28 results about "Premature convergence" patented technology

According to the large power grid reactive power optimization method and device, the storage medium and the computer equipment provided by the invention, the advantages of the two algorithms are fully exerted through the hybrid chaos quantum particle swarm optimization algorithm and the dimension-by-dimension convex space search algorithm. According to the chaotic quantum particle swarm algorithm, the global search capability and the capability of jumping out of local optimum of a particle swarm are enhanced by utilizing the characteristics of quantum behaviors and chaotic mapping, and the problem of premature convergence of a traditional heuristic intelligent algorithm is avoided. And according to the dimension-by-dimension convex space search algorithm, fine search is carried out on each excellent particle in different dimensions, a local optimal solution is determined, and the search precision and efficiency are further improved. According to the design of the hybrid algorithm, special optimization is carried out aiming at the characteristics of a reactive power optimization problem model, such as variable property difference, constraint complexity and the like, and the technical defects of poor optimization effect and optimization efficiency of an optimization solution algorithm in the prior art are effectively overcome.

The invention relates to a factory job shop scheduling control optimization method and device, equipment and a medium, and the method comprises the steps: classifying workpieces irrelevant to all MPBPO sets into a first workpiece set, classifying the workpieces corresponding to each MPBPO set into one set to determine a second workpiece set, independently generating job task sorting sub-chromosome segments, and generating a job task sorting sub-chromosome segment; corresponding operation machine numbers and operation time are respectively filled into corresponding gene positions of the operation machine sub-chromosomes and the operation time sub-chromosomes, and the operation task sorting sub-chromosomes, the operation machine sub-chromosomes and the operation time sub-chromosomes are combined to obtain initial chromosomes so as to generate initial population individuals; genetic manipulation is carried out on the initial population individuals, and population individuals with the high fitness and the number of the population individuals being the population scale are selected as a next-generation initial population; and repeatedly executing, and outputting the production scheduling scheme with the optimal earliest completion time. According to the method, algorithm premature convergence can be avoided in a generalized job-shop scheduling problem with a forced parallel batch processing procedure.

The invention discloses a ship plane segmentation workshop fuzzy scheduling method, which comprises the following steps of: firstly, establishing a ship plane segmentation fuzzy scheduling model, adding uncertainty of processing time to the traditional scheduling model, and representing the processing time by using a triangular fuzzy number; secondly, improving the Harlisia eagle algorithm, introducing nonlinear decreasing escape energy and simulated annealing temperature parameters, solving the problems of premature convergence and falling into a local optimal solution of the algorithm, and improving the quality of an initial population by adopting an improved NEH algorithm and chaotic mapping; and finally verifying the validity of the algorithm through a reference function, and applying the optimization method to an engineering example. The method is helpful for improving the efficiency of a ship plane section processing workshop, and has important practical significance.

The invention relates to the technical field of alfalfa screening, discloses a high-quality and high-yield alfalfa hybrid combination screening method fusing multiple technologies, provides a multi-dimensional coding and double-elite evolution mechanism, constructs an improved non-dominated sorting genetic algorithm-II, and provides a high-quality and high-yield alfalfa hybrid combination screening method by coding a hybrid combination into a continuous real number vector. A chromosome expression mode decoupled from a problem structure is constructed, a double-elite mechanism of elite retention and offset mating is introduced, the retention and heredity ability of a high-quality solution is improved, offset crossover and jitter operation is used, premature convergence is avoided, efficient global search of a strain combination space is achieved, and a high-quality solution is obtained. Meanwhile, a multi-omics association model of genes, metabolites and phenotypes is provided, transcriptome, metabolome and molecular marker data are comprehensively utilized, a multi-dimensional character prediction model is constructed, early-stage accurate screening of filial generations is achieved through correlation analysis and marker verification, the error selection rate is reduced, and the breeding period is shortened.

The invention discloses a photovoltaic MPPT (Maximum Power Point Tracking) control algorithm, which relates to the technical field of photovoltaic control algorithms, utilizes the characteristics of simple structure, high convergence speed, high global optimization capability, wide applicability and the like of a gradient optimization algorithm, and improves the defects of premature convergence and sensitivity to control parameters of the gradient optimization algorithm. Meanwhile, the photovoltaic output energy efficiency and the stability of the photovoltaic output energy efficiency serve as the evaluation basis, an MPPT algorithm performance evaluation model is constructed through an analytic hierarchy process, photovoltaic MPPT performance is evaluated, and GTO performance is verified and improved.

The invention provides a multi-AGV scheduling and path planning joint optimization method applied to intelligent warehousing, and the method comprises the steps: building a multi-AGV scheduling and path planning joint optimization model, and enabling the optimization target of the multi-AGV scheduling and path planning joint optimization model to be the minimum maximum operation completion time; and solving the proposed joint optimization model based on a biased random key genetic algorithm (BRKGA) to obtain a population optimal solution and corresponding maximum operation completion time of all AGVs. According to the method, a conflict-free path planning algorithm is adopted as a decoding scheme of population individuals in the biased random key genetic algorithm, so that a path planning scheme corresponding to a scheduling scheme represented by the individuals is obtained, and meanwhile, a local search strategy and a restart mechanism are added in the biased random key genetic algorithm; while the solving accuracy and convergence speed of the algorithm are improved, premature convergence of the population is prevented, so that a better result is obtained.

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

The invention discloses a determination method and device for a high-entropy alloy yield strength prediction model, equipment, a medium and a product. The method comprises the steps that an original feature sample set and an initial prediction model of a high-entropy alloy are obtained; screening the original feature sample set to determine an optimal feature subset; performing global search on hyper-parameters of the initial prediction model through a whale optimization algorithm to determine optimal hyper-parameters; and inputting the optimal feature subset and the optimal hyper-parameter into the initial prediction model for training, and determining a final prediction model. Firstly, key feature selection is carried out on an original feature sample set, an optimal feature subset is determined, global and local collaborative search is carried out on model hyper-parameters through a whale optimization algorithm, premature convergence is effectively avoided by utilizing an adaptive step length and position updating strategy, then model training is carried out, and a final prediction model is obtained. The calculation resource consumption in the model training and verification process is reduced, and the convergence speed and accuracy of yield strength prediction are improved.

This invention discloses a meme island optimization method and related apparatus for complementary sequence sets, belonging to the field of complementary sequence design. The method includes: constructing multiple island subpopulations that evolve in parallel; initializing a global archive for recording the history of search space region visits; performing population evolution in parallel on multiple islands; evaluating each candidate complementary sequence set according to a fitness function that incorporates region guidance; exchanging elite individuals among multiple islands when a preset migration condition is met; and outputting the optimal complementary sequence set found among all islands when an iteration condition is satisfied. By introducing region guidance based on the global archive into the fitness function, the search is actively guided to unexplored regions, systematically avoiding the search process from stagnating in the same basin, avoiding getting trapped in local optima, significantly improving global exploration capability, and reducing the risk of premature convergence.

The invention relates to the technical field of power system scheduling, and relates to a power system scheduling method based on particle swarm optimization, comprising the following steps: S1, establishing a scheduling model, and obtaining a total objective function; s2, constructing a power system constraint condition; s3, constructing an original artificial intelligence algorithm, initializing a population scale, and determining an iteration tolerance N1 and a local optimal tolerance N2; s4, performing population iteration until the number of iterations reaches N1, and recording the fitness of population particles; s5, screening local optimal particles, performing population iteration by taking the local optimal particles as parent particles, and querying population particle fitness in N2 iteration processes; s6, effective local optimal particles are determined, invalid local optimal particles are removed, and a population updating result is obtained; and S7, performing iteration by taking a population updating result as a parent particle, and outputting an optimal scheduling scheme. According to the method, premature convergence is avoided, the global search capability of the algorithm is remarkably enhanced, and the problem of easy local optimum in complex scheduling is solved.

This invention discloses a container scheduling method and system based on entropy weighting and multi-strategy particle swarm optimization. The method includes: constructing a multi-objective container scheduling model with the objective function of minimizing the data exchange rate between different servers and the average load imbalance of servers; acquiring server set, container set, and historical scheduling instance data, and constructing an objective priority matrix based on the multi-objective container scheduling model to determine the weights of each objective function; transforming the multi-objective container scheduling model into a single objective using a linear weighting method, and iteratively solving the single-objective container scheduling model using the multi-strategy particle swarm optimization algorithm to obtain the final optimal container scheduling solution. This solves the objective balance problem and the problems of premature convergence and insufficient population diversity in the basic particle swarm optimization algorithm, resulting in a more balanced server load and lower network communication load, achieving the optimal balance effect.

The invention discloses an NSGA-II (Non-dominated Sorting Genetic Algorithm-II) algorithm and a forest coverage type classification method. The algorithm comprises the steps of population initialization, non-dominated sorting, fitness evaluation, tournament selection, crossover and mutation operation, elitist retention and termination condition judgment. According to the invention, based on the improved NSGA-II algorithm, simplified congestion distance calculation is adopted, so that the required convergence speed is improved by 30-40%, the calculation time is reduced, and meanwhile, through cooperation of the adaptive crossover probability and the mutation probability, dynamic adjustment can be carried out according to the population evolution state, global exploration and local development are effectively balanced, premature convergence is avoided, and the convergence efficiency is improved. And the precision of outputting the optimal solution is improved.

The invention belongs to the technical field of artificial intelligence, and provides an arc model parameter optimization method based on an improved Asian wolf optimization algorithm, and the method comprises the steps: collecting an arc current signal based on a current sensor, and extracting the spectrum capability waveform of the arc current signal; establishing a direct-current arc noise model according to the arc current signal spectrum capability waveform; constructing an objective function based on the root-mean-square error, and determining a to-be-optimized parameter; and executing the improved Asian wolf optimization algorithm, and optimizing the to-be-optimized parameters. According to the method, the Asian wolf optimization algorithm is introduced through the chaos self-scaling learning mechanism, adaptive interaction of population individual position information is achieved through the chaos self-scaling learning mechanism, the optimization algorithm can be effectively prevented from being caught in premature convergence, the convergence speed is increased, and the method has high recognition precision for parameters of the direct-current arc noise model.

A computational architecture employing self-pruning fractal branch management for achieving supercomputer-class performance on standard hardware and noisy intermediate-scale quantum (NISQ) devices. Unlike conventional parallel computing systems requiring massive hardware resources or genetic algorithms requiring extensive population evolution, this invention utilizes hierarchical fractal doubles—modular computational units organized in self-similar tree structures—with real-time adaptive pruning eliminating non-promising solution branches based on geometric performance metrics computed via √2-scaled fractal analysis. Controlled perturbations (branch shaking) inject stochastic exploration preventing premature convergence while pruning maintains computational efficiency. The system achieves quantum-competitive performance on classical hardware through fractal interference patterns mimicking quantum superposition, and enables NISQ quantum computers to operate effectively despite hardware noise by pruning decoherence-corrupted branches before they contaminate computation. Core innovation: geometric pruning criterion comparing branch trajectory fractal dimension against optimal threshold, triggering instant elimination of branches exhibiting non-productive exploration patterns. Applications include neural architecture search, protein folding simulation, quantum system modeling, combinatorial optimization, and multi-agent coordination—all achieving 10-100× speedup versus conventional approaches while consuming 60-80% less energy through aggressive branch elimination. Technical advantages: (1) no training dataset required (deterministic pruning), (2) hardware-agnostic (runs on CPU / GPU / QPU), (3) noise-tolerant (quantum error mitigation via pruning), (4) energy-efficient (eliminates wasted computation), (5) scalable (fractal recursion to arbitrary depth).

A general assembly project scheduling optimization method based on importance and a resource unavailable time window comprises the following steps: analyzing topological characteristics and information transfer capability of a process in a complex equipment general assembly network through a process importance evaluation method, and after the process importance is obtained, generating a global approximate optimal scheduling scheme according to a hybrid genetic-variable neighborhood search algorithm. According to the method, through an optimization module for process importance quantification, global search of a hybrid genetic algorithm and local search of an improved variable neighborhood search algorithm, heuristic information mining and scheduling optimization of a complex equipment general assembly system are integrated, so that the algorithm search efficiency is remarkably improved, and meanwhile, premature convergence to a local optimal solution is avoided; and a new thought is provided for solving a production scheduling optimization problem in a complex production environment.

The invention discloses a code development system and method with self-debugging capability, and relates to the technical field of artificial intelligence and software engineering. The system comprises a code defect acquisition module, a self-debugging model module, a verifiable evaluation module and a strategy updating module. The self-debugging model module generates a candidate code repair scheme based on a large language model; and the strategy updating module adopts an adaptive entropy guide reinforcement learning method, and dynamically adjusts a dominant function for training by calculating the variable quantity of the model strategy entropy so as to excite the model to realize intelligent balance between exploration of a new repair path and utilization of a known effective method. The corresponding method performs model training and code repair based on the system. According to the method, the problems that an existing code debugging method based on reinforcement learning is insufficient in exploration capability and a repair strategy is converged too early are solved, and the diversity and robustness of a model generation repair scheme can be improved.

The application discloses a heterogeneous multi-core processor task scheduling method, system, device and medium, mainly relates to the technical field of task scheduling, and is used to solve the problems that the traditional scheduling algorithm cannot process the task dependency and communication overhead of the heterogeneous multi-core processor, the heuristic algorithm is easy to fall into local optimization, and the basic sparrow search algorithm has the problem of premature convergence in task scheduling. The application comprises the following steps: taking the task scheduling sequence related to the DAG task scheduling graph, the communication frequency sum corresponding to the task scheduling sequence, and the cross-core communication overhead sum as the input data of the sparrow search algorithm; updating the discoverer in the sparrow population through a random fractal search mechanism, and updating the joiner in the sparrow population through a topology adaptive mechanism; meanwhile, taking the minimum scheduling length calculation function as the objective function, and configuring the constraint condition; when the preset stop iteration condition is reached, the optimal task scheduling sequence meeting the objective function and the constraint condition is output.

The application discloses a code development system and method with self-debugging capability, and relates to the technical field of artificial intelligence and software engineering. The system comprises a code defect acquisition module, a self-debugging model module, a verifiable evaluation module and a strategy updating module. The self-debuging model module generates a candidate code repair scheme based on a large language model. The strategy updating module adopts an adaptive entropy-guided reinforcement learning method, calculates the change amount of the model strategy entropy, dynamically adjusts the advantage function used for training, and stimulates the model to intelligently balance between exploring new repair paths and utilizing known effective methods. The corresponding method performs model training and code repair based on the system. The application solves the problems of insufficient exploration capability and premature convergence of repair strategies in the existing code debugging method based on reinforcement learning, and can improve the diversity and robustness of the repair scheme generated by the model.

The application discloses a tunnel smooth blasting parameter selection method, aiming at the problem of tunnel overbreak and underbreak, constructs a blasting parameter optimization model by taking the minimization of overbreak and underbreak as an objective function, and taking the tunnel section size, lithology grade and geological condition as constraint conditions, and uses an improved genetic algorithm with dynamic constraints, hierarchical coding and multi-stage fitness evaluation to screen out the optimized blasting parameters under the minimum overbreak and underbreak through continuous selection, crossover and mutation operations. Compared with the traditional genetic algorithm, the application improves the global convergence and parameter practicability, and avoids the problems of premature convergence and invalid solution of the traditional genetic algorithm.

The invention specifically relates to a semiconductor wafer manufacturing system scheduling method based on an adaptive genetic algorithm, and the method comprises the steps: building a time Petri net model according to the technological process and technological time characteristics of a semiconductor wafer manufacturing system, and determining an initial identifier, a target identifier and a transition trigger logic of the time Petri net model; initializing parameters of the genetic algorithm and an initial population; and the genetic algorithm is subjected to iteration of strategies such as crossover, variation and selection to finally obtain a scheduling result of the semiconductor wafer manufacturing system. In the solving process, in order to enhance exploration and avoid premature convergence, three adaptive strategies of adaptively adjusting crossover probability according to fitness bodies of parent individuals, adaptively adjusting mutation probability according to population diversity and selecting operators are provided, and global search and local development capabilities of the algorithm are balanced.

This invention discloses a chaotic quantum-enhanced Snow Goose algorithm for complex optimization problems. This algorithm combines a chaotic mapping mechanism with a quantum rotation gate strategy, aiming to achieve a more optimized trade-off between the exploration and development phases. First, a nonlinear chaotic system is introduced, utilizing its inherent randomness and ergodicity to generate dynamic perturbations, thereby enhancing population diversity in the search phase and preventing premature convergence. Second, based on the quantum superposition principle, a quantum rotation gate operation is used. Leveraging the characteristics of quantum superposition states, a strategy is designed to periodically update individuals, guiding the population towards the optimal region for precise development, significantly improving convergence accuracy. Although the quantum rotation gate primarily performs local fine-tuning of individuals, it guides them to the most globally recognized positions, indirectly promoting global search. The chaotic quantum-enhanced Snow Goose algorithm effectively optimizes performance by embedding these two strategies into the main loop of the SGA and executing them in alternating periodic phases.

This application relates to a multi-objective flexible job shop scheduling method. It establishes a mathematical model for multi-objective flexible job shop scheduling with the objectives of maximum completion time, total machine load, and maximum machine load. A two-segment encoding method, including machine code and operation code, is used to represent the scheduling scheme. The method leverages the local and global search capabilities of a nonlinear convergence factor balancing algorithm. Furthermore, two neighborhood structures based on key processes are proposed to improve solution quality. Finally, an improved whale algorithm combined with a multi-objective framework is used to solve the multi-objective flexible job shop problem model. The method in this application can better balance efficiency and solution accuracy, and avoid premature convergence and getting trapped in local optima.

The invention discloses a complementary sequence set-oriented memetic island optimization method and a related device, and belongs to the field of complementary sequence design, and the method comprises the steps: constructing a plurality of island sub-populations evolved in parallel, and initializing a global file used for recording the access history of a search space region; executing population evolution in parallel on a plurality of islands, and evaluating each candidate complementary sequence set according to a fitness function fused with region guidance; when a preset migration condition is met, exchanging elite individuals among the plurality of islands; and when an iteration condition is satisfied, outputting an optimal complementary sequence set found in all islands. According to the method, region guidance based on global archives is introduced into a fitness function, search is actively guided to an undeveloped region, the situation that the search process stops in the same basin is systematically avoided, local optimum is avoided, the global exploration ability is remarkably improved, and the premature convergence risk is reduced.

The invention discloses a network-on-chip mapping method based on an improved hiking optimization algorithm, and belongs to the field of integrated circuit design. In order to solve the problems of premature convergence, low search efficiency and the like of an existing mapping algorithm, the invention provides a new mechanism which comprises the following steps: firstly, performing population initialization by adopting Logistic chaotic mapping to enhance population diversity and ergodicity; secondly, constructing a GA-HOA coevolution framework, designing an adaptive probability crossover and multi-strategy mutation operator, and realizing dynamic balance between global exploration and local development; and finally, a 2-opt local search mechanism is introduced, convergence is accelerated, and the solution quality is improved. Experiments show that the method is superior to a traditional algorithm in key indexes such as communication cost, calculation time, energy consumption and delay, and is particularly suitable for application mapping optimization of a large-scale network-on-chip system.

A mobile edge computing offloading scheduling method based on predator swarm intelligence evolution. The method is a model strategy that optimizes time delay, energy consumption and cost together, which is closer to the cost consideration in the real scene. Chaos mapping is used to increase population diversity during population initialization, and then the marine predator algorithm combined with the differential evolution algorithm completes the scheduling of task offloading. The mutation advantage of the differential evolution algorithm is used to jump out of local convergence. Compared with other heuristic algorithms, the offloading scheduling method given here can effectively reduce the time delay and power consumption of edge computing offloading. The test results also show that MPA-DE can effectively reduce premature convergence and has good global search ability, and can efficiently handle more dimensional complex NP-complete problems.

This invention discloses a method and storage medium for workflow data layout in a cloud-edge environment. The cloud-edge environment is mathematically represented, and based on replica generation and data transmission overhead, the data layout problem is modeled as a 0-1 integer programming problem with the goal of minimizing total latency, resulting in a mathematical problem model. A nonlinear inertial weighted discrete particle swarm optimization algorithm based on genetic algorithm operators is employed. The crossover and mutation operators of the genetic algorithm are introduced into the particle swarm algorithm, and the inertial weights are adaptively adjusted according to the differences between the current particle and the global particle to solve the mathematical problem model. Workflow data layout is performed based on the solution results. This effectively reduces latency. Furthermore, the introduction of crossover and mutation operators of the genetic algorithm into the particle swarm algorithm enhances the search capability of the particle swarm algorithm, avoids premature convergence, and the adaptive adjustment of the inertial weights according to the differences between the current particle and the global particle makes the optimization process more efficient.

The invention discloses a low-carbon high-performance paving material proportion intelligent design system and method, and particularly relates to the technical field of computer aided design and intelligent optimization algorithms. The method is used for solving the problems that when an existing intelligent optimization method is used for processing multi-source and strong coupling constraints, due to design space fragmentation, local optimization is likely to be caused, and the global optimization capacity is insufficient. The method comprises the following steps: analyzing the violation state of an initial population to constraints, identifying a constraint coupling group causing space fragmentation based on violation co-occurrence, dividing and identifying sub-populations corresponding to different fragment areas according to the constraint coupling group, and calculating the coding entropy of each sub-population and the average constraint boundary closeness to evaluate the evolution potential of the sub-population. According to the method, differentiated optimization resources are allocated, independent optimization calculation is carried out, meanwhile, the evolution trajectory convergence situation of each sub-population is monitored, and when it is judged that the sub-populations tend to a close area and premature convergence exists, cross-sub-population matching scheme directional migration is executed based on the situation, so that population implementation is updated.

The application discloses a kind of multi-crane collaborative transport under mixed flow workshop self-adapting scheduling method, system, equipment and medium, relating to ship pipe production scheduling and intelligent optimization technical field, its specific steps are as follows: determine the mixed flow workshop scheduling object and scheduling boundary under crane collaborative transport constraint, establish production scheduling mathematical model, iterative search and optimization are carried out in the feasible solution space defined in the production scheduling mathematical model using adaptive learning evolutionary algorithm, and the optimal scheduling scheme is output.The application is based on fully depicting the coupling relationship between processing process and crane collaborative transport process in mixed flow workshop, introduces adaptive learning to intelligently guide the search process in evolutionary algorithm, so that the algorithm can dynamically adjust the search strategy according to the population evolution state and search feedback information, thereby overcoming the problem of search efficiency decline and premature convergence of traditional evolutionary algorithm under complex constraint conditions.