discloses a task allocation method and system based on an improved whale optimization algorithm framework, relates to the technical field, and is used for optimizing order type adaptive cross-domain traffic control network task allocation and improving the key task response capability of a time-sensitive traffic system. ICWOA initializes a population through Chebyshev mapping, and introduces Levy flight disturbance to enhance the optimization ability; dynamically balancing local and global search by means of adaptive parameters; relieving population diversity attenuation through randomness retention, diversity maintenance and boundary constraint; dimension type pinhole imaging reverse learning is fused to reduce high-dimensional optimization dimension interference. According to the algorithm, the convergence speed and the solving precision are better, sub-second calculation time is kept under different task scales, the task distribution efficiency is improved, and the time-sensitive scene task success rate is remarkably improved. The method solves the problems that an existing algorithm is insufficient in adaptive order type'order application-order sending 'structure and prone to falling into local optimum, population diversity attenuation and calculation speed.

The invention relates to the technical field of hydraulic engineering safety monitoring and data analysis, in particular to a dam deformation prediction method based on multi-source data dynamic fusion and a related product, and the method comprises the steps: obtaining multi-source monitoring data to construct an extended feature set; generating a weighted fusion input sequence; carrying out primary modal decomposition and secondary modal decomposition, and recombining into three types of components; obtaining a prediction result of each component; and outputting a dam deformation prediction result. According to the method, a target-oriented feature weight matrix dynamic updating mechanism is constructed, so that the problem that the actual loading state of the dam cannot be reflected by traditional static weighting is solved; by adopting a selective secondary decomposition strategy based on a modal aliasing criterion, redundant calculation of non-aliasing components is avoided while modal aliasing is effectively eliminated and high-purity characteristic components are extracted; by performing hybrid optimization on the neural network parameters, the convergence speed and population diversity are considered, the global optimality of the model parameters is ensured, and the prediction error is reduced.

The invention discloses an electric vehicle charging station planning and pricing method based on an existing charging station, and the method achieves the deep fusion of a charging station extension decision and an operation strategy through the construction of a unified framework of planning and pricing collaborative optimization, solves a problem that the overall optimization effect is not good because of the disjunction of the two in an existing method, and improves the efficiency of electric vehicle charging station planning and pricing. And the economical efficiency and the practicability of the scheme are improved. According to the improved chaos non-uniform mutation artificial hummingbird algorithm provided by the method, population diversity is ensured through chaos initialization, and a search behavior is dynamically adjusted by adopting an adaptive mutation strategy, so that the problems that a traditional optimization algorithm is easy to fall into local optimum and slow in convergence speed when processing a high-dimensional, nonlinear and multi-constraint mixed integer programming problem are effectively solved. The Pareto optimal solution set finally output by the method provides rich tradeoff selection schemes for decision makers, scientific decision making according to different preferences (such as cost minimization or user satisfaction maximization) is supported, and the flexibility and applicability of the method are enhanced.

This invention belongs to the field of power grid fault diagnosis technology and discloses a power grid fault diagnosis method and system based on differential evolutionary logic operations. The method includes: collecting the action information of protection devices and circuit breakers in the power system; filtering out a set of candidate elements that have experienced faults based on preset association rules between faulty elements and protection / circuit breaker actions; establishing a 0-1 integer programming model based on the determined protection action information, circuit breaker action information, and candidate element set; and solving the 0-1 integer programming model using an improved differential evolutionary algorithm based on logic operations to determine the fault state of the candidate elements. This invention directly encodes individuals using binary, eliminating the need for floating-point to binary conversion. It also constructs a binary mutation operator and an improved crossover operator based on logic operations and employs adaptive parameter adjustment to balance the population diversity and search efficiency of the algorithm.

This invention provides a method, system, and electronic device for image recognition in the forestry and fruit industry, belonging to the field of intelligent agriculture technology. The method first determines the hyperparameters to be optimized and initializes the snow goose population; in the migration exploration phase of iterative optimization, a lead goose is selected from multiple candidate individuals using a probabilistic strategy to guide the position update; in the foraging and development phase, the position is updated using the complementary solutions of elite individuals; finally, the model is updated based on the optimal configuration obtained through optimization, and the forestry and fruit images are recognized. This invention effectively maintains population diversity by selecting the lead goose using a probabilistic strategy, enhances local development accuracy using the complementary solution update mechanism, avoids the algorithm getting trapped in local optima, and significantly improves the accuracy and reliability of image recognition in the forestry and fruit industry.

The present disclosure discloses a method for reconstructing the working face of coal mine based on search space pruning and multi-population dynamic adjustment, which involves the technical field of working face reconstruction of coal mine under incomplete projection conditions, comprising: based on prior knowledge of the working face reconstruction model structure, an initial grid partitioning of the exploration area is performed. Subsequently, multi-scale grid partitioning is achieved through search space pruning, guided by the sum of ray intercepts within each grid unit; to meet the requirements of population diversity and rapid convergence in the multi-population genetic algorithm, a multi-scale reconstruction objective function is constructed based on the partitioned multi-scale grids, this objective function is then solved using a dynamic multi-population genetic algorithm. Therefore, the above-mentioned method for reconstructing the working face of coal mine based on search space pruning and multi-population dynamic adjustment may dynamically adjust and optimize the number of populations, to ensure that the diversity of populations may be maintained, and the search efficiency may be improved, thereby improving the stability and convergence speed of the working face reconstruction operation.

The application relates to the technical field of GIS equipment PD type determination, and particularly discloses a GIS equipment PD type determination method using an improved marine predator algorithm to optimize SVM. On one hand, the initial population of the original marine predator algorithm is randomly generated, which may cause uneven distribution of the initial population individual positions, reduce population diversity and affect the optimization speed; therefore, a chaos mapping strategy and a reverse learning mechanism are introduced to improve the initial population quality. On the other hand, the original marine predator algorithm has defects such as being prone to falling into local optimization and slow convergence speed; therefore, a multi-subpopulation disturbance idea is proposed. The prey population of the marine predator disturbed by the FADs effect is divided into two subpopulations according to the fitness function value; the subpopulation with a higher fitness function value is disturbed according to the self-adaptive Cauchy mutation, and the other subpopulation is subjected to differential evolution, so as to generate a multi-subpopulation disturbance solution which is brought into the next iteration.

The application discloses a train operation table compiling method based on Morlet parameter adaptive differential evolution algorithm, and the method is characterized in that: according to a preset weight coefficient, three indexes of 'line capacity maximization', 'departure frequency minimization' and 'total passenger travel time minimization' are weighted and synthesized to construct a comprehensive objective function, so as to establish a single-objective optimization model, and the decision variables include the departure frequency of large and small inter-route trains and the operation interval of small inter-route trains. The application adopts an improved differential evolution algorithm for solving: through a two-stage adaptive mechanism based on a Morlet function, a scaling factor and a crossover probability are dynamically adjusted, the sensitivity of the algorithm to parameters is reduced, and an opposite disturbance strategy is introduced in the crossover operation to enhance the population diversity. Finally, the large and small inter-route train operation timetables are automatically generated according to the optimal solution. The application can flexibly adapt to different operation scenes and dynamic changes.

The application belongs to the technical field of semi-active suspension control method, and relates to a semi-active suspension control method based on an improved multi-objective particle swarm optimization algorithm, which comprises self-adaptive grid division of an external archive of the MOPSO algorithm to determine a global optimum with the most diversity and a global optimum with the most convergence; evolution state of a population is judged according to a distribution entropy change quantity of all optimal solutions in the external archive, and a global optimum gBest for guiding evolution is selected; flight parameters of population evolution are adjusted according to a change quantity of local crowding distance of current population evolution to balance evolution trend of the population; the above two types of core mechanisms are fused, a multi-information fusion multi-objective particle swarm optimization algorithm is proposed, and the algorithm is used for determination of weight coefficients of a semi-active suspension LQR control strategy. The novel multi-objective optimization algorithm effectively realizes balance between convergence precision and population diversity by synergistically integrating external archive information and current population dynamic information.

This application discloses a multi-strategy adaptive particle swarm optimization method and apparatus for dynamic parking space scheduling, belonging to the field of parking space scheduling technology. The method includes: cleaning flight and parking space data to construct a flight-parking space compatibility matrix; mapping particle positions using continuous real-number encoding and initializing the population through an OBL (Optimal Boundary Learning) strategy; adaptively adjusting the inertia weight AIW based on population diversity and combining it with a random migration RI (Increase in Randomization) strategy to avoid local optima; applying boundary constraints and conflict resolution to particle positions, and using a DA (Data Determination) mechanism based on congestion distance to preserve non-dominated solutions; and outputting the optimal parking space scheduling scheme through multi-attribute decision-making after the iteration meets the termination condition. This application improves the algorithm's convergence speed and global optimization capability through multi-strategy collaborative optimization, reduces the proportion of infeasible solutions, increases parking space utilization and flight docking rate, and balances passenger travel experience with airport operational efficiency.

The application provides a silicon-aluminum alloy turning process parameter multi-objective optimization method based on an improved BPNN-DE algorithm, first, a BPNN model taking a cutting speed, a feed speed and a cutting depth as input and taking a surface roughness, a material removal rate and energy consumption as output is built, and the DE algorithm is improved to improve population diversity and optimization ability of the original DE algorithm, then the output of the BPNN model is combined with a geometric mean method to serve as fitness value of the improved DE algorithm, finally, the improved BPNN-DE algorithm is used to optimize the turning process parameters, and the improved BPNN-DE algorithm has good optimization effect compared with the unimproved BPNN-DE algorithm.

The application discloses a method and equipment for optimizing arrangement of multiple types of wind turbines in an offshore wind farm and a readable medium, and belongs to the technical field of offshore wind farm planning and design and intelligent optimization algorithm. The method realizes integrated coding of wind turbine position, type and activation state by using four-dimensional chromosome coding, retains gradient information through a soft constraint smoothing penalty mechanism, corrects space violations by combining a guided multi-strategy mutation operator that fuses wind power engineering knowledge, balances global exploration and local development capabilities of the algorithm by supplementing population diversity repair and elite-random environment selection strategies. The application can minimize the penalty electricity cost on the premise of meeting the target capacity, adapt to irregular non-convex and offshore wind farms containing complex engineering constraints, and improve the economic benefits and layout optimization efficiency of the whole life cycle of the offshore wind farm.

The present application relates to a kind of animal population diversity monitoring device and method.The monitoring device includes track, track is annular and track whole is not on a plane;Mobile mechanism is arranged on track;Monitoring camera is arranged on mobile mechanism, for monitoring animal in forest;Power supply mechanism is arranged on track, for mobile mechanism is powered;Boost mechanism is arranged on track, for when mobile mechanism moves upward, mobile mechanism is provided with boost;The present application is arranged in forest by annular track, and different height and different area are realized in forest by monitoring camera moving along track using trolley along track, so that different population of animal in forest is monitored in real time, so that the real degree of monitoring can be improved, and desired information is obtained from monitoring process.

The invention relates to a rectification system multi-objective optimization method based on dynamic NSGA-II, and belongs to the technical field of chemical process system optimization. The method comprises: determining a decision variable and a target function according to a rectification system model; a diversity index is obtained by calculating an invalid solution proportion of each generation of population and diversity of a target and decision space; and the crossover probability, the mutation probability and the population size are dynamically adjusted, a dynamic NSGA-II algorithm is constructed, the rectification system is optimized, and an optimal solution is obtained. According to the method, dynamic parameter adjustment is achieved by introducing the invalid solution proportion and the population diversity index, the number of non-convergent solutions can be effectively reduced, the population diversity is enhanced, premature convergence is avoided, the calculation cost is reduced while the optimization efficiency is improved, and an efficient and practical solution is provided for multi-objective optimization of a complex chemical system.

The invention relates to the field of federal learning, and provides a federal feature selection method based on evolutionary multi-task optimization. The method comprises the following steps: each client generates an initial feature selection population based on local data of the client, and uploads a current optimal feature subset to a server; the server carries out client layering through cross evaluation based on the current optimal feature subsets uploaded by the clients and calculates the similarity between tasks; each client updates a local population based on a hierarchical strategy of the server, and in a local optimization process, according to a task similarity and a migration strategy guided by the server, a knowledge migration operation perceived by the similarity is executed, and meanwhile, population diversity is dynamically maintained; and each client uploads an optimized result to the server, and the server screens a global excellent solution and updates a system state for a next round of iteration.

The invention relates to the technical field of power distribution network flexibility resource management and scheduling optimization, and discloses a power distribution network and virtual power plant cooperative regulation and control method, system and device and a medium, and the method comprises the steps: constructing a partition cooperative regulation and control model of a power distribution network and a virtual power plant in a power system; solving the partition coordinated regulation model through an improved sea gull optimization algorithm to obtain an optimal partition result and a corresponding unit scheduling strategy; according to the improved sea gull optimization algorithm, a self-adaptive adjustment mechanism based on population diversity feedback is introduced in the position updating process of the sea gull optimization algorithm. According to the method, the limitation of a traditional seagull optimization algorithm is broken by introducing an adaptive adjustment mechanism, the performance of the algorithm in the aspects of solution quality, convergence speed and robustness is greatly improved, and accurate regulation and control of flexible resources in a power system are achieved.

The application discloses an electrocardiogram classification method based on an improved parrot algorithm optimized residual network, relates to the technical field of electrocardiogram classification based on a machine learning algorithm, and solves the problems of low initial population diversity of an existing parrot algorithm, easy falling of an optimization model into a local optimal solution, and low precision of a model classification result when an electrocardiogram classification and identification task is performed. The application extracts signal features from electrocardiogram signal data, divides the electrocardiogram signal data into a training set and a test set, constructs an electrocardiogram classification model of a ResNet, adopts two improved parrot algorithms of an adaptive convergence factor and a hybrid Cauchy Gaussian variation to optimize the classification model, updates the model by using optimized parameters, finally obtains a trained classification model, and inputs the test set into the trained classification model to realize accurate classification of electrocardiograms. The application has stronger global search capability and avoids the risk of falling into a local optimum. The classification effect of electrocardiogram data is more accurate.

This invention discloses an AUV-UFastSLAM algorithm optimized based on the whale algorithm, comprising a series of processes including initialization, prediction, sampling, map updating, and resampling. This method optimizes the particle sampling process using the whale algorithm, enabling the particle swarm to move towards a high-likelihood region, making the AUV pose estimation closer to the true value. An inertia weight factor is introduced to improve the whale algorithm's position update formula, increasing the population convergence speed and accuracy. Simultaneously, Cauchy mutation is used to randomly perturb the optimal neighborhood to increase population diversity and improve the algorithm's global search capability. An improved resampling method is used for particle filtering to ensure particle diversity. Through these adjustments, the accuracy of AUV simultaneous localization and map creation is improved.

The application discloses a coal mine working face reconstruction method based on search space pruning and multi-population dynamic adjustment, relates to the technical field of coal mine working face reconstruction under incomplete projection conditions, and comprises prior knowledge based on a working face reconstruction model structure, preliminary grid division of a working face exploration area, and multi-scale grid division through search space pruning according to the sum of ray intercepts in each network; on the basis of considering the population diversity and fast convergence requirements of a multi-population genetic algorithm, a multi-scale reconstruction objective function is constructed based on the divided multi-scale grid, and is solved through a dynamic multi-population genetic algorithm. Therefore, the coal mine working face reconstruction method based on search space pruning and multi-population dynamic adjustment can dynamically adjust and optimize the number of populations, so that the diversity of the population can be maintained, the search efficiency is improved, and the stability and convergence speed of working face reconstruction operation are improved.

The invention discloses a DPA-NSGA-II-based beam string structure vibration control optimization method and system, and belongs to the technical field of beam string structure optimization. Constructing a multi-target vibration control optimization model of the beam string structure by taking structural acceleration response and energy consumption as targets according to the structural characteristics of the beam string structure; the multi-target vibration control optimization model of the beam string structure is converted into a mathematical optimization model, and a rapid elite multi-target genetic algorithm DPA-NSGA-II is improved by using a dynamic adaptive crossover operator based on population diversity and a mutation operator perceived by an evolution state to generate a Pareto optimal solution; and evaluating the Pareto optimal solution based on a TOPSIS decision method, and determining the vibration control optimal solution of the beam string structure. According to the method, vibration acceleration suppression and actuator energy consumption minimization serve as two parallel optimization targets, and a series of Pareto optimal solutions can be obtained through single calculation.

The invention provides a table data anomaly detection method and system based on a genetic algorithm learning normal form, and belongs to the technical field of abnormal data detection.The method comprises the steps that firstly, a dirty data set to be detected is obtained, an initial code is obtained in combination with a word vector model and a TF-IDF algorithm, weighting is carried out by means of a context attention mechanism, and data instance code representation is obtained; then, a genetic algorithm serves as a data selector, a data set is divided into tuple individuals, the individual fitness is calculated through a comprehensive fitness function, and population initialization and selection strategy design are completed; then performing crossover and mutation operations, screening population diversity individuals, and preliminarily labeling high-probability normal and abnormal individuals; and finally, on the basis of the labeled sample set, positive and negative sample packets are established by a label diffusion mechanism, pseudo-label expansion training data are generated through adaptive iteration, and comprehensive judgment of table data exception is realized through model training. According to the invention, the problem that diversified errors cannot be comprehensively and systematically covered without human participation in the prior art is solved.

The invention relates to the field of artificial intelligence and feature selection, in particular to a feature selection meta-heuristic algorithm method based on a three-person voting rule. The method comprises the following steps of: firstly, constructing an initial feature subset population of binary coding; secondly, three individuals are randomly selected in each iteration to form a group, and a new candidate feature subset is generated based on a bit-by-bit majority voting rule; when the values of the three individuals on a certain bit are the same, a flipping probability decreasing along with iteration rounds is introduced to carry out probabilistic inversion on the bit so as to keep population diversity; then, carrying out fitness evaluation on the new subset, comparing the new subset with individuals in the group, and replacing a poorer solution with a better solution; and finally, continuously updating the population through multiple iterations until an optimal feature subset is obtained. The method can effectively solve the problems of easy local optimum, low convergence speed and high feature redundancy in the feature selection process, and significantly improves the accuracy and calculation efficiency of feature selection.

The invention discloses an intelligent ore blending method based on a CEALA algorithm. According to the invention, the co-evolution ALA algorithm is applied to an intelligent ore blending scene. The population scale is dynamically adjusted according to the population evolution condition in the optimization process through a cyclic population attenuation mechanism, the calculation cost is reduced, and convergence is accelerated while the population diversity is guaranteed. Meanwhile, the algorithm provides a co-evolution mechanism, based on a self-adaptive exploration mechanism and a self-adaptive development mechanism, individuals are dynamically allocated to different evolution mechanisms, the influence of parameter setting on the optimization performance of the algorithm is reduced, exploration and development are balanced, population diversity is increased, and local optimum is avoided. A parameter adaptive mechanism is introduced into the algorithm to capture beneficial information in a population iteration process, so that the influence of parameter setting on the optimization performance of the algorithm is reduced, and the robustness of the algorithm is enhanced. And finally, the algorithm is based on external archiving, beneficial information of abandoned solutions is mined, and the optimization performance of the algorithm is improved.

The invention belongs to the technical field of landslide displacement prediction, and discloses a loess landslide displacement prediction method based on an improved education competition optimization algorithm and an ICEEMDAN-LSSVM, and the method comprises the steps: carrying out the preprocessing and normalization of collected landslide data; decomposing the original displacement sequence into a plurality of intrinsic mode functions and residual terms by utilizing adaptive noise complete ensemble empirical mode decomposition (ICEEMDAN); a least square support vector machine (LSSVM) model is established, and an improved educational competition optimization algorithm (IECO) is adopted to carry out automatic optimization on kernel parameters and penalty coefficients of the model. According to the IECO algorithm, population diversity is enhanced through Latin hypercube sampling, adaptive t distribution variation and multi-scale Gaussian collaborative variation strategies are introduced, and dynamic balance between global exploration and local development is achieved. And finally, superposing and reconstructing prediction results of the components to complete displacement prediction. The method has the advantages of high prediction precision, strong robustness and the like, and provides reliable technical support for early warning and prevention and control of landslide disasters.

The application relates to a WSN cluster head election method and system based on an elite multi-objective gold panning algorithm, and the method comprises the following steps: calculating an optimal cluster head number based on a wireless sensor network; generating an initial population by using a Sobol sequence and performing population iteration; updating individuals of the population by using an optimized gold panning optimization algorithm to obtain a first population; performing Gaussian mutation on the first population to obtain a second population; combining the three populations to form an enhanced elite candidate population; performing target function calculation, quickly non-dominant sorting the individuals by using an NSGA-III algorithm to obtain the level to which the individuals belong; judging whether the current iteration number reaches a target iteration number; if not, selecting the next generation population based on the enhanced elite candidate population and by using the NSGA-III algorithm to perform population iteration; and if yes, outputting a Pareto front to determine a cluster head node in a wireless sensor network (WSN). The application introduces the enhanced elite candidate population to expand population diversity and avoid loss of historical elite solutions.