46 results about "Crowding distance" patented technology

The invention relates to the technical field of multi-objective optimization, in particular to a multi-mechanism deep integrated multi-objective optimization framework and an optimization method and application thereof, comprising a reinforcement learning module, a genetic algorithm module, a bidirectional evolutionary learning coupling module, a Pareto experience pool module, a preference management module and a graph neural network module, the bidirectional evolutionary learning coupling module is used for realizing bidirectional information interaction between the reinforcement learning module and the genetic algorithm module, and the reinforcement learning module generates candidate solutions and feeds the candidate solutions back to the genetic algorithm module; the genetic algorithm module screens the elite solution with the crowding distance larger than a set threshold value in the first Pareto layer and updates the strategy of the feedback reinforcement learning module. According to the method, the strategy learning ability of reinforcement learning and the global population search ability of the genetic algorithm are fused, and the overall optimization efficiency and the approximation ability to the complex Pareto frontier are remarkably improved.

The invention discloses a sintering batching optimization scheme, and relates to the technical field of sintering ore batching control, and the technical key points are as follows: a random forest proxy model is used to predict the performance of finished ore, and a nonlinear mapping relationship between an independent variable and a dependent variable is used as a target function; carrying out Pareto optimal solution set search through non-dominated sorting and crowding distance calculation by adopting an NSGA-II (Non-dominated Sorting Genetic Algorithm-II) algorithm; calculating the weight of each target index by using an entropy weight method based on a Pareto solution set; weight is determined from a Pareto solution set generated by an NSGA-II algorithm, an optimal batching scheme closest to an ideal solution and far away from a negative ideal solution is screened out through a TOPSIS method, and multi-target comprehensive weighing is achieved; according to the method, sintering batching optimization based on finished ore performance feedback is realized, dynamic optimization of a batching plan can be effectively guided, the sinter quality control level is improved, the production cost is reduced, and technical support is provided for efficient and stable operation of a blast furnace ironmaking process.

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 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 provides an axial flux motor multi-objective optimization method based on an improved homing religious algorithm. Firstly, key structure parameters are screened through sensitivity analysis, and the optimization range of the key structure parameters is determined; then, sample data is generated by adopting improved Latin hypercube sampling, and a data set is constructed through finite element simulation; then, performing automatic optimization on smooth factors of the generalized regression neural network based on a He-horse optimization algorithm, and establishing a high-precision agent model; improving a horizon algorithm by adopting Tent chaotic mapping, non-dominated sorting and congestion degree distance, and combining the horizon algorithm with the proxy model to carry out multi-objective optimization to generate a Pareto solution set; and finally, selecting an optimal motor parameter according to the target weight. According to the method, parameterized finite element analysis, an optimization agent model and an improved multi-target algorithm are deeply fused, so that the global optimization capability is ensured, the optimization efficiency and precision are remarkably improved, and an effective solution is provided for rapid and accurate design of the axial flux motor.

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 cluster parallel multi-stage fuzzy job scheduling method, which comprises the following steps of: receiving a processing request which is submitted by a user and contains multi-stage jobs, and modeling task execution time, data transmission time and request deadline into triangular fuzzy numbers, constructing a scheduling model taking the minimization of the total lease cost and the minimization of the request tardiness as targets; carrying out global exploration by adopting an improved second-generation non-dominated sorting genetic algorithm to generate parent and offspring populations; based on population similarity threshold judgment, dynamically triggering local search guided by a multi-agent near-end strategy optimization algorithm, and adaptively selecting a local search operator for each individual to generate an adjacent population; combining various populations, performing non-dominated sorting and crowding distance calculation, and screening out a new generation of populations; and iterating the process until convergence, and outputting a Pareto optimal solution set. According to the method, the problem of multi-target scheduling with fuzzy time variables in a heterogeneous environment is effectively solved, and the quality of a scheduling scheme and the algorithm search efficiency are improved.

The invention discloses a limited transportation resource flexible job shop scheduling system and scheduling method based on a novel evolutionary algorithm, and relates to the technical field of optimal scheduling, and the method comprises the steps: calculating the process completion time of a new scheduling scheme, and generating an optimal scheduling plan; performing non-dominated sorting on the optimal scheduling plan, calculating a congestion distance of a solution, and performing solution set screening to generate an optimal Pareto solution set; and integrating the optimized objective functions, generating comprehensive objective functions, and selecting an optimal scheduling scheme corresponding to the minimum comprehensive objective function based on the optimized Pareto solution set. According to the invention, a self-adaptive crossover and mutation probability adjustment mechanism is introduced through the genetic scheduling module, the global search capability of the evolutionary algorithm is improved, an optimization objective function is constructed through operation data, and the equipment utilization rate and the production efficiency are improved in combination with a self-adaptive evolutionary strategy and multi-objective optimization.

The invention relates to the technical field of target scheduling and warehouse logistics, in particular to a multi-vehicle scheduling method based on a genetic algorithm. Comprising the steps of generating an initial solution by adopting an improved repair mode; taking the total time as a target, performing roulette selection on parents according to the fitness, and performing crossover and mutation operation to generate offspring; repeating improvement and restoration in the initial solution for the filial generation; combining the parent generation and the child generation, and reserving individuals with high fitness to enter the next generation; with the conflict probability as a target, multi-vehicle trajectory planning is optimized through timestamp estimation and an artificial potential field; taking the conflict cost and the total distance as optimization targets, and performing target optimization through a non-dominated sorting genetic algorithm to obtain a non-dominated solution; time and conflict cost are normalized, the Euclidean distance between each solution and an ideal point is calculated, and the solution with the minimum distance is selected as the optimal solution. The method has the advantages that multi-objective optimization is realized through non-dominated sorting and crowding degree distance calculation; conflict risks are considered while global planning is performed, and a reasonable route is planned.

The invention provides a power network three-phase balance optimization method based on a multi-target particle swarm optimization algorithm, which belongs to the technical field of power grid three-phase balance adjustment, and comprises the following steps: acquiring low-voltage transformer area network topology and user power consumption data, initializing a particle swarm population, updating particle positions through a dynamic inertia weight and a self-adaptive learning factor, and optimizing the power grid three-phase balance. The three-phase unbalance degree, the voltage deviation and the economic cost are evaluated in combination with low-voltage power flow calculation, an external archiving mechanism, a crowding distance strategy and variation maintenance solution set diversity are adopted, an optimal phase sequence adjustment scheme and wire diameter configuration are output, and aiming at the characteristics of high impedance and three-phase load imbalance of a low-voltage transformer area, the voltage quality is remarkably improved, and the power consumption is reduced. The method is suitable for intelligent reconstruction of low-voltage distribution networks in residential areas, small industrial and commercial areas and the like, and has good practicability and economical efficiency.

This invention discloses a multi-objective optimization method for high-pile piers and abutments based on an improved Grey Wolf algorithm. The method includes the following steps: establishing a design variable with pile inclination and torsion angle as the design variables, and simultaneously minimizing the standard deviation of pile driving force, maximum pull-out force, and maximum pile bending moment as the optimization objective; designing an integer index discrete encoding strategy to map discrete inclination and torsion angle parameters in the project to integer indices; using an improved multi-objective Grey Wolf optimization algorithm for solving the problem, efficiently searching for Pareto optimal solutions by introducing a nonlinear convergence factor, external archive maintenance, and a leader selection mechanism based on crowding distance; and finally outputting a series of pile placement schemes that achieve a balance between pile driving force uniformity, pull-out force, and bending moment control. This invention achieves collaborative optimization of multiple engineering objectives, with strong algorithm adaptability and significant optimization effects, providing scientific and efficient design decision support for high-pile piers and abutments and other pile foundation projects.

The invention relates to the technical field of cloud computing and distributed computing scheduling, in particular to a server-free MapReduce multi-target scheduling optimization method. Comprising the following steps: constructing a directed acyclic graph, coding a resource allocation decision into a solution from a source node to a target node, generating an initial population by adopting a hybrid initialization strategy, evaluating fitness, calculating a congestion distance of a solution in each non-dominated leading edge, and sequentially executing tournament selection, a self-adaptive crossover strategy and a topological repair variation strategy. And periodically executing a solution injection strategy guided by heuristic search, carrying out crowding distance selection, updating the global Pareto optimal solution set, outputting the current global Pareto optimal solution set when the time is up, and otherwise, returning to iteration. The method has the positive effects that the execution cost and the execution time of the MapReduce operation are balanced, the global Pareto optimal solution set quality of the multi-target scheduling scheme is improved, and the convergence speed and the robustness of the algorithm are enhanced.

The embodiment of the invention relates to the field of agricultural chemical production base site selection, in particular to an agricultural chemical production base multi-target site selection method and device, equipment and a medium. A specific embodiment of the method comprises the following steps: determining an agricultural chemical production base multi-target site selection model; performing fitness non-dominated sorting processing on the initial population; determining iteration information; executing the following iteration steps: determining a filial generation site selection population corresponding to the initial site selection population; performing combination processing on the initial site selection population and the offspring site selection population; performing non-dominated crowding distance sorting processing on the combined site selection population to generate a target offspring site selection population; executing the following determination steps: updating the iteration information to generate target iteration information; determining a new population for site selection; and determining the site selection new population as the agricultural chemical production base multi-target site selection information. According to the embodiment, when a large-scale population is processed, the efficiency of non-dominated sorting can be improved.

This invention discloses a green scheduling method for unrelated parallel machines using dual factors of electricity and carbon. The method includes the following steps: S1, collecting parameters related to production conditions, and constructing an unrelated parallel machine scheduling model with the goal of maximizing completion time and minimizing electricity and carbon costs; S2, constructing a distributed evolutionary algorithm with adaptive and neighborhood search mechanisms, inputting the parameters related to production conditions, solving the unrelated parallel machine scheduling model, and obtaining an optimal scheduling method. When solving the unrelated parallel machine scheduling model, the algorithm randomly initializes the population, employs a multi-objective non-dominated sorting method and crowding distance calculation to improve individual diversity, and dynamically adjusts the learning rate and mutation rate through an information entropy adaptive mechanism. Simulation results show that the ANEDA algorithm performs significantly better in optimizing maximum completion time and total electricity and carbon costs, verifying the algorithm's effectiveness and practical value.

The invention provides a feature selection method, device and equipment based on self-adaptive multiple tasks and a medium, and relates to the field of feature selection. The method comprises the following steps: obtaining a population scale and a decision space dimension of feature selection, and adaptively initializing to generate an initial population and task number set; then carrying out mixed breeding to obtain a progeny sub-population and a corresponding task number; searching solutions related to the current task number from the sub-population and the descendant population corresponding to each number in the task number set as a candidate solution set; after deduplication, elite solutions are selected from the candidate solution set through non-dominated sorting and crowding distance, and the elite solutions are reserved and updated to maintain the size of the sub-populations unchanged until each number in the task number set is traversed; then, dynamically combining different tasks and sub-populations related to the tasks to obtain a population after multi-task combination; and when a set standard is met, obtaining a final population, otherwise, continuing to breed and iteratively updating. According to the method, the diversity and convergence dynamic balance of the multi-task sub-population is realized.

A method and system for optimizing the configuration of hierarchical protection point selection for a distribution network includes setting multiple objective functions and multiple constraints for protection point selection optimization, wherein the protection point selection optimization is to optimize the number and location of protection points; establishing a constraint violation degree calculation formula based on all constraints; obtaining distribution network parameters, and iteratively optimizing the multiple objective functions based on these parameters using a particle swarm algorithm that introduces a three-dimensional vector rotation matrix and a dynamic perturbation vector. During the optimization process, particles are screened based on the constraint violation degree, and particle priorities are calculated based on the constraint violation degree and congestion distance; a set number of protection points are randomly selected from the multiple protection points optimized, and the final protection points are obtained through clustering and screening. The present invention realizes three-dimensional exploration of the multidimensional target space of distribution network protection point selection, significantly improving the distribution uniformity and global search capability of the solution set.

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

The invention belongs to the technical field of unmanned underwater vehicle path planning, and discloses an unmanned underwater vehicle multi-target path planning method and system. According to the multi-target path planning method for the unmanned underwater vehicle, the path length and the path safety serve as optimization targets, firstly, environment modeling is carried out by adopting a grid method combined with a topographic information gray value, and a path smoothness strategy is proposed according to a Bessel function; secondly, introducing an improved particle swarm optimization algorithm, and realizing the balance of diversity and convergence through crowding distance sorting and a Pareto frontier updating strategy; and finally, through non-dominated solution screening, outputting an optimal path solution set. According to the path planning method, the path searching capability and the path distribution diversity can be effectively improved during multi-target path planning, the method has high engineering applicability, and especially under the double consideration of the path length and the path safety index, the good solving and planning capability can be kept.

The invention relates to the technical field of snack product research and development, and discloses a snack product prototype promotion method and device based on deep learning and a cloud platform. The method comprises the following steps: constructing a time sequence multi-modal training set containing formula, process and business metadata and sensory evaluation values, firstly establishing a first model by using a deep neural network, and implementing optimization training for multiple stages such as initialization, data sampling, buffer area capacity, data disruption and hyper-parameter search; the second model takes the physical and chemical simulation value as the input and outputs the corrected predicted value to replace the original theoretical value, the first model is retrained, and then a Pareto frontier-crowding degree distance multi-target algorithm is utilized to efficiently screen out a prototype with a high sensory score from the candidate library for trial production; and if the actually measured deviation exceeds a threshold value, actually measured data is returned to trigger incremental learning, only the last two layers of the model are finely adjusted, and an exponential attenuation weight and an elastic weight are adopted for consolidation to prevent catastrophic forgetting, so that data-model closed-loop iteration is realized, and snack prototypes meeting market requirements are continuously output.

The invention discloses a pump station multi-objective optimization scheduling method based on a multi-strategy enhanced African vulture algorithm, and relates to the technical field of water conservancy projects. The method comprises the following steps: acquiring basic data of pump station operation, and establishing a multi-target optimization scheduling model which takes minimization of electric charge and minimization of the quadratic sum of water shortage of a water receiving area as target functions during the pump station operation period and takes water pump power constraint, water pump pumping capacity constraint and starting-up water pump number constraint as constraint conditions; according to the multi-strategy enhanced multi-target African vulture algorithm, a crowding degree distance sorting mechanism and a hypercube mechanism are adopted to select double leaders to guide a population, an archive-based crossover and mutation strategy is adopted to reduce the possibility that the multi-target algorithm falls into local optimum, and a progressive repair strategy is adopted to repair an out-of-border solution after a main loop is finished. And the multi-strategy enhanced multi-target African vulture algorithm is adopted to carry out pump station multi-target optimization scheduling simulation, so that the operation efficiency of the pump station can be improved.

The present application relates to cloud computing and distributed computing scheduling technical field, specifically belong to a kind of serverless MapReduce multi-objective scheduling optimization method, including: constructing directed acyclic graph, resource allocation decision is encoded as the solution from source node to target node, initial population is generated using hybrid initialization strategy, fitness evaluation, the crowded distance of solution in each non-dominated front is calculated, in turn execute tournament selection, adaptive crossover strategy, topological repair mutation strategy, periodically execute heuristic search guided solution injection strategy, crowded distance selection is carried out, the global Pareto optimal solution set is updated, the global Pareto optimal solution set of current time output is achieved, otherwise return iteration. The present application has the positive effect of balancing the execution cost and execution time of MapReduce job, improving the global Pareto optimal solution set quality of multi-objective scheduling scheme, enhancing the convergence speed and robustness of algorithm.

The invention relates to the field of jacket structure optimization design, and particularly discloses a jacket structure multi-objective intelligent optimization method, which comprises the following steps: establishing a jacket structure multi-objective optimization mathematical model; performing parameter initialization and fitness value calculation, and screening initial schemes to form an initial Pareto solution set; calculating the congestion distance of each solution in the initial Pareto solution set; performing multi-stage iterative optimization, gradually approaching a non-dominated solution, and generating a new scheme; the multi-target fitness value of the new scheme is calculated, and the performance of the new scheme on multiple targets is evaluated; updating a Pareto solution set, and performing solution set fusion and screening on the initial scheme and the new scheme; performing non-dominated sorting on the updated Pareto solution set, and screening a rank 1 layer scheme as a core reference of the next round of iteration; and outputting an optimization result after the maximum number of iterations is reached. By adopting the method, the possibility of multi-target tradeoff can be more comprehensively covered, an optimization mechanism is more suitable for an actual engineering scene, the engineering conversion cost is reduced, and the engineering availability of a design scheme is improved.

The application discloses a distributed server resource allocation method and system, electronic equipment and storage medium. The method determines a variation pool containing a plurality of variation parents; generates a new population containing a plurality of new individuals according to the plurality of variation parents in the variation pool and the sorting values in the first sorting result corresponding to each variation parent; combines the new population and the current population as a total population, performs fast non-dominated sorting on each individual in the total population to obtain a second sorting result; selects a plurality of individuals from the total population according to the sorting values in the second sorting result, calculates the second dynamic crowding distance of each individual corresponding to the maximum sorting value meeting the first preset condition in the plurality of individuals; selects a plurality of elite individuals from the plurality of individuals according to the second dynamic crowding distance to construct an elite population, and iterates the elite population as the current population until a preset iteration number is reached to obtain a target elite population. The application can improve the rationality of distributed server resource allocation.

This invention discloses a method for coordinated optimization of MPPT (Multi-Level Testing) and capacity in a wind power-to-hydrogen system. Meteorological data and system operating parameters are collected, a component mathematical model is established, and the optimization objective and operating boundary are determined. A two-layer collaborative algorithm (upper quantum layer and lower myxomycete layer) is employed to optimize MPPT control parameters and system capacity configuration respectively, and a two-layer collaborative feedback update is performed. The parent generation's non-dominated optimal solution, quantum layer offspring, and myxomycete layer offspring are merged to form a joint population. The Pareto front solution set is screened through non-dominated sorting and crowding distance calculation. After iterative convergence, the Pareto optimal solution set is output, generating a matching MPPT control parameter and capacity configuration scheme. This invention can solve the problems of response lag, susceptibility to local optima, and disconnect between control and capacity matching under wind speed fluctuations, thereby improving wind energy utilization.

The invention discloses a multi-target energy-saving distributed flow shop group scheduling method and system, and relates to the technical field of intelligent production scheduling and optimizing.The method comprises the steps that operation parameters of a distributed flow shop group scheduling scene are obtained, constructing a multi-objective mixed integer linear programming model taking minimization of the maximum completion time and the total energy consumption as optimization objectives; generating an initial population containing multiple feasible solutions by adopting multiple initialization strategies, and dividing the initial population into two sub-populations by adopting a Pareto frontier-based classification mechanism; constructing a dual Q learning mechanism, establishing two independent Q tables to respectively optimize two targets, generating a candidate solution in each sub-population by adopting an adaptive iteration greedy algorithm, a Q learning algorithm and a hybrid algorithm of the adaptive iteration greedy algorithm and the Q learning algorithm, screening an optimal solution by adopting non-dominated sorting and crowding distance sorting to update the sub-population, and continuously iterating and updating until a termination condition is met; and combining the solutions of the two sub-populations to form a Pareto frontier solution set, and obtaining an optimal scheduling scheme with a better effect.

The application relates to a key region identification method based on a four-dimensional characterization index. The method comprises the following steps: based on a sample set, calculating an information surface entropy, a maximum gradient consistency, a local-global similarity divergence and a local-global extreme point ratio to form a four-dimensional characterization index vector of a key region; fusing the four-dimensional characterization index vector based on a Pareto dominance relationship, dividing candidate key regions into different front levels through non-dominated sorting, calculating the crowding distance of each front region, and screening the key region in the test sample space in combination with the front level and the crowding distance. The method can improve the utilization efficiency of a new type of aero-engine digital twin test resources.

This invention discloses a multi-objective parameter intelligent optimization method and system for axial magnetic field permanent magnet motors. The method includes generating a population based on the design variables and their value range constraints of the axial magnetic field permanent magnet motor; generating a set of candidate design schemes based on the current population using an optimizer; calling a simulation evaluator to perform boundary clipping, integer variable rounding, and geometric constraint prediction; performing electromagnetic simulation and calculating the objective vector and total constraint violation degree of the candidate design schemes; backfilling the data to the NSGA-II algorithm; performing non-dominated sorting of the parent and child populations of the current population based on the Deb constraint dominance relationship and calculating the crowding distance within the same non-dominated layer; selecting individuals from the population according to the principle of prioritizing non-dominated levels and having larger crowding distances to obtain an updated population; and finally outputting the optimal candidate design scheme. This invention aims to reduce the proportion of invalid simulations and improve the optimization efficiency of multi-objective parameter optimization for axial magnetic field permanent magnet motor design.

The application provides a multi-load AGV task allocation method based on a clustering algorithm, in a multi-load AGV task allocation scene, the application can better adapt to the dynamic change of tasks in the multi-load AGV storage system and the requirement of multi-objective optimization by constructing a multi-objective function. Meanwhile, in the solving process of the multi-objective function, the application uses a non-dominated sorting genetic algorithm combined with a simulated annealing algorithm to solve the multi-objective task, can consider multiple objective functions at the same time, and can effectively search and optimize the solution space through mechanisms such as non-dominated sorting and crowded distance calculation, further improving the working efficiency and task processing capacity of the whole system.

The application relates to a compiler automatic tuning method based on a discrete particle swarm algorithm, which improves the particle swarm algorithm to adapt to a discrete solution space of a compiler optimization problem, and proposes a speed updating strategy combining a crowded distance and fitness, balances the moving speed of particles by evaluating the crowded distance and fitness of solution samples in a region where the current particles are located, selects the best optimization sequence, and thus improves the performance during compiler automatic tuning; solves the premature convergence problem existing in many current discrete particle swarm algorithms and the problem of insufficient global search capability when the problem dimension is high, and significantly improves the tuning effect of compiler automatic tuning.

This application relates to a method, apparatus, device, storage medium, and program product for coupling optimization of AC quantum parameters. The method includes: initializing an initial population to obtain an initialized first population; spatially mapping the individuals in the first population according to a preset coupling index model to obtain a second population; selecting individuals in the second population based on their non-dominance level and crowding distance to obtain a third population; performing crossover and mutation operations on the individuals in the third population to generate a fourth population; performing iterative optimization based on the fourth population, and outputting the second parameters of the optimal individuals obtained during the iterative optimization process after completion. This method can effectively improve the performance of AC quantum signals, thereby achieving efficient optimization of AC quantum signals.