79 results about "Knapsack problem" patented technology

The invention discloses a method for islanding an electric power system into a plurality of subsystems. The method comprises the following steps: dividing an electric power system islanding model into a first electric power submodel and a second electric power submodel; for the two submodels, using a staging solving strategy which is searching+adjusting: a first stage: searching the first electric power submodel based on a knapsack problem restrained by a connected graph so as to acquire each isolated subsystem; a second stage: carrying out optimization and adjustment to the each isolated subsystem based on the second electric power submodel. According to the each subsystem obtained through the islanding method, specific coherent generator groups are in the same subsystem, the subsystems are connected, voltages of the each isolated subsystem and an equipment capacity constraint are all in a bound, and a total load of safe power supply of the each isolated subsystem is greatest. Because of employing a decomposition solving strategy, a calculating speed can be accepted by engineering by using the islanding method.

A method of determining allocations in a business operation to maximize profit includes: collecting profit data for a plurality of classes in the business operation, where each class includes an allocation having a cost function and each allocation belongs to the group consisting of physical allocations and economic allocations; determining profit functions for the allocations from the profit data; formulating a Multiple Choice Knapsack Problem to maximize profit from the profit functions, the cost functions, and a cost constraint; and solving the Multiple Choice Knapsack Problem to determine values for the allocations.

The invention discloses a multichannel diversity-based cognitive medium access control realizing method, which is designed on the basis of multichannel diversity. In the method, source nodes and target nodes compete for the right of use of a data channel through exchange control packets on a public control channel, node pairs wining the competition determine data channels and corresponding power allocation for the transmission according to power / channel combined allocation and are shifted to corresponding data channels to transmit a plurality of data packets continuously. The invention also provides a method for resolving power / channel combined allocation, which is to resolve the power / channel combined allocation by converting the power / channel combined allocation into a multi-choice knapsack problem and by dynamic planning. Simulation results show that the cognitive MAC protocol can effectively improve intra-node pair data transmission rate and network throughput under a condition that the total transmission power is limited compared with an opportunistic multiradio MAC (OMMAC) protocol.

The invention discloses an electric system splitting fracture surface searching method based on master-slave problem alternating optimization. A complete active splitting model is transformed to a master-slave problem based on the connected graph constrained Knapsack problem (CGKP) theory. The master problem is the graph optimal balanced dividing problem and solution is performed with the CGKP technology adopted. The slave problem is that the solution is performed with the optimal power flow (OPF) technology adopted and based on the scheduling problem of the OPF problem. The coupling between the master problem and the slave problem is achieved through regulation variable of node load. A more excellent splitting scheme is obtained through alternating iteration between the master problem and the slave problem. Compared with the prior art, a novel strategy based on the a master-slave problem alternating solving electric system optimal active splitting section is proposed, and meanwhile, the splitting scheme is enabled to be closer to an optimal solution of the complete model. Therefore, a greater load is generated after splitting to achieve power supply.

The invention provides a method for network flow scheduling suitable for a distributed network environment. A network flow prediction method based on Kalman filtering is adopted in the flow scheduling process to predict flow collected by a distributed network flow center; due to the fact that a flow scheduling model in the distributed network environment is a multi-dimensional knapsack problem which is an NP problem, by the combination with factors such as predicted flow and each network time delay, the NP problem is solved, an optimized scheduling result obtained; after flow scheduling, the distributed network can access a server in a maximum ratio within the bearing capacity range of the server, message spreading time delay and the hop count are minimized, the goal of load balance is achieved, and the flow scheduling effect is optimal. Efficient prediction of the distributed network flow and flow scheduling high in utilization rate and low in consumption can be achieved by applying the method.

The invention relates to a modeling method for a micropore structure in a bionic bone scaffold. The method comprises the following steps that: a negative model of the micropore structure in the bionic bone scaffold is solved on the basis of a multi-constraint knapsack problem model by using an ellipsoid as a base element, using the minimum accommodating box of a bone scaffold model as a constraint space and using a hybrid genetic algorithm; and then a bionic bone scaffold model comprising the micropore structure is built through a Boolean subtraction operation between the scaffold model whichdoes not contain micropores and the negative model of the micropore structure. Under the condition of ensuring relatively high void ratio and superficial area of the micropore structure in the bionicbone scaffold, by the method, single shape of the internal micropores and uneven distribution of the micropores are overcome and the torsion resistance of internal communication pore canals in the bionic bone scaffold is increased, so that the performance of the bionic bone scaffold model is improved and the bionic bone scaffold model has relatively high bioactivity.

The invention relates to a recognition method for components of Chinese character pictures and belongs to the field of image processing and pattern recognition. The method includes firstly pre-processing Chinese character component pictures with specific fonts, performing skeleton extracting, forming initial sections according to endpoints and intersections which are detected out, and merging some sections in an artificial interactive mode; next, performing statistical modeling on the marked sections and describing directional information of the sections by using a four-dimensional vector; then constructing section neighbor relations to complete the component modeling process according to the maximum spinning tree principle; for an input Chinese character, firstly decomposing the character into sections, then generating a group of section sets with the maximum similarity by contrasting each component in a library, finally obtaining recognition results of components of the input Chinese character through the optimum selection strategy, namely, obtaining the optimum solution through solution of the variety knapsack problem. Local features are introduced, the optimum combination strategy is adopted, the complete recognition method for the components of the Chinese character pictures is formed, and the recognition rate can be improved effectively.

The invention relates to an adaptive genetic annealing calculation method for solving a zero-one knapsack problem. The algorithm adopts a selection mechanism combining roulette and an optimal storage strategy, so that the current optimal individuals are always kept in a group; by the adaptive intersection and variation probability, the group search range is expanded, then a simulated annealing algorithm is introduced, and the convergence speed of an iterative later-stage algorithm is increased; and finally, the improved adaptive genetic annealing algorithm is applied to a zero / one knapsack. An experiment result indicates that by the adaptive genetic annealing algorithm, a more satisfactory effect than that of a standard genetic algorithm and that of the conventional adaptive genetic algorithm can be achieved. The adaptive genetic annealing calculation method has the advantages of high convergence speed, high optimizing capacity and high stability, and is particularly suitable for achieving an effect of high-dimension constraint optimization.

The invention discloses an underwater multi-sensor cooperative passive tracking method based on a dynamic cluster. According to the method, a distribution type fusion estimation process with feedbackis introduced into a target tracking process, and a linear minimum variance fusion criterion weighted by a scalar quantity is used for minimizing a trace of fusion error covariance by utilizing components, and the optimal fusion state estimation is achieved. A dynamic clustering process selected based on adaptive nodes is used for dynamically selecting cluster head nodes and cluster member nodes which are involved in the passive target tracking process, wherein the selection of the cluster head nodes is mainly started from the view of energy; and the selection of the cluster member nodes is that an objective function is constructed by using utility functions and cost functions, the node selection problem is classified as a knapsack problem in mathematics, and finally a dynamic programmingmethod is used for selecting an optimal node combination to achieve the maximization of the objective function. By adopting the method, the convergence of the accuracy of the passive target tracking can be guaranteed and the energy consumption of the network in the passive tracking process can be effectively reduced.

The invention provides a three-dimensional encasement scheme generating method based on a three-step dynamic programming. A three-dimensional encasement problem is decomposed into three 01 knapsack problems which are conducted in sequence. The generating process of a three-dimensional encasement scheme is converted into a solution of the three 01 knapsack problems. According to the three-dimensional encasement scheme generating method based on the three-step dynamic programming, compared with existing methods, the encasement rate and the encasement scheme generating speed are improved; the container space use rate is further increased through the three-dimensional encasement scheme generating method, the container freight cost is reduced, and the corporate profits are increased.

A hardware-software partitioning method based on a greedy simulated annealing algorithm comprises the following steps: (1) specifying the hardware-software partitioning problem as 0-1 knapsack problem, initially partitioning a task set by a greedy algorithm with low time complexity, and setting the partitioning result as the initial value of the simulated annealing algorithm; and (2) performing the simulated annealing algorithm, which is mainly composed of two layer loops, namely an inner loop and an outer loop, wherein the inner loop can generate a new partition according to a disturbance model, and determine and accept the new partition according to an acceptance criteria; and the outer loop is used for determining whether or not to exit the loop process according to a temperature threshold and the number of new partitions which are not accepted continuously. The method provided by the invention can reduce algorithm operation time, improve search quality and reduce calculation complexity.

The invention discloses a resource allocation method applicable to a multi-hop wireless mesh network. The method includes the steps that the parameter of each node on a multi-hop path is acquired, and the characteristic parameters of each hop of link in the multi-hop path at different rates and different frame lengths are calculated and comprise media access time slot numbers and packet error rates; with minimum time slot occupation as an optimization target, a multi-choice knapsack problem mathematic model is established with the end-to-end PER as the constraint condition; the solution of a multi-choice knapsack problem is converted into the solution of a multistage decision diagram according to a dynamic planning method; the multistage decision diagram is solved according to a Dijkstra algorithm so that an optimal resource allocation result can be obtained. Compared with the prior art, the method is applicable to resource allocation of the multi-hop WMN, resource allocation is performed by source nodes, a graph theory is used for simplifying the algorithm, the resource allocation complexity is reduced, and a feasible optimal allocation result can be obtained.

The invention provides an intelligent asset matching method comprising the following steps: S1, calculating the characteristic value of an asset target to be matched; S2, selecting a real asset target of which the characteristic value matches the characteristic value of the asset target to be matched from an asset pool; S3, comparing the characteristic value of the real asset target with the characteristic value of the asset target to be matched, completing matching if the characteristic value of the real asset target is greater than or equal to the characteristic value of the asset target to be matched, and going to S4 if the characteristic value of the real asset target is smaller than the characteristic value of the asset target to be matched; and calculating the characteristic values of remaining asset targets to be matched, and going to S2. According to the method, by calculating the characteristic values of asset targets and conducting asset matching according to the characteristic values and based on a knapsack problem, intelligent matching of assets is realized, asset matching can be optimized as much as possible, and users' money can be utilized effectively.

A solution to a variant of a class constrained multiple knapsack problem. Previous solutions require that memory demand of every application be identical and do not consider minimizing placement changes. Previous techniques do not consider optimizing placement to improve load balancing as is described subsequently. Thus, the present invention provides systems, methods and apparatus, encapsulated in software, to provide the dynamic placement of application instances on a heterogeneous cluster of server machines. It depends on the existence of a visible and controllable platform, systems management and other business services that signal events and accept commands. It provides dynamically placing applications on servers such as to maximize a total amount of satisfied application demands, minimize a number of placement changes from a current placement, and minimize imbalance of load distribution of said applications on said at least one server.

The invention discloses a multiple knapsack problem-based aggregated spectrum allocation method. The problem of low spectrum utilization rate of the conventional allocation method is mainly solved. The method is implemented by the following steps that: a base station collects the service request information of users and idle frequency bands in system frequency bands, aggregates the idle frequency bands in the system frequency bands from low frequency bands to high frequency bands to obtain M usable aggregated frequency bands, simulates a judgment process of allocation of the M aggregated frequency bands into a multiple knapsack problem (MKP) according to a spectrum utilization efficiency maximization rule, solves a final allocation result according to a solving principle of the MKP, and transmits the allocation result to the users to finish current aggregated spectrum allocation. The advantages of discrete idle frequency bands can be fully utilized, the method can be applied to a wireless communication system supporting spectrum aggregation (SA), the bandwidth requirements of the users are met, and meanwhile, the utilization efficiency of spectrums is greatly improved.

The invention relates to a bat algorithm and a system, belonging to the field of data processing, and particularly relates to a single target optimization problem method integrating explosion strategy, opposition-based learning and bat algorithm and a system. In the optimization process, the opposition-based population of each bat in the bat algorithm is obtained by opposition-based learning, andthe elite selection is performed on the two populations, thus the diversity of the population is increased, and the global search ability is improved, the local optimal solution is avoided, the explosion strategy is added during the flight process, thus the local exploration ability of the algorithm is improved, and the optimal solution is more accurately searched. Through the simulation test of 12 typical test functions and knapsack problems, it is proved that DGOBA has higher efficiency for solving continuous and discrete optimization problems.

A resource management method in SVC multicast based on NOMA in a heterogeneous wireless network is characterized in that the resource management is joint resource management including wireless spectrum allocation and transmitting power division, a joint resource management problem is decomposed into sub-problems, then the sub-problems are solved respectively. The method comprises the following steps: 1) converting a joint resource management problem into a mixed integer nonlinear programming problem, and decoupling the mixed integer nonlinear programming problem P0 into two sub-problems: a multicast group internal transmitting power division problem P1; inter-base station spectrum resource segmentation and multi-group spectrum resource allocation problem P2 in the base station; 2) solvingthe problem P1, solving all possible power distribution schemes, wherein the group with the highest overall video quality is the optimal solution; and 3) solving the problem P2, modeling the spectrumresource segmentation between the base stations and the resource allocation problem between the multicast groups into a knapsack problem, and solving the knapsack problem.

The embodiment of the invention discloses a service providing method and device, electronic equipment and a computer-readable storage medium. The method comprises the steps of pre-estimating a total service quantity required by a current user according to attribute information of the current user; determining different service weights corresponding to different feature label service sets accordingto the total service quantity, wherein each feature label service set comprises one or more services with same feature labels, and the sum of different service weights is smaller than or equal to thetotal service quantity; determining one or more services from different feature label service sets according to a solution algorithm of a knapsack problem, service feedback attributes and the serviceweights; providing the determined services for the current user. According to the service providing method, the possible bearing range of the user for the demanded service quantity is taken into consideration, the suitable services are selected from the service sets with different feature labels and provided for the user, and therefore matching of finally-obtained services can meet the service requirements of the user in all aspects.

The invention discloses an efficient improved FSPSO (Particle Swarm Optimization based on prey behavior of Fish Schooling). According to the efficient improved FSPSO, an intelligent behavior is simulated, and current globally-optimal particles search for current globally-superior positions through own optimal position information provided by a minority of other random particles. When fish schooling is attacked by other predators, weak fish which cannot escape quickly is eaten. The behaviors are simulated, weak particles close to current globally-worst particles are replaced with particles which are generated randomly, so that the diversity of the schooling is improved, and a local optimum can be effectively avoided by the FSPSO. The efficient improved FSPSO can be particularly applied to the solving process of complicated optimization problems such as function optimization and knapsack problems, traveling salesman problems, assembly line work problems and graph and image processing problems.

Disclosed methods, systems, and apparatus, include computer programs encoded on computer storage media, for performing allocation of M resources among N users into K pools by solving a knapsack problem (KP) using a distributed computing system. The method includes: receiving data representing K global constraints and L local constraints of the KP; transforming the KP into a dual problem using K dual multipliers; decomposing the dual problem into N sub-problems; performing two or more iterations in solving the dual problem, wherein in one iteration, for each dual multiplier corresponding to a global constraint corresponding to a pool: determining an updated dual multiplier for the global constraint corresponding to the pool to be a non-negative threshold; and computing M decision variablesof each of the N users corresponding to the updated dual multiplier in solving each of the N sub-problems corresponding to the each of the N users.

The invention discloses a method for link self-adaption of video sensing in an HSDPA network. The method comprises the steps of first calculating video transmission utility of a video packet for the video packet of a terminal user; dynamically calculating transmission capability and a corresponding link self-adaption scheme and transmission error probability according to reporting conditions of wireless channel quality signal interference noise ratio (SINR) of the terminal user; finally utilizing a dynamic programming mathematical method to convert a link self-adaption utility maximum restricted problem to a standard mathematical Knapsack problem, and solving a video transmission utility maximum link self-adaption scheme through limited mathematic iteration. According to the method, factors such as video packet transmission utility, user wireless transmission capability and user wireless transmission error probability are considered comprehensively, and simulation experiments show that the wireless transmission capability of video users in an HSDPA system can be effectively improved.

The invention discloses a wireless heterogeneous network file cache updating method based on multicast. The method comprises the steps of 1, establishing a network topology which comprises small basestation distribution, user distribution, and distribution of connection relationships between small base stations and users; 2, establishing a current popular file set popularity distribution model; 3, establishing an energy consumption model for updating files in a multicast mode; 4, inferring a target function and a limiting condition for maximizing a system cache hit rate, and establishing an optimization model; 5, converting an optimization problem into a multi-choice knapsack problem, and carrying out solution to obtain cache file updating schemes of all small base stations; 6, sending afile to the small base stations in a multicast mode; and 7, periodically carrying out cache updating on all small base stations in the system through adoption of the steps 2-6 when a popular file setis changed. According to the method provided by the invention, popular files can be reasonably updated to different small base stations, so the possibility of directly obtaining video data from the small base stations by the users is improved, and the data of a network backhaul link is reduced.

The invention provides a method for automatic material interchange in urgent specification change of a hot rolling production line. When a three-level computer receives a second-level specification change telegraph text, virtual slabs which are not brought into a rolling mill operating plan yet and meet contract matching conditions are searched for interchange. Model constraint conditions include that the width and thickness of hot rolling materials are identical to the target width and target thickness of an order, actual steel codes of the slabs are identical to representative steel codes or nominal steel code of the order, the nominal width of the slabs minus the maximum side pressure quantity of the slabs is smaller than or equal to a target width value of a hot rolling procedure outlet, the nominal width of the slabs minus the minimum side pressure quantity of the slabs is larger than or equal to the target width value of the hot rolling procedure outlet, checking weight is larger than or equal to a minimum inlet piece weight and smaller than or equal to a maximum inlet piece weight, and solution can be worked out by translating matching between the contract and the slabs into a 0-1 knapsack problem. Using the method can improve operating efficiency, shorten delivery time for users, reduce overstocked products, quicken logistic circulation and reduce coil surplus and capital employment rate.

The invention discloses a coil steel warehouse-out management method based on warehouse operation, and relates to the technical field of cargo operation management. According to the method, t, the inventory overturning and tamping amount is calculated; d; determining an optimal cargo warehouse-out sequence according to the turnover quantity; e; each trolley can load goods meeting the conditions according to the optimal sequence; t; the time loss in the warehouse-out process is reduced; m; meanwhile, t, the load of the vehicle is also considered in the operation process; A; an inventory sortingproblem is abstracted as a backpack problem; c; calculating the inventory of the backpack when the backpack reaches the maximum weight; T; the maximum weight needs to be smaller than the maximum loading weight of the vehicle, if the loaded inventory cannot meet the conditions, t, the previous cargo is replaced with the next cargo in the to-be-sorted warehouse-out list until the conditions are met, t, the cargo is prevented from being returned and unloaded, t, the vehicle detention time is shortened, and the operation efficiency of the bulk cargo warehouse in the warehouse-out process is improved.

The invention relates to the technical field of knapsack problem solving, and discloses a method for solving a bounded knapsack problem based on an improved dynamic programming algorithm, and the method comprises the steps of defining the maximum number of article types as N, the maximum capacity of a knapsack as C, the article types as i, i being greater than or equal to 0 and less than or equalto N, the single value of each article as vi, the weight as wi, and the number of each article is ki; defining an (N + 1) * (C + 1) two-dimensional value table f, the knapsack capacity is j, and j islarger than or equal to 0 and smaller than or equal to C; and calculating a remainder a, grouping the capacity states j according to the capacity remainder a, and calculating the capacity states j with the same capacity remainder in one group. According to the invention, the capacity states j are grouped according to the remainder, a traditional dynamic programming recursion formula is improved, redundant calculation brought by the BKP solving process is reduced, meanwhile, parallelization is conducted on the improved algorithm, and compared with an existing algorithm, the method has higher efficiency and can achieve rapid solving of the BKP along with increase of the data volume.

A method for assembling luggage includes: hierarchically classifying the luggage according to the characteristics of the luggage and the total number of layers to obtain the classification hierarchy of each line of luggage; S2, numbering the luggage for each classification level, and establishing a knapsack problem model according to the characteristics of the luggage, wherein the knapsack problemmodel takes the maximum distribution profit of all the parcels as an objective function; S3, solving the objective function by adopting a dynamic programming algorithm and a greedy method, and obtaining an assembly scheme ofthe luggage. Through layered classification of goods, and in the process of classification, based on the fragility of bags and other characteristics, for each layer, according to the characteristics of bags and benefits maximization, the knapsack problem model is built, greedy method is used to solve, so as to obtain the assembly plan of the luggage.

The invention provides an express distribution method based on 0-1 knapsack problem analysis. The method comprises the following steps: counting the weight and commission of each express; obtaining vehicle load information of express delivery carried by a courier; establishing a 0-1 knapsack problem mathematical model of express distribution; and solving a 0-1 knapsack problem of express distribution through an enhanced genetic algorithm. The algorithm of the invention can effectively help the courier to distribute the express and increase the salary commission available for the courier by analyzing the express information and the vehicle information of the courier, improves the rationality of express distribution, and has a good application prospect.

The invention belongs to the technical field of power supply management, in particular to a method for quickly distinguishing transformer area user-transformer relations. The line loss electric quantity and the user electric quantity are analyzed by using a Pearson correlation coefficient method; particularly, correlation analysis is carried out on a negative loss transformer area, abnormal userscan be quickly found, the number of field check users is reduced, a user marking unit is designated as a unit, the unit is a meter box (generally one meter box represents the same power supply point,namely the same transformer area), the daily power consumption of the meter box is calculated according to the relationship between the meter box and the users, and then the transformer area relationship of the user meter box is checked; a 'genetic algorithm' is adopted to solve a 'combination problem', one population has a plurality of individuals, and each individual has a chromosome and a corresponding gene and is similar to a knapsack problem in the genetic algorithm.

The invention provides a content caching and spectrum allocation joint design method oriented to NOMA wireless video multicast. According to the method, joint allocation of cache resources, power and bandwidth resources is comprehensively considered, and the purpose is to maximize the overall video receiving quality of users in a multicast group under the constraint of multi-dimensional resources. According to the invention, the method comprises the steps: modeling spectrum allocation and layered video caching as a mixed integer linear programming problem; in order to facilitate processing, decoupling the optimization problem into a resource allocation sub-problem of intra-group spectrum constraint and cache constraint; finally, converting the resource allocation sub-problem of the intra-group spectrum constraint and the cache constraint into a knapsack problem, and designing a multicast group-oriented PSNR priority algorithm for solving. On the basis, a hierarchical content caching algorithm in a multicast group is designed to obtain an optimal PSNR and an optimal hierarchical video caching scheme. Simulation results show that the scheme is superior to the existing method in the aspects of network resource utilization rate, video receiving quality and the like.

The invention discloses a classical combinatorial optimization problem in the field of operational research, i.e., the multi-scale quantum resonance solving algorithm for a multi-choice and multi-dimensional knapsack problem, firstly, the Lagrange relaxation dual model is established for the original problem model, which greatly reduces the constraint conditions and evaluates the value of the items in the group by using Lagrange value. On this basis, the MMKP approximate kernel problem is introduced, which is heuristic to find the optimal solution and reduces the scale of the problem. In addition, based on the approximate kernel problem, a mixed Gaussian sampling model is constructed to guide the algorithm to intensify the search in the trusted region and to uniformly search in the untrusted region, which improves the solution accuracy and convergence speed.