10 results about "Knapsack problem" patented technology

The invention relates to the field of artificial intelligence and optimization algorithms, in particular to an intelligent evolution optimization method based on large language model driving. According to the method, semantic comprehension and reasoning capabilities of large language models such as DeepSeek and the like are deeply embedded into a core process of a traditional evolutionary optimization algorithm (PSO, GA), a'semantic cognition-dynamic decision 'double-loop mechanism is constructed, and full-process optimization from problem modeling, intelligent search to explainable output is realized. According to the method, the effectiveness of the method is verified on two typical optimization scenes of a 0-1 knapsack problem (discrete type) and Rosenbrock and Sphere function (continuous type), and through semantic-driven population initialization, dynamic parameter regulation and control and a closed loop feedback mechanism, the core problems that a traditional algorithm is low in initial solution quality, prone to local optimum, insufficient in calculation efficiency and the like are effectively solved.

A wind power grid impact damping suppression method based on branch and bound and knapsack optimization control can realize efficient and rapid suppression of wind power fluctuation. The device is characterized by combining the advantages of thyristor switched filter (TSF) and active power filter (APF), and developing active damping generator technology based on knapsack optimization control for wind power grid impact suppression. The developed ADG technology has the following advantages: ① It has the characteristics of TSF harmonic impact suppression, voltage mutation and high cost performance; ② It has the characteristics of APF output damping continuous adjustable and high precision; ③ Based on branch and bound, the wind power impact is classified as the reference of the maximum damping of ADG output, which effectively improves the rapidity and stability of ADG response; ④ The knapsack problem (UKP) is used to realize the optimal switching control of ADG, which meets the minimum cost and efficient wind power disturbance impact suppression.

This invention discloses a mobile edge caching method based on user location prediction. For multi-base station caching scenarios, this method caches a portion of files in nodes of a mobile edge network with storage capabilities. When users request these cached files, they can obtain them directly through the edge nodes without sending requests to the server via the backhaul link, avoiding duplicate file transmission and alleviating pressure on the operator's backhaul link. Simultaneously, a user location prediction mechanism is introduced, using an LSTM model to predict user locations, obtain the user group within the base station's service area for a certain time period, and calculate the file request distribution. Under cache capacity constraints, by optimizing the global caching strategy, a problem of maximizing operator caching revenue is constructed, simplified to a knapsack problem, and a dynamic programming algorithm is used to find the optimal solution for the caching strategy of each base station at each time step. This effectively reduces backhaul link overhead and improves the operator's caching revenue.

The application provides a multi-objective artificial bionical optimization method for solving knapsack problems, comprising the following steps: S1, setting artificial bionical population parameters and control parameter values, initializing positions and fitness values of each artificial bionical in the artificial bionical population; S2, simulating foraging, dormancy and reproduction behaviors of the artificial bionical; S3, updating the positions and fitness values of the artificial bionical according to step S2; S4, converting a continuous solution space into a discrete solution space through discretization processing; S5, selectively selecting part of the goods according to characteristics of all the goods; S6, repeating steps S2-S5, iteratively updating the positions and fitness values of the artificial bionical until the iteration termination condition is met, the method simulates foraging, dormancy and reproduction stages, and can guide the search direction to turn to other insufficiently explored areas for a multi-objective optimization problem, so that a better global solution can be found.

This disclosure relates to the field of computer technology and provides a picking location planning method, a picking location planning device, a computer-readable storage medium, and an electronic device. The picking location planning method includes: constructing a total picking cost function for the order based on the picking decision variables and picking costs corresponding to each location; constructing an initial problem model based on the total picking cost function of the order combined with a first demand constraint associated with each target item category; transforming the initial problem model to obtain K knapsack problem models corresponding to the K target item categories; solving each knapsack problem model using a dynamic programming algorithm to obtain each initial non-picking location set corresponding to each target item category; and determining the picking location set corresponding to each target item category based on the K initial non-picking location sets. This disclosure enables faster acquisition of optimal picking locations.

The application discloses a kind of federal learning client selection methods and systems for heterogeneous intelligent transportation system, comprising the following steps: S1, establish global prediction model based on federal learning protocol, model client selection problem as two-dimensional complete knapsack problem;S2, obtain the CPU execution frequency of all clients and the physical distance between client and edge server;S3, according to the CPU execution frequency of client and the physical distance with edge server, the client is clustered by heterogeneous perception clustering method;S4, the clustering result obtained is maximized by client selection method The number of clients participating in training within the deadline.Compared with the prior art, by increasing the number of clients participating in training within the deadline, the training efficiency and prediction accuracy can be improved within a limited time.

On the basis of a theoretical method for the ordered multi-knapsack problem, by using a greedy algorithm, i.e., loop statements, the present application solves the problem of combinatorial optimization among different steel grades and different thicknesses and widths in intelligent scheduling and production scheduling processes of endless continuous casting and rolling of hot-rolled strip steel; and while meeting machining constraint conditions, various aspects of requirements in production tasks are fulfilled by means of rational rolling planning, and a global optimal solution can be found more quickly so as to minimize a penalty value (i.e., to obtain a production sequence that minimizes the penalty value). The application is applied to a rolling production process of endless continuous casting and rolling of hot-rolled strip steel, and can realize the maximum utilization of production resources in endless continuous casting and rolling of hot-rolled strip steel (the ratio of total revenue to total cost is maximized), thereby ensuring that orders are completed within a delivery period, mitigating the problems of randomness and volatility caused by manual scheduling, improving production efficiency, and lowering production costs.

The invention discloses a local disturbance agent-based hybrid expert model heterogeneous pruning method, which comprises the following steps of: S1, acquiring hierarchical structure information of a MoE model to be pruned, determining the number of MoE layers contained in the model, and setting a total expert activation budget after pruning; s2, evaluating output representation of each MoE layer under different activation expert numbers by using a pre-selected calibration data set, calculating representation similarity of the output representation relative to high-quality baseline configuration, and obtaining a value function of the layer when an expert is adopted; s3, modeling a hierarchical Top-K activation pruning problem into a multi-choice knapsack problem under the constraint of total budget based on a value function, and solving the optimal expert activation number configuration of each MoE layer; s4, according to the optimal expert activation number configuration, the reasoning routing logic of the MoE model is updated; the method is used for solving the problem that in the prior art, a uniform pruning strategy with the low Top-K value is adopted for all layers in a unified mode, the calculation cost is reduced, and meanwhile the model performance is obviously reduced.

The application relates to the technical field of computer data processing, and provides a storage-computing separation database query optimization method and system for massive data analysis, which solves the technical problems of low system resource utilization and high query response delay. The method comprises the following steps: collecting business query logs and metadata information of all basic tables; constructing a syntax tree based on a query text, and calculating fluctuation indexes of each basic table; constructing a query topology graph based on a fingerprint sequence to obtain a candidate substructure set; taking the remaining available space as a space constraint, determining a target substructure combination through a multi-dimensional knapsack problem, and generating a query rewriting rule; matching the target fingerprint sequence with the query rewriting rule to determine a target substructure; replacing the target substructure with a read operation of pre-computed data corresponding to the target substructure combination; and generating an execution plan for optimizing the query, so that transparent rewriting of the query based on the pre-computed result is realized, and the query performance is maximally improved under the condition of limited maintenance cost.

This invention proposes a parking space planning method based on dynamic programming and bin packing algorithm, comprising: Step 1, constructing an abstract layout of the apron, wherein the abstract layout includes setting the area of ​​the apron and defining obstacles and service lanes in the apron area; Step 2, combining the aircraft types to be parked according to their available taxiing methods to form multiple combinations of aircraft types and taxiing methods, calculating the footprint of each combination of aircraft types and taxiing methods, and calculating the width of the right taxiway based on the footprint; Step 3, treating the parking space planning problem as a complete knapsack problem; each combination of aircraft types is regarded as an item to be packed, the value of each aircraft type is regarded as the item value, the available area of ​​the apron is regarded as the knapsack capacity, determining whether each combination of aircraft types can be parked on the apron according to the parking conditions, and obtaining all possible parking space planning results in the order of each row.