222 results about "Population diversity" patented technology

The invention discloses a path planning heuristic function generation platform and method based on collaborative optimization of a large-scale language model and evolutionary computation. According to the technology, the large-scale language model (LLM) and evolutionary computation (EC) work cooperatively. The platform generates or mutates a heuristic function expressed as an executable code through LLM based on a structured prompt containing an environment context and performance feedback; and an EC framework (such as genetic programming) is combined with performance evaluation feedback to perform selection and iterative optimization on a heuristic code population, and population diversity is maintained. The method aims at overcoming the limitation that a traditional heuristic design is difficult and poor in adaptability, a high-quality heuristic function adapting to a complex and dynamic environment is automatically generated, and therefore the efficiency of a path planning algorithm and path quality are remarkably improved.

The invention discloses an unmanned aerial vehicle path planning method and system for a complex urban environment, and relates to the technical field of unmanned aerial vehicle path planning. According to the method, a plurality of algorithms are deeply fused, and a combination mode and an information transmission and optimization result inheritance mechanism between stages are beneficial to path planning of the unmanned aerial vehicle; a self-adaptive learning factor with a time-varying period is designed, the global exploration and local development balance capability of PSO is remarkably improved, and the population diversity and the global search capability are enhanced; through rapid global guidance of PSO, population dynamic enhancement of SSA, solution space depth optimization of GA and final precision improvement of LS, the path of the unmanned aerial vehicle can be rapidly and efficiently planned.

The invention discloses an engineering optimization method based on an improved particle swarm algorithm, and the method comprises the steps: constructing a particle swarm optimization model according to an engineering problem mathematical model, and initializing the position and speed of particles; then, calculating a fitness value index and a diversity index of the particles according to the model, and constructing convergence and diversity data according to the fitness value index and the diversity index; the two kinds of data are used for updating the initial positions and speeds of the particles, and the middle adjustment positions and speeds are obtained. Then, on the basis of the particle swarm optimization model and the intermediate adjustment position, the particle individuals and the global position are updated, and individual convergence and global convergence positions are obtained; and calculating a particle swarm output result according to convergence and diversity data and individual and global convergence positions. And finally, obtaining an optimal solution of the engineering problem according to an output result. According to the embodiment of the invention, two kinds of data are constructed by taking population diversity and fitness value as evaluation criteria, the population position and speed are updated, the optimal solution can be found more quickly and accurately, and the engineering optimization problem is efficiently solved.

The invention relates to a fuzzy flexible job shop scheduling method, a fuzzy flexible job shop scheduling system, fuzzy flexible job shop scheduling equipment and a storage medium, which are applied to a scene in which scheduling is required when disturbance occurs in a job shop operation process. Parameters of the genetic algorithm are initialized according to the real-time resource state of the current production environment, wherein the parameters comprise the maximum number of iterations; an initial population is generated according to the parameters, a weight vector is initialized, an external file is created, the external file is used for storing an optimal solution or an optimal solution in the algorithm, and the optimal solution is a solution capable of increasing population diversity; selecting an operation individual in the population and the external archive; and performing iterative optimization on the population based on the operation individuals until the maximum number of iterations is reached, outputting a non-dominated solution set, and after each iterative optimization, updating the external archive and the weight vector based on the generated non-dominated solution so as to optimize the fuzzy flexible job shop scheduling problem under the urgent order insertion and improve the production efficiency and the response flexibility.

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 micro-grid optimal scheduling method based on a self-adaptive scheduling participation mutation particle swarm algorithm, and aims to improve the operation efficiency and environmental protection performance of a micro-grid. According to the method, a self-adaptive parameter adjustment method and a variation mechanism are innovatively provided, the problems that a traditional particle swarm algorithm is prone to premature and lacks population diversity are solved, and the global search capability and the optimization effect are remarkably enhanced. Constructing a mathematical model in a microgrid grid-connected operation mode by taking the operation cost and the environmental protection cost as optimization targets; scheduling optimization is carried out by using a self-adaptive scheduling participation mutation particle swarm algorithm; through comparison simulation experiments, the superiority of the method under different load requirements and resource distribution conditions is verified. The method is suitable for micro-grid optimization scheduling scenes including distributed power supplies, energy storage systems and the like, and maximization of economic and environmental protection benefits can be realized while power supply reliability is ensured.

The invention relates to a flexible job shop energy-saving batch flow scheduling optimization method considering machine tool deterioration and preventive maintenance, and belongs to the technical field of shop scheduling. Comprising the following steps: constructing a flexible job shop energy-saving batch flow scheduling mathematical model taking completion time, total energy consumption and total production cost as optimization targets and considering machine tool deterioration and preventive maintenance; a model solving method is put forward, a strategy is introduced based on population diversity of historical populations, and the coping capacity of the algorithm to environment changes is enhanced; developing a self-adaptive crossover mutation operator, and balancing the diversity and convergence of the algorithm; and proposing a neighborhood search strategy, and exploring a local optimal solution of the energy-saving batch flow scheduling problem. According to the method, the problem of flexible job shop energy-saving batch flow scheduling multi-objective optimization considering machine tool deterioration and preventive maintenance can be efficiently solved, under-maintenance or excessive maintenance of the machine tool is effectively avoided, production interruption and maintenance cost are reduced, and a scheduling scheme with excellent completion time, total energy consumption and total production cost is obtained.

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 is suitable for the technical field of structure optimization design, and provides a tension-compression spring design method based on an improved particle swarm optimization algorithm. According to the improved particle swarm optimization (SuperMOP) algorithm provided by the invention, an elite archive strategy and an advantage combination learning strategy are fused, and a mutation operator based on an individual level is introduced. Different roles are allocated to particles according to the performance of the particles, so that a learning strategy is dynamically updated. The improved particle swarm optimization algorithm is compared with several most advanced algorithms, and the result shows that the SuperMOP algorithm effectively balances the exploration and utilization process, improves the population diversity and enhances the diversity of learning samples. According to the method, the improved particle swarm optimization algorithm is applied to the design problem of the tension-compression spring, a new mode with strong global search capability is provided for the design problem of the tension-compression spring, and the method is particularly suitable for solving the optimization problem of complex and changeable constraint engineering.

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 path planning method and device, equipment, a storage medium and a product, and relates to the technical field of unmanned transportation, and the method comprises the steps: obtaining an initial path population, taking the initial path population as the first input, and carrying out the loop iteration through employing a differential evolution algorithm, a squirrel search algorithm and a large neighborhood search algorithm. According to the method, the scale factor and the crossover operator which adaptively change based on the number of iterations are introduced, so that the mutation and crossover probability of the differential evolution algorithm is adjusted, the differential evolution algorithm is prevented from falling into a local optimal solution along with the increase of iterations, and the diversity of a population is kept; a squirrel search algorithm is introduced to optimize a population solved by a differential evolution algorithm, a large neighborhood search algorithm is combined, a local optimal solution created by the differential evolution algorithm can be effectively evaded, and based on continuous iteration, a logistics planning scheme with lower output energy consumption and higher efficiency is achieved.

The invention discloses a neural architecture search method based on a diffusion evolution algorithm, and the method comprises the steps: firstly constructing a supernet which comprises subnets, and enabling each subnet to give a weight to each operation on each connection edge through continuous coding; then obtaining different types of pictures to construct a data set; and finally, performing alternative optimization on the super-network weight and the sub-network codes, stopping optimization until constraint conditions are met, obtaining optimal sub-network codes, and further obtaining the architecture of the neural network. According to the method, an iterative denoising mechanism of a diffusion model and a global search strategy of an evolutionary algorithm are creatively fused, a super network is constructed by combining the denoising mechanism of the diffusion model and the global search capability of the evolutionary algorithm, and network weight and subnetwork coding are alternately optimized; through adaptive noise scheduling and density estimation, population diversity is enhanced, local optimum is avoided, and an optimal neural network architecture suitable for a task can be better found.

The invention discloses a wind power prediction method and system based on an improved sparrow algorithm, and relates to the technical field of wind power generation, and the method comprises the steps: collecting and preprocessing the historical operation data of a wind power plant, and constructing a wind power prediction model; the method comprises the following steps: introducing Tent chaotic mapping into a traditional sparrow search algorithm, and introducing adaptive parameters into the Tent chaotic mapping to obtain a preliminary improved sparrow search algorithm; integrating Gaussian mutation operation to obtain a final improved sparrow search algorithm; and using the final improved sparrow search algorithm to optimize the wind power prediction model, and using the optimized wind power prediction model to predict the wind power. According to the invention, through multi-level algorithm improvement and optimization strategies, the problem that a traditional sparrow search algorithm is easy to fall into local optimum is successfully solved; by introducing Tent chaotic mapping and adaptive parameters, the initial population position is optimized, and the global search capability is improved; gaussian mutation operation is integrated, population diversity is maintained, and the capability of jumping out of local optimum is enhanced.

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 ore collection device multi-parameter optimization design method based on deep sea tuberculosis grading, and the method employs a discrete genetic algorithm to carry out the overall optimization of a plurality of design parameters of an ore collection device, and improves the collection efficiency. An elite retention strategy and a uniform two-point crossover method are introduced, several individuals with the highest fitness are selected as elite individuals, two points are randomly selected from gene strings of two parent individuals according to the uniform two-point crossover method, and then gene segments between the two points are exchanged to generate new individuals, so that exploration of new parameter combinations is facilitated, population diversity is increased, and the method is suitable for large-scale popularization and application. Therefore, a better design solution can be found; according to the method, a maximum likelihood method is adopted to estimate parameters of marginal probability density functions of the size and mass of the nodule, and a size-mass joint probability distribution function of nodule particles is constructed, so that the size and mass distribution of the nodule can be more accurately fitted, and a more real seabed environment model is created for the subsequent design of a mineral collection device; and relatively real seabed tuberculosis particle distribution is provided.

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 provides a beer production scheduling method under an uncertain condition aiming at the scheduling problem in beer production. According to the method, through an iterative optimization strategy, a target function is adjusted step by step, an alternating mechanism is introduced, an original target is used as a guide in the initial stage of optimization, an evaluation function is alternately adopted in the later stage, exploration and development are balanced, and a trap of a local optimal solution is avoided. According to the algorithm, a non-dominated sorting genetic algorithm (NSGA-II) is adopted, population diversity is maintained through selection, crossover and mutation operation, a Pareto frontier solution set is generated through a non-dominated sorting mechanism, and the multi-objective optimization problem is effectively solved. Finally, the algorithm provides a series of Pareto optimal solutions, supports decision makers to perform tradeoff selection among productivity, inventory and other optimization objectives, and significantly improves the flexibility and efficiency of beer production scheduling.

The invention discloses a slope stability prediction method and device. The method comprises the steps of obtaining training data and establishing a PPO-PSO-BP prediction model for predicting slope stability; training data are input into the PPO-PSO-BP prediction model, training optimization is carried out on particle swarm parameters according to a strategy network and a value network to obtain an optimal strategy so as to determine optimal particles, the particle swarm parameters specifically comprise an inertia weight # imgabs0 #, an individual cognitive learning factor # imgabs1 # and a social cognitive learning factor # imgabs2 #, and the particle swarm parameters specifically comprise an inertia weight # imgabs0 #, an individual cognitive learning factor # imgabs1 # and a social cognitive learning factor # imgabs2 #. The optimal particle is specifically an optimal weight parameter and an optimal offset parameter of a BP network in the PPO-PSO-BP prediction model; and inputting the slope data at the current moment into the PPO-PSO-BP prediction model after the parameters are determined to obtain the slope stability at the prediction moment, so that the problems of poor population diversity, difficulty in convergence and falling into a local extreme value are avoided, and the prediction precision and prediction stability of the slope stability can be effectively improved.

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 relates to the technical field of intelligent network connection vehicle cluster decision making, in particular to an intelligent network connection vehicle cluster macro-micro integrated alliance decision making method, which constructs a cluster alliance decision making cost function by constructing an initial path of an intelligent network connection vehicle cluster and considering safety, comfort, efficiency and energy consumption targets. Under the condition that kinematics constraint and comfort conditions of the intelligent network connection automobile are met, an adaptive coevolution algorithm is used for solving, a diversity-based search distribution mechanism is adopted in the adaptive coevolution algorithm for population diversity distribution and traffic flow guide variation, a global exploration module and a local optimization module are optimized respectively, and a global optimization module and a local optimization module are optimized respectively. And operations such as periodic global optimization are carried out to generate an optimal intelligent network connection vehicle cluster decision, so that the vehicle can reduce energy consumption and improve comfort on the premise of ensuring the safety to the maximum extent and improving the efficiency.

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 relates to the field of data processing, in particular to a high-throughput reaction screening system and method based on computer control and data processing. The method comprises the following steps: executing a parallel chemical reaction experiment, obtaining experiment parameters and experiment reaction results, and carrying out standardization processing to obtain standardized parameter values and standardized experiment reaction results; calculating the relative weight of the experimental parameters by analyzing the correlation between the experimental parameters and the experimental reaction results; and based on the relative weights of the standardized parameter values and the experimental parameters, selecting an optimal parameter vector through a high-throughput reaction parameter screening algorithm for guiding a subsequent experiment. The problems that in a chemical reaction screening method, experimental parameter importance is difficult to quantify, fitness is evaluated only based on a reaction result, and a suboptimal solution may be mistakenly selected are solved; in the optimization process, the population diversity is possibly reduced due to insufficient variation, or the optimal solution cannot be converged due to excessive variation, so that the optimization effect of the chemical experiment condition is influenced.