141 results about "Population diversity" patented technology

The invention discloses a method for dispatching automatic guided vehicles in a parallel arrangement container terminal. The method comprises the following steps: collecting container terminal AGV dispatching parameters; establishing an AGV scheduling optimization model; solving the AGV scheduling optimization model; and outputting an AGV scheduling scheme. The AGV scheduling optimization model with minimum AGV power consumption and minimum operation completion time as optimization objectives is constructed by considering the influence of AGV path conflicts and the charging process on actual operation, so that collaborative optimization of AGV charging, task assignment and path planning problems is realized, and the method has the characteristics of high universality and wide coverage. According to the method, the improved adaptive genetic algorithm is adopted, and adaptive adjustment of crossover and mutation operators gives consideration to population diversity and convergence rate at the same time. And meanwhile, conflict-free path planning is carried out by adopting a space-time A * algorithm, so that collaborative optimization of AGV task scheduling and path planning is realized, the prediction precision is high, the convergence is fast, and the workload is small.

The invention discloses a cable operation state fault rapid detection method based on an improved grey wolf algorithm model. The method comprises the following steps: collecting fault data through a detection unit; cleaning, denoising, normalization processing and feature extraction are carried out on the collected fault data; constructing an improved grey wolf algorithm model, wherein the improved grey wolf algorithm model comprises a coding module, an adaptive weight adjustment module, a multi-objective optimization module, a population diversity control module and an optimal path search module; fault detection is carried out on the cable operation state data through a global search module and a local search module; real-time monitoring and early warning are carried out, wherein the real-time monitoring and early warning comprise early warning threshold setting, early warning signal triggering, early warning notification and feedback optimization; according to the invention, the operation state fault of the cable is detected through the improved grey wolf algorithm model, and the detection efficiency and the accuracy of cable fault positioning are greatly improved.

The invention relates to the technical field of panel processing, in particular to a panel furniture workpiece typesetting method based on an improved genetic algorithm, which comprises the following steps: acquiring parameter information of a to-be-typeset workpiece; and performing classification based on the parameter information of all the workpieces to be typeset to obtain a plurality of workpiece categories, endowing each workpiece category with a unique type code, constructing an initial population according to the category and the number of the workpieces to be typeset, and inputting the initial population into the improved genetic algorithm to obtain an optimal chromosome. According to the method, the workpieces with the same size and shape are classified into workpieces with the same specification, and a repeated gene coding mode is adopted, so that the defect that actual repeated chromosomes are generated when a plurality of workpieces with the same specification are processed in a traditional unique coding mode is effectively avoided, and the algorithm efficiency is improved. And secondly, in the gene crossover operation, an improved ordered crossover and order crossover strategy is introduced, and a random deletion mechanism is combined, so that the algorithm population diversity and the solution space coverage are effectively improved, and the search divergence and convergence efficiency are improved.

The invention discloses an unmanned aerial vehicle flight path planning method based on a hybrid WOA-CEM-DRL algorithm, and the method is based on a hybrid WOA (whale optimization algorithm), a cross entropy method (CEM) and deep reinforcement learning (DRL), and aims at solving the problems that an existing algorithm is slow in convergence speed and is liable to fall into a local optimal solution in a complex three-dimensional environment. The invention provides a three-stage dynamic coevolution framework. In the early stage of evolution, population diversity is enhanced through global disturbance of CEM; in the middle stage of evolution, exploration and development are balanced; and in the later stage of evolution, performing fine tuning on the elite solution by using gradient information of the DRL. According to the method, global search of WOA, efficient sampling of CEM and rapid convergence capability of DRL are fused, and the method is mainly used for accurately generating a safe and smooth optimal track for an unmanned aerial vehicle in a complex three-dimensional environment.

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 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 belongs to the technical field of semi-active suspension control methods, and relates to a semi-active suspension control method based on an improved multi-objective particle swarm optimization (MOPSO) algorithm, which comprises the following steps of: performing adaptive grid division on an external file of the MOPSO algorithm to determine global optimum with the most diversity and global optimum with the most convergence; judging the evolution state of the population according to the distribution entropy variation of all optimal solutions in an external file, and selecting a global optimal gBest for guiding evolution; according to the variable quantity of the local crowding distance of the current population evolution, adjusting flight parameters of population evolution to balance the evolution trend of the population; and fusing the two core mechanisms, providing a multi-information fusion multi-target particle swarm optimization algorithm, and applying the multi-information fusion multi-target particle swarm optimization algorithm to determination of a semi-active suspension LQR control strategy weight coefficient. According to the novel multi-objective optimization algorithm, external archive information and current population dynamic information are cooperatively integrated, so that balance between convergence precision and population diversity is effectively realized.

The invention provides a building component prefabrication task scheduling method based on quantum inspiration, and relates to the technical field of task collaborative scheduling, and the method comprises the steps: abstracting a building prefabrication task into a quantum optimization model, and coding a task allocation scheme through a quantum bit probability amplitude; and the quantum superposition state is collapsed into a classical feasible solution through Monte Carlo observation, and multi-target quantitative evaluation is carried out by using a weighting function. A controlled phase gate is introduced to realize directional crossing among chromosomes, a simulated annealing temperature attenuation strategy is fused to dynamically adjust variation intensity, probability amplitude distribution is adjusted in real time according to fitness difference by using an adaptive quantum revolving door, and population quality is continuously improved. Monitoring resource load and process dependence, and performing intelligent repair and elastic scheduling when a conflict or overrun occurs; and iteration termination adopts triple criteria of an algebraic threshold value, a fitness improvement rate and population diversity, after convergence, the final generation individuals are comprehensively weighed, and a global optimal scheduling scheme is output.

The invention discloses a multi-strategy improved fox-monkey optimization algorithm for global optimization and application thereof, and belongs to the technical field of optimization algorithms, the method comprises the following steps: firstly, using Chebyshev chaotic mapping to initialize a population so as to improve the diversity and coverage range of the initial population; secondly, a difference population evolution strategy is introduced in the iteration process, and the algorithm is prevented from falling into local optimum in the later period; and finally, introducing a crisscross strategy to enhance global search capability and maintain population diversity. 20 benchmark test functions and a part of complex functions in a cec2017 function set are adopted for a simulation experiment, and the experiment result shows that the optimization precision, the convergence speed and the stability of the improved algorithm are obviously improved. The improved algorithm is applied to the engineering optimization problem, compared with an original algorithm, the convergence precision of the algorithm in three actual engineering problems is improved by 19.56%, 19.18% and 6.7% respectively, the standard deviation is reduced by 91.44%, 27.56% and 92.19% respectively, and the feasibility of the improved algorithm is further verified.

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 provides a hybrid MPPT (Maximum Power Point Tracking) control method based on a chaos initialization Levy flight Swane algorithm and an improved conductance increment method, belongs to the field of photovoltaic power generation systems, and aims at enhancing population diversity through chaos initialization, improving global search capability in combination with a Levy flight strategy and switching to the improved conductance increment method in an optimal solution neighborhood to realize high-precision tracking. The Logistic chaotic mapping initialization is combined to solve the problem that local optimum is caused by periodic values or uneven distribution easily occurring in the random initialization of the traditional South goose algorithm, the Brownian motion of the original South goose algorithm is changed into the Levy flight, the dynamic beta is increased, the global exploration and local exploration capabilities are balanced, local optimum is avoided, the improved conductance increment method is combined, and the local optimization is improved. And finally, the optimal duty ratio is screened by using a sliding window to lock the output, so that the problem of insufficient MPPT precision of a traditional optimization algorithm is solved, and the problem of power jitter near the maximum power point of a traditional conductance increment method is solved.

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 relates to a dynamic multi-objective optimization method and device, equipment and a medium. The method comprises the following steps: establishing an event model of a multi-objective optimization event; if it is detected that the environmental information changes, a first group and a second group are obtained based on a multi-objective evolutionary algorithm and a historical optimal set, an original optimal set of an event model is determined according to the first group and the second group, the first group is obtained based on a representative individual prediction algorithm, and the second group is obtained based on an inflection point prediction algorithm; otherwise, taking the Pareto optimal solution of the event model at the current moment as an original optimal set; and inputting the original optimal set into a social optimization learning model, and outputting a target optimal set of the event model through the social optimization learning model. According to the method, when the change of the environment information of the multi-target optimization event is detected, the target optimal set is output through the social optimization learning model by improving the population diversity and improving the precision of the Pareto optimal solution from the perspective of the population diversity.

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 a distributed wind power site selection and optimization method, system and device and a medium, and the method comprises the steps: obtaining wind resource data of a candidate wind power plant region, and building a multi-factor physical model; constructing a multi-factor wind field site selection model, and calculating optimal solutions of total power generation power maximization and total power generation cost minimization; and determining an optimal site selection scheme by combining the model optimal solution, wind resource distribution and topographic conditions. By improving the reserved suboptimal solution of the NSGA2 algorithm and dynamically adjusting the crossover and mutation probability, the population diversity is effectively maintained, the global search ability is enhanced, and a decision maker is supported to select the optimal layout; wind resource characteristics are comprehensively quantified through a multi-factor physical model, and the physical accuracy of site selection analysis is improved; through model hypothesis, the model complexity is reduced on the premise of ensuring the reliability, and the calculation efficiency is improved; the balance between economical efficiency and power generation efficiency is realized by taking minimization of power generation cost and maximization of power generation power as targets through a multi-factor wind field site selection model.

The invention discloses a dynamic cooperative wireless charging system control method based on a genetic gradient optimization algorithm, and the method comprises the steps: carrying out the global search based on a genetic algorithm, taking the transmitting power and phase angle of a transmitting coil of a group of transmitting arrays as particles for coding, and evaluating the adaptation degree of individuals in a population through building a fitness function. Iteration of the population is realized through roulette selection and self-adaptive crossover and variation; when the population diversity is insufficient, triggering gradient descent to carry out local deep optimization on the optimal individual; selecting excellent individuals of current genetic iteration, and performing gradient calculation and updating by taking phase angle parameters of the excellent individuals as particles; and combining the phase angle parameter with the power parameter to form a new population, sorting individuals in the new population according to fitness, retaining the individuals with the fitness being first 10%, continuing to enter genetic algorithm iteration, and starting a new round of circulation. The energy efficiency of the system is improved, and the convergence speed of the algorithm and the electromagnetic safety are optimized.

The application aims at providing a multi-stage multi-level assembly job shop energy-saving scheduling multi-objective optimization method, relates to the technical field of assembly shop scheduling, and establishes a mathematical model of the problem in stages, and designs a problem-driven energy-saving strategy triggering mechanism according to the individual state, so that the quality and search efficiency of the solution are improved. Secondly, two heuristic rules and a random generation method are used to construct an initialization population that takes into account high quality and diversity, and a dynamic self-adaptive adjustment strategy for the assimilation operator parameters is realized through Q-learning, so that the convergence speed is improved while the population diversity is ensured, so that the exploration and mining ability of the algorithm is better balanced. A revolutionary operation guided by a hyper-heuristic variable neighborhood search is designed, and a high-quality colony found is searched in detail. The joint empire invasion operation is used to replace the competition, realizes the cooperative evolution and information interaction sharing of multiple empires, and thus finds non-dominated solutions with more uniform distribution.

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.

The invention discloses an electrocardiogram classification method for optimizing a residual network based on an improved parrot algorithm, relates to the technical field of electrocardiogram classification based on a machine learning algorithm, and solves the problems that an existing parrot algorithm is low in initial population diversity and an optimization model is easy to fall into a local optimal solution. According to the method, signal feature extraction is carried out on electrocardiogram signal data, and the electrocardiogram signal data are divided into a training set and a test set; constructing an electrocardiogram classification model of the ResNet, optimizing the classification model by adopting two improved parrot algorithms of an adaptive convergence factor and hybrid Cauchy Gaussian variation, updating the model by utilizing optimized parameters, and finally obtaining a trained classification model; the test set is input into the trained classification model, and accurate classification of the electrocardiogram is achieved. The method has higher global search capability, and avoids the risk of falling into local optimum. And the classification effect of the electrocardiogram data is more accurate.

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 present application relates to the technical field of photonic device design, and especially relates to a photonic device reverse design optimization method, the present application adopts sin mapping, segmented linear chaotic mapping and reverse learning combined mode to generate initial population, enhances initial population diversity, reduces initial error and accelerates algorithm convergence; adopts Levy flight and teaching mechanism alternating parallel mode to accelerate algorithm convergence process, and adopts teaching factor decreasing with iteration number to further accelerate algorithm to find optimal solution; designs dynamic self-adaptive discovery probability, discovery probability linearly decreases in search late stage, and local optimal solution is more easily discovered, thereby accelerating local convergence; differential evolution retains individuals with higher fitness, so that population continuously approaches optimal solution, thereby increasing algorithm convergence speed; in addition, the present application adopts differential evolution mechanism to generate new individuals through differential calculation on individuals in population, thereby increasing population diversity and global search capability.

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 invention discloses a hospital operation efficiency management method and system based on a CMP value, and belongs to the technical field of hospital management, and the method comprises the steps of hospital operation data collection, hospital operation data preprocessing, hospital operation efficiency evaluation model construction, model parameter optimization and hospital operation efficiency management. According to the scheme, the optimal number of neighbor nodes is dynamically determined, a topological structure and feature information are fused to construct a node similarity matrix, enhanced features are obtained by aggregating the neighbor nodes, node features are learned through a double-layer feature learning network, and similarity comparison loss is introduced to set a total loss function; according to an iterative search progress proportion and population diversity, a dynamic adjustment coefficient, a weight coefficient and a disturbance coefficient are designed, an elite layer is updated based on the three coefficients, an exploration layer is updated based on an all-body optimal position and a random individual position, a restart mechanism is set, a CMP value is introduced, and the accuracy of hospital operation efficiency evaluation is improved. And reliable support is provided for management decision.

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.