379 results about "Crossover" patented technology

A k-parallel row sorting problem solving method considering multiple channels relates to the technical field of disassembly line layout, and mainly comprises the following steps: determining an objective function, calculating channel coordinate information in a region, generating a population P1 and randomly generating a population P2 by using a greedy strategy, combining the population P1 and the population P2 into an initial population P, calculating the fitness of all solutions in the initial population, and obtaining a k-parallel row sorting problem; screening an elite solution, selecting solutions except the elite solution in the initial population based on the fitness by using a roulette mechanism to execute genetic circulation, sequentially carrying out crossover and mutation operation based on Q-learning, combining the population with the elite solution after mutation with the population P1 and the population P2 to update the population, according to the maximum number of iterations, it is judged that iterative calculation continues or a result is output; according to the variable domain genetic algorithm based on Q-learning, an optimal solution for solving kPROPP can be provided in a short time, the solving efficiency of the variable domain genetic algorithm is greatly superior to that of a conventional accurate solver, the solving effect of the variable domain genetic algorithm is superior to that of other methods, and reliable support is provided for solving the problem of parallel layout planning.

The invention relates to a visual-language model cue word evolution generation method based on a genetic algorithm, which comprises the following steps of: randomly generating different cue words under a target task to construct an initial cue word set, preprocessing the randomly generated cue words, and reserving N groups of cue words as an initial population; and designing a multi-dimensional fitness evaluation function to evaluate the performance quality of each group of cue words in a vision-language task, and selecting high-quality cue word individuals based on an elitism strategy. Performing crossover and mutation operation on the selected high-quality cue word individuals by referring to a genetic algorithm to generate a new-generation cue word population; and finally, carrying out iterative optimization for multiple times until a preset termination condition is met, and outputting an optimal cue word set. By the adoption of the visual-language model cue word evolution generation method based on the genetic algorithm, high-quality and diversified cue words can be automatically generated, the performance of a visual-language model is remarkably improved, and meanwhile the visual-language model cue word evolution generation method has good interpretability and adaptability.

System and method for hybrid analysis of quantum and classical genetic algorithms is disclosed. The method includes, receiving an input bitstring, the input bitstring being an output of a genetic optimization module, processing the input bitstring to generate quantum processed bitstrings, mutating the input bitstring, mutating the quantum processed bitstrings as a function of the input bitstring and the mutated input bitstring, performing crossover on a combination of the mutated input bitstring and the mutated quantum processed bitstrings, and selecting a set of individuals from the quantum processed bit strings and output of the crossover. The method further includes, determining, after selecting the set of individuals, if the hybrid analysis is complete or incomplete based on predetermined criteria, returning, in response to the hybrid analysis being incomplete, the set of individuals as the input bit string, else, outputting the set of individuals as a result of the hybrid analysis.

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 belongs to the technical field of petroleum and natural gas drilling engineering, and particularly relates to a borehole friction coefficient segmented inversion method based on a genetic algorithm and string mechanics, which comprises the following steps of: acquiring specified data of a construction well, dividing a borehole of the construction well to be predicted into well sections with fixed lengths, defining depth ranges of the well sections, and setting a global search range of a friction coefficient; initializing the population size, the maximum number of iterations, the selection probability, the crossover probability and the mutation probability of the genetic algorithm, and performing downward inversion section by section from a wellhead section: initializing a candidate friction coefficient population of a current well section, setting a sectional friction coefficient, calculating a hook load at the tail of the current section, and calculating the maximum number of iterations; iterative optimization is carried out until a termination condition is met, and then the optimal friction coefficient of the section is output and fixed; and iteratively executing until inversion of all well sections is completed, outputting a full-borehole segmented friction coefficient profile, and realizing intelligent segmented inversion of the borehole friction coefficient by combining a tubular column mechanical model and a genetic algorithm, so that the prediction precision of the friction coefficient is improved, and the well drilling design and construction are optimized.

The air freight loading efficiency and the flight safety are crucial to the efficient operation of a transportation system. Therefore, aiming at the problem of multi-cargo loading optimization under the complicated constraint condition of the cargo hold, a multi-objective optimization model containing business load limitation, gravity center shift, volume utilization rate and rotational inertia is constructed, and a solution method based on a grey wolf genetic hybrid algorithm is provided. A high-quality initial population is generated by adopting a two-stage initialization strategy, and evaluation is performed through a fitness function. And dynamically adjusting a step length coefficient and a mutation probability to realize self-adaptive adjustment of the search capability. In the selection stage, excellent individuals are reserved by using a tournament strategy, and meanwhile, the population quality is improved through an alpha wolf direct inheritance mechanism. In the breeding stage, the feasibility and diversity of solutions are enhanced through single-point crossing and mutation operation based on the gravity center distance. And after iterative updating, outputting the scheme with the highest evaluation value as the optimal loading scheme. According to the algorithm, the loading balance and the calculation efficiency are improved, and the high loading rate of the space in the cabin is ensured.

The invention discloses a tunnel smooth blasting parameter selection method, which aims at the tunnel back-break and back-break problem and comprises the following steps of: constructing a blasting parameter optimization model by determining the back-break and back-break amount minimization as a target function and taking the tunnel section size, the lithology grade and the geological condition as constraint conditions; an improved genetic algorithm which introduces dynamic constraint, hierarchical coding and multi-stage fitness evaluation is utilized, and optimized blasting parameters under the minimum back break amount are screened out through continuous selection, intersection and mutation operations. Compared with a traditional genetic algorithm, global convergence and parameter practicability are improved, and meanwhile the problems of premature convergence and invalid solutions of the traditional genetic algorithm are solved.

There is provided a system and methods to determine an improved solution to a Mixed Integer Problem (MIP) using a quantum-assisted MIP solver. The methods are performed by a digital processor in communication with a quantum processor. Methods include: selecting at least one feasible solution determined by an MIP solver, determining a first sub-problem of the MIP based on the at least one feasible solution; casting the first sub-problem as Binary Quadratic Models (BQMs); solving the BQMs using the quantum processor to generate sample solutions; determining a second sub-problem based on at least the sample solutions, and obtaining a current solution to the MIP by evaluating the second sub-problem; and updating an incumbent solution if the current solution improves over the current incumbent solution. The quantum-assisted MIP solver uses hybrid crossover and mutation heuristics to improve the convergence time and accuracy of solutions obtained using Branch-and-Cut solvers.

The invention discloses an electronic circuit energy efficiency optimization design method based on a genetic algorithm, and the method comprises the following steps: S1, carrying out the modeling of a to-be-optimized electronic circuit, forming a mixed type chromosome, and initializing a genetic algorithm population; s2, constructing a fitness function based on a genetic algorithm population; s3, executing a selection operation on the current population, and replacing a parent by a child with high adaptability; s4, performing crossover operation on the parent individuals to generate new offspring individuals; s5, performing mutation operation on the offspring individuals to form new individuals; s6, performing circuit simulation on the new individual after the genetic manipulation is executed, and inputting a simulation result into a fitness function for evaluation; s7, updating the population according to the fitness score, and entering next-generation evolution; and S8, configuring an individual parameter which meets a convergence condition and has the highest output fitness score. Based on a local competition algorithm, a double-layer block recombination strategy and a Pareto frontier algorithm are fused, and energy efficiency optimization of the electronic circuit is achieved.

The invention discloses a multi-objective continuous optimization method for a chemical reaction kettle. The method comprises the following steps: S1, initializing a population and a trainable parameter set; s2, calculating an individual target value and a gradient, and obtaining a performance steepest improvement direction after standardization; s3, constructing a conjugate direction in combination with historical gradients, and identifying sensitive key dimensions of the target function; s4, fusing the gradient and the conjugate direction to generate a main direction candidate solution, and applying refined disturbance to the sensitive dimension to generate a structural disturbance candidate solution; s5, updating the Pareto optimal solution set, and if a termination condition is met, outputting a multivariable optimization parameter set such as temperature-pressure-flow of the reaction kettle for actual operation; otherwise, returning to S2; according to the method, through gradient correction, conjugate direction fusion, sensitive dimension directional disturbance and parameter adaptive adjustment, the efficiency, precision and stability of multivariable optimization of the reaction kettle are remarkably improved.

The invention discloses a green robust independent parallel locomotive scheduling method with uncertain processing time. The method comprises the following steps: acquiring a to-be-scheduled parameter set; constructing an irrelevant parallel machine scheduling model taking worst scene completion time WC and scene average energy consumption MTEC as double targets based on the parameters; a scene-driven double-population discrete artificial bee colony algorithm is adopted for solving, and the method comprises the steps of population initialization, employed bee global search based on ternary championics and two-point crossing, division into two sub-populations according to MTEC, MN local search based on a mean value scene, WN local search based on a worst scene, LN observation bee self-adaptive neighborhood search based on Q-learning and scout bee disturbance. And finally, outputting a robust scheduling solution set with both robustness and low-carbon property according to a Pareto criterion. And a plurality of scheduling schemes considering robustness and energy consumption optimization are provided for decision makers.

The invention relates to the technical field of intelligent breeding and logistics optimization control, and solves the technical problems of high energy consumption, unstable efficiency, unbalanced distribution, lack of an intelligent scheduling mechanism and the like in feed conveying of a multi-storey pig farm. The method comprises the following steps: acquiring static parameters (physical characteristics of feed, physical attributes of a conveying system and a pig house structure) and dynamic parameters (real-time feeding requirements, equipment and material states and external environment factors); establishing a multi-objective optimization model of a collaborative optimization energy consumption model E (x), a time model T (x) and a conveying balance degree model U (x); solving by adopting a genetic algorithm with a special design crossover and mutation operator to obtain an optimal scheduling scheme; in the execution process, a closed-loop self-learning calibration mechanism is started, actual power is measured through a current sensor, and when the deviation between predicted energy consumption and actual energy consumption exceeds a preset threshold value, efficiency parameters in the energy consumption model are reversely corrected through a gradient descent method; the weight coefficients of the three models are dynamically adjusted according to the real-time electricity price and the inventory state. The system adopts a three-layer architecture of a perception and data acquisition layer, a decision and control core layer and an execution layer. According to the method, multi-target collaborative optimization and intelligent adaptive scheduling are realized, the total energy consumption is effectively reduced, the transmission time is shortened, the distribution balance degree is improved, and the energy consumption prediction accuracy is remarkably improved.

The invention discloses an ecological flow process multi-objective optimization method based on an NSGA-II algorithm, and relates to the technical field of water conservancy projects. Comprising the steps of defining discrete decision variables, setting an objective function, constructing constraint conditions and a mixed penalty function, and configuring algorithm parameters; generating an initial solution meeting the ecological flow constraint, and repairing the solution which does not meet the constraint after genetic manipulation to ensure the feasibility of the solution; performing non-dominated sorting on the population to divide Pareto frontier layers, calculating a congestion distance of a solution in each frontier layer, realizing genetic manipulation through tournament selection, binary crossover simulation and polynomial variation, and updating the population in combination with environment selection; and extracting non-dominated solutions, screening optimization schemes from the non-dominated solutions, and outputting monthly scheduling schemes and water transfer and ecological flow shortage statistics of each reservoir under each scheme. According to the invention, automatic distribution of the ecological base flow, the basic ecological flow and the target ecological flow in the interannual period is realized.

The invention discloses an underwater multi-target routing method based on an improved non-dominated sorting genetic algorithm. The method comprises the following steps: firstly, arranging sensor nodes in a target water area, networking, and encoding a feasible path from a source node to a target node by adopting a segmented structure; secondly, constructing a routing multi-target fitness function, and introducing a dynamic weighting function to form a comprehensive fitness index; and finally, grading the population by using an improved non-dominated sorting genetic algorithm, and exploring a better path through crossover and mutation operations. And designing a reference point elite selection strategy, improving the coverage of the solution set in the target space, and outputting an optimal routing path until the algorithm converges. According to the method, the dynamic balance among different optimization targets can be realized, and the link quality is considered while the energy consumption and the time delay are reduced, so that the network routing performance is improved.

The invention relates to a low-temperature high-strength steel lining polytetrafluoroethylene material based on a novel plasticizer and a preparation method of the low-temperature high-strength steel lining polytetrafluoroethylene material, and belongs to the field of anticorrosive materials. The method comprises the following steps: constructing a multi-dimensional associated training set by taking a historical plasticizer component ratio, tensile strength, elongation at break and temperature resistance indexes of a material and sizes and functional parameters of different products as input; by defining a multi-objective fitness function, synthesizing the material strength, low-temperature toughness and cost economy, utilizing the global search capability of genetic algorithm selection, crossover and mutation operation, and combining with a simulated annealing algorithm, the local optimization characteristic of collaborative iterative optimization is realized by dynamically adjusting the inferior solution accepting probability, and the optimal proportioning scheme of the plasticizer is gradually approached. According to the SA algorithm, global exploration and local development are balanced through a temperature attenuation mechanism, the GA premature convergence problem is effectively avoided, the robustness of a matching scheme is improved, and the efficiency and precision of material performance optimization are remarkably improved.

The invention discloses a multi-target multi-bar truss optimization method based on single-target sequence search assistance. The method comprises the following steps: preliminarily locking an angular solution region by using an angular solution search mechanism; introducing a crossover operator to communicate with a population history optimal individual to generate an excellent individual; a mutation operator is introduced, so that the population is more diversified; an adaptive strategy is introduced, so that the offspring population is more excellent; external archives are introduced to improve the error-tolerant rate during individual selection; the angular solution quality is improved by adopting an angular solution detection mechanism; using the obtained angular solution to guide a multi-target algorithm to carry out further diversity search; selecting a plurality of non-dominated solutions using a non-dominated sorting method and the congestion distance; and evaluating the result on a plurality of test problem sets. A two-stage search mode is adopted, in the first stage, targets are searched in sequence on the whole, and an angular solution and a non-dominated solution are obtained; in the second stage, the solution obtained in the first stage serves as an initial population, evolution is conducted through a multi-objective optimization algorithm, and therefore better convergence and non-dominated solution diversity are achieved.

The invention discloses a heterogeneous integration test method based on AI, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting heterogeneous integration operation data, and carrying out data cleaning, anomaly detection, feature extraction and standardized output of a standardized data set; generating a plurality of neural network frameworks by using random initialization and Bayesian optimization, and outputting an optimal neural network structure in combination with fitness evaluation; optimizing the neural structure network and outputting an AI test model by combining crossover variation, pruning optimization and a genetic algorithm; function testing, pressure testing and safety testing are carried out in a real environment, key performance indexes are calculated, and optimization suggestions are output in combination with test feedback data; and outputting an intelligent test model by using a model-independent meta learning and transfer learning method. Through combination of crossover variation, pruning optimization and a genetic algorithm, each network structure can meet different targets, and meanwhile, the test efficiency can be improved.

The invention relates to a water supply network multi-leakage node positioning method based on hydraulic model and WLS-GA fusion, and belongs to the technical field of water supply network leakage detection. According to the technical scheme, the method comprises the steps that firstly, a weighted least square method WLS is adopted to conduct single-leakage-point leakage amount estimation on all nodes of a pipe network, candidate leakage points with the high leakage possibility are screened out based on residual errors and a sensitivity matrix, then the candidate leakage points are combined, finally, an optimized combined solution serves as an initial population, and the leakage amount of all the nodes of the pipe network is estimated. The improved genetic algorithm GA is adopted to carry out multi-leakage-point accurate positioning, and the final multi-leakage-point position and leakage amount are determined through adaptive crossover probability and elitist retention strategy iterative optimization. The method has the beneficial effects that the search space of the genetic algorithm is reduced through weighted least square method pre-screening, the calculation efficiency is improved, the algorithm is prevented from falling into local optimum by improving iterative optimization of the adaptive cross strategy and the elitist retention strategy, and the convergence speed of the genetic algorithm is improved.

The invention provides an adaptive optimization method for process parameters of a process forming scheduling workshop, and the method comprises the following steps: carrying out the feature extraction and feature alignment of the multi-modal data of the process forming scheduling workshop, and obtaining the feature vector of each modal data; according to the contribution degree of each modal data to the target, calculating a modal weight, and carrying out weighted fusion on the feature vectors; the method comprises the following steps: respectively coding process parameters and processes into chromosomes, constructing a fitness function according to feature vectors after weighted fusion, calculating influence weights of the process parameters based on the sensitivity and modal contribution degree of the process parameters, and adaptively adjusting crossover probability and mutation probability of each parameter gene according to the influence weights. Solving an optimal process parameter and a process scheduling scheme; and inputting the optimal process parameters and the process scheduling scheme into a simulation system, comparing the optimal process parameters and the process scheduling scheme with an on-line actual measurement result, if an error between a predicted defect rate and an actual defect rate exceeds a threshold value, updating the step length of genetic search and the contribution degree of each mode and then carrying out iteration again, otherwise, issuing the optimal process parameters and the process scheduling scheme to a production line for execution.

The invention discloses a large model output data security detection method and system based on adversarial attacks. The method comprises the following steps: constructing and optimizing a strategy space containing a plurality of attack strategies, and grading and sorting the strategies to improve the attack efficiency; generating a single-strategy antagonism prompt by the attack model according to the optimized strategy space, and performing effectiveness evaluation and feedback correction on the prompt by the judgment model; inputting a prompt passing the evaluation into the target large model to obtain a response, and performing malicious degree scoring on the response by the judgment model; and if the single-strategy attack is not successful, introducing an optimization mechanism based on a genetic algorithm, generating a more complex multi-strategy antagonism prompt through strategy variation and crossover, and carrying out iterative attack until the target large model is successfully broken into the prison. According to the method, the security defects of the large model can be efficiently and comprehensively detected in a self-adaptive and multi-strategy attack mode.

The invention provides an energy-saving cloud manufacturing multi-target scheduling method and system for improving rate-driven heterogeneous aggregation, and the method comprises the steps: A, setting algorithm parameters, job attributes and machine constraints, generating a weight vector and a neighborhood, and randomly binding an initial aggregation method; b, generating an initial population, performing heuristic decoding, and initializing an ideal point and an external archive set; c, calculating a dynamic switching threshold value based on the current iteration progress; d, executing sequential crossover and swap mutation operators to generate offspring individuals, and performing heuristic decoding based on consistency increment evaluation; e, updating an ideal point and maintaining an external archive set; f, executing self-adaptive environment selection according to the dominating relation and the relative improvement rate, and updating a neighborhood solution and a bound aggregation method; and G, if the termination condition is not met, returning to the step D, otherwise, outputting a non-dominated scheduling scheme set. The method has the advantages that the convergence problem under the multi-target conflict is effectively solved through self-adaptive cooperation of heterogeneous strategies. According to the method, heuristic batch decoding and time sequence linkage are adopted, a heuristic decoding algorithm with cluster constraints is designed, through real-time calculation of idle increments and switching losses, deep fusion of cross-process and cross-region resources is achieved, the cooperation efficiency of the whole cloud manufacturing process is guaranteed, and the maximum completion time and the total manufacturing cost can be balanced on the premise that production constraints are guaranteed; and thus, a high-quality collaborative scheduling solution set is stably obtained.

The invention discloses a spacecraft form control method and system based on an evolutionary algorithm and reinforcement learning, and the method specifically comprises the steps: S1, building an initial population, and enabling individuals in the population to be spacecrafts of different forms, which are different functional modules and different module number combinations; s2, performing inner ring initialization learning training on all individuals in the population, and calculating a fitness value of each individual; s3, selecting individuals with relatively high fitness values to form an elite population; s4, performing uniform crossover and single-point mutation on the elite population by using genetic and mutation operations in a genetic algorithm to generate elite offspring; s5, inner ring reinforcement learning: performing learning training on the elite offspring obtained in the step S4; s6, performing form evaluation; and S7, forming an optimal individual. According to the method, the characteristics of a space environment and task requirements are fully combined, an inner and outer ring algorithm architecture based on deep evolution reinforcement learning is adopted, and through continuous alternation of outer ring form evolution and inner ring learning training, form autonomous generation of the modular spacecraft is realized.

The invention discloses a technological parameter optimization method for electric pulse rock breaking. The method comprises the steps that electric tree characteristic parameters under different technological parameters are obtained; establishing a process parameter prediction model by taking the process parameters as input and the electrical tree characteristic parameters as output; based on a process parameter prediction model, an improved NSGA-2 multi-objective optimization algorithm is utilized to optimize process parameters, and the process comprises the following steps: providing a selection, intersection and variation process fusing a tabu search thought, namely, in a filial generation population generation process, a process of generating a filial generation population is provided; a solution with better fitness than a parent and a suboptimal solution within the number limit of flouting criteria serve as offspring solutions and are added into a taboo table, repeated acquisition is avoided, the local search ability of the algorithm is enhanced, a crowding degree value calculation method is improved based on the super-lattice thought to complete offspring population selection and construction, and the global search ability of the algorithm is improved. According to the method, the technological parameters are optimized through the improved NSGA-2 multi-objective optimization algorithm, the optimal technological parameter combination can be obtained, and the rock breaking efficiency is improved.

The invention discloses an underwater robotic fish full-coverage path planning method and system based on multi-strategy collaborative optimization, and belongs to the technical field of underwater robot path planning. Aiming at the problems of low path planning efficiency, poor dynamic environment adaptability and difficulty in algorithm parameter adjustment in the prior art, a dynamic grid coding and multi-resolution collaborative modeling method is provided, an obstacle probability region is divided through sonar point cloud data, the grid refining depth is dynamically adjusted in combination with a greedy optimization algorithm, and a multi-layer environment representation map is generated; an improved genetic algorithm framework is adopted, path fragments are recombined through self-adaptive variation rate adjustment and a three-point crossover operator, a conflict resolution mechanism is introduced to correct a countercurrent path direction, and a motion trail is optimized in combination with path smoothness constraint. According to the invention, high-efficiency and low-energy-consumption full-coverage path planning can be realized in a complex underwater environment, and the method is suitable for scenes of ocean exploration, pipeline inspection, ecological monitoring and the like.

The invention discloses an intelligent production scheduling method and system based on a fixed sequence genetic algorithm. The method comprises the following steps: dividing independent production areas of a workshop; key operation is determined, and modeling is carried out on time of the key operation; generating a process set containing a fixed sequence key job based on a process path, performing crossover variation by using an improved genetic algorithm, and keeping the sequence of internal processes of a task unchanged to avoid an invalid solution; and calculating the shortest processing duration through population iteration, and generating a production scheduling Gantt chart. The system comprises a workshop function area module, a key operation type module, a key operation modeling module and a production scheduling result output module, so as to realize the output of an intelligent production scheduling result. The method improves the production scheduling efficiency and the applicability of the production scheduling result, and is suitable for complex production scenes such as electronic assembly and cabinet debugging.

According to the low-power-consumption task distributing and scheduling method for the heterogeneous multi-core processing system, the minimum power consumption serves as the primary factor for optimizing the task distributing and scheduling scheme, the task processing efficiency and reliability of a heterogeneous multi-core processor are improved, and power consumption is reduced; a power consumption calculation function is fitted by using a Gaussian process model in Bayesian optimization, so that direct calculation of most multivariable nonlinear power consumption is avoided, and the complexity and calculation amount of an optimization algorithm are greatly reduced; the crossover mutation probability of the genetic algorithm is defined by using an adaptive function, so that the genetic algorithm can be dynamically adjusted according to the population characteristics of the existing scheduling scheme, and the method has considerable superiority in the field of task allocation and scheduling; the adaptive population updating strategy is used to balance exploration of a new scheduling scheme and maintenance of a scheduling scheme with low power consumption, population updating can be performed according to the characteristics of the current population scheduling scheme, and the efficiency of finding a globally optimal solution by the genetic algorithm is greatly improved.

The invention provides an unmanned aerial vehicle cluster weapon target collaborative allocation method and system under space-time constraint, and the method comprises the steps: defining a collaborative allocation object, constructing a collaborative combat scene target function, defining a collaborative combat scene constraint condition, and finally solving the target function through employing a multi-gene population parallel ant colony algorithm, encoding the weapon set, the target set and the unmanned aerial vehicle set into a weapon gene sequence, a target gene sequence and an unmanned aerial vehicle gene sequence respectively to generate an initial population, and performing staged joint optimization by fusing a pheromone guiding mechanism of an ant colony algorithm and crossover mutation operation of a genetic algorithm, the optimal cooperative allocation scheme is searched when the constraint condition is satisfied, the unmanned aerial vehicle task allocation result can be better obtained and optimized through the cooperative allocation mode, and the unmanned aerial vehicle cooperative allocation efficiency is improved.

The invention discloses a resource-constrained scene-oriented user interactive neural architecture search method, and relates to the technical field of neural architecture search. The method comprises the following steps: setting initial parameters; the method comprises the following steps: pre-defining a search space, coding the architecture of the search space, and generating an initial parent population containing different candidate architectures; on the basis of a current parent population, an offspring population is generated through crossover and mutation operation, then the performance of all individuals, namely candidate architectures, in the population is evaluated, a performance index penalty value and an optimization target are calculated, the population is updated through a two-stage hierarchical non-dominated sorting strategy, and therefore the performance of the individuals, namely the architectures is optimized step by step; based on the user interaction frequency m in the evolutionary search parameters, triggering user interaction once every m generations, adjusting preference weights according to user instructions, and guiding the next architecture optimization direction; and when the user interaction is not triggered, judging whether a preset iteration termination condition is met, and if the iteration termination condition is met, outputting the current Pareto frontier solution set for the user to screen the architecture and deploy.

The invention provides a multi-zone-area ordered charging scheduling method and system based on a simulated annealing genetic algorithm, and the method comprises the steps: generating an initial population through employing a real number coding mode, and enabling a population individual to represent a charging scheduling scheme; constructing a fitness function in the genetic algorithm by taking load balancing and network loss minimization as optimization targets; selecting individuals by adopting a roulette selection and elitism strategy, and reserving a plurality of individuals with the highest fitness to enter the next generation by adopting a self-adaptive crossover mutation operation; taking the optimal individual of the current population as an initial solution, and executing simulated annealing operation; after optimization of a preset number of iterations, an optimal charging scheduling scheme is output, and the load rate, the voltage and the charging pile state of each transformer area are monitored in real time; and when abnormality is detected, charging power distribution is dynamically adjusted, and the load of the overload area is transferred to the low-load area. Through fusion of simulated annealing and a genetic algorithm, multi-zone-area charging scheduling is optimized, load balancing and network loss minimization are realized, and the stability and dynamic adaptability of a power grid are improved.

The invention discloses a game optimization solving method for a multi-user power control problem in a wireless network, and the method comprises the following steps: building a multi-user power control game model, and arranging a joint strategy vector x according to the transmission power decision of a user; designing a fitness function F (x); selecting an elite individual with F (x) closest to 0 in the genetic algorithm population; and updating a local search radius by adopting a cosine annealing algorithm, performing optimization comparison on elite individuals in combination with a particle swarm optimization algorithm, forming a new generation of population in combination with new individuals obtained by selection, crossover and variation, and outputting an individual with F (x) closest to 0 after iteration, namely a Nash equilibrium solution as a final power control decision. According to the method, a game problem is converted into an optimization problem by designing a specific fitness function, so that the calculation complexity and the implementation threshold are remarkably reduced; and the method has low requirements on the property of a cost function, does not need to be continuous or differentiable, has relatively high robustness and universality, and can efficiently solve a continuous game problem.