40 results about "Metaheuristic algorithms" patented technology

Route planning methods for use by a package delivery service provider are disclosed that satisfy a stochastic daily demand while taking advantage of drivers' route familiarity over time. A model for estimating the value of driver familiarity is disclosed along with both an empirical and a mathematical model for estimating the value of route consistency, along with a Core Area Route Design which involves the concepts of combinational optimization, meta-heuristic algorithms, tabu search heuristics, network formulation modeling, and multi-stage graph modeling. In one embodiment, a service territory is divided into unassigned cells associated with a grid segment involving prior driver delivery stops, and a driver from a pool of unassigned drivers is assigned to a route based on examining each driver's grid segment visiting frequency limit with respect to a minimum limit so as to optimize driver selection based on of each driver's familiarity with the route.

Watershed hydrology analysis and management process and system with a network of weather stations and artificial drainage systems with artificial and natural reservoir management through locks and pumping stations. It evaluates potential hydrologic risk in each area and analyses the possible consequences of future precipitations using simulations. To make the simulation, it calculates hydrographs for each sub-basin, streams and rivers in the basin. It simulates the behavior of the basin under different scenarios corresponding to different types of management of the operation of locks and/or pumps and compares its results in terms of loss of flooded area, economic loss in each area, loss for flooding of urban areas, etc. Optimization of the simulation through artificial intelligence (AI, meta-heuristic algorithms, neural networks, etc.) allows it to act as a search engine to find better solutions and the best configuration of resource management that allows minimizing the socio-economic impact on each basin.

The invention describes IP cores applied to 3D FPGAs, CPLDs and reprogrammable SoCs. IP cores are (a) used for continuously evolvable hardware using 3D logic circuits, (b) applied with optimization metaheuristic algorithms, (c) applied by matching combinatorial logic of netlists generated by Boolean algebra to combinatorial geometry of CPLD architecture by reaggregating IP core elements and (d) used to effect continuous recalibration of IP cores with evolvable hardware in indeterministic environments for co-evolutionary reprogrammability.

The invention provides a vehicle spare part sales volume forecasting method and system based on a unified dynamic integration model and a meta-heuristic algorithm. The method comprises the steps thata database is established to store data needed for forecasting the vehicle spare part sales volume, and the sales volume of various vehicle spare parts is comprised and is called as a forecasting variable; a data acquisition module is connected with the database and the vehicle spare part sales volume forecasting system to acquire the needed forecasting variable, and a number of parallel typical forecasting methods are used for forecasting to acquire forecasting results corresponding to various forecasting methods; furthermore, various forecasting results are stored, and a unified dynamic integrated model is established; the meta-heuristic algorithm is used to optimize the forecasting model coefficients; the acquired forecasting model is stored in a vehicle spare part sales volume forecasting application system; and a spare part sales volume forecasting result is generated after the corresponding vehicle spare part sales volume data are input. According to the invention, the model which is suitable for forecasting various vehicle spare parts is found; the characteristics of high optimization precision and the like of the meta-heuristic algorithm are used; and the vehicle spare partsales volume forecasting precision is effectively improved.

The invention discloses a hyper-heuristic algorithm based ZDT (Zero Dead Time) flow shop job scheduling method. According to the invention, an objective function of a ZDT flow shop job scheduling problem is set firstly and a corresponding scheduling optimization model is established. On the above basis, a hyper-heuristic algorithm frame is combined, a harmony search algorithm applied widely is adopted as an HLH (High Level Heuristic) strategy of the hyper-heuristic algorithm, and a simple heuristic rule is designed aiming at the characteristics of the ZDT flow shop job scheduling problem for constructing an LLH (Low Level Heuristic) method set, so that the optimization solution of the ZDT flow shop job scheduling problem is realized. According to the invention, good global optimization performance of a meta-heuristic algorithm is remained and uncertainty caused by algorithm parameter adjustment depending on artificial experience in the meta-heuristic algorithm is avoided, so that the algorithm design efficiency is improved effectively and the method is significant to the improvement of flow shop job scheduling efficiency.

The invention provides a hybrid particle swarm tabu search algorithm for solving a job-shop scheduling problem. Compared with other meta-heuristic algorithms, the algorithm has the characteristic of ''elite memory'' according to a PSO and has the characteristic of fast convergence, the PSO is taken as an initial solution source of TSAB tabu search, and an encoding and decoding mechanism for mapping a particle swarm continuous solution space into a discrete space of the job-shop scheduling problem is designed. A real number solution of the PSO is converted into an integer solution of the tabu search algorithm through a real integer encoding method and the integer solution of the tabu search algorithm is converted into the real number solution of the PSO through a real integer decoding method after one-time iteration; and a chance of accurate search is made in a potential space to own more exploration in a global search space. An improved PSO with a balancing strategy is provided, and a balance operator beta is introduced. The performance of the algorithm is greatly strengthened through these improvements and the actual job-shop scheduling condition is combined. The algorithm is high in practicability and good in usability.

The invention relates to a Hadoop load balance task scheduling method based on a hybrid metaheuristic algorithm. A resource slot pressure model is established; the model aims to enable calculation pressures of all Slave node processing tasks in a cluster to be in the same horizontal line; solution of an optimal task scheduling scheme is carried out by adopting the hybrid metaheuristic algorithm based on simulated annealing and particle swarm optimization; and load balance task scheduling in a Hadoop cluster environment is implemented. Further, parallel programming of the algorithm is implemented by a high-performance and wide-transportability MPICH (MPI over CHameleon), the calculating process of a heuristic optimization algorithm is transferred to an additional calculation node, and by simultaneous solution of various swarms, a calculation pressure of a Master node is reduced, and solution capacity of the optimal task scheduling scheme in unit time is promoted. According to the invention, calculation resources of a Hadoop cluster can be subjected to overall distribution, so that the nodes of the cluster are balanced in load, waste of the calculation resources of the nodes is avoided, and profits of equipment investment of a data center are maximized.

The present invention discloses a charging pile optimization layout method based on real driving data of an electric vehicle. The method comprises: firstly, using the analysis method of big data to analyze the real driving data of all electric vehicles, and screening out the parking distribution of the electric vehicles; setting a time threshold value, selecting the location where the parking timeexceeds the threshold value from the parking distribution and fitting the location as a candidate location of the charging pile; and finally taking the number of charging pile locations actually required for construction, the rated cruising range of the electric vehicle and the like as constraints so as to reduce the number of over-discharges of electric vehicle power batteries, and using the meta-heuristic algorithm to obtain the global optimal scheme, that is, the optimal layout scheme of the charging pile. The example shows that the method can quickly and effectively select the charging pile location, and can meet the convenience of charging and the high utilization rate of the charging facility.

The invention discloses a big data acquisition method, a big data acquisition device and a big data acquisition system, which relate to the field of mobile communication. The big data acquisition method comprises the steps of: receiving a connection request sent by an acquisition client about to upload data; calculating delay connection time of the acquisition client by adopting a heuristic algorithm according to current network conditions of the acquisition client; and returning the delay connection time to the acquisition client so that the acquisition client uploads the data to an acquisition server after the delay connection time. The big data acquisition method, the big data acquisition device and the big data acquisition system provided by the invention deals with the condition of oversize transient data volume occurred in the process of network data acquisition to a certain extent, introduce time parameters on the basis of a load balancing technique, coordinate the relationship between two dimensions by means of the meta-heuristic algorithm, allow the flow to be evenly distributed in various time periods, and maximize the use of existing resources.

Techniques and apparatus disclosed herein include methods for allocating data sessions among two radio access networks (RANs), as might be carried out in a network management node operatively connected to one or more network nodes in each of a first RAN and a second RAN, where the first and second RANs have overlapping coverage areas. An example method includes receiving (401) current data session information and network performance information for each of the first and second RANs, from the one or more network nodes, and computing (402) a reallocation of data sessions among the first and second RANs, based on the performance information and configuration data for the first and second RANs, using a metaheuristic algorithm. The method further includes triggering (403, 404) a transfer of one or more current data sessions between the first and second RANs, based on the computed reallocation.

The invention discloses a fireworks-algorithm-based wireless sensor node deployment method. Sensor deployment is carried out based on a fireworks algorithm for solving an optimization problem by using simulation of firework explosion. The fireworks algorithm not only carries forward many advantages of the existing meta-heuristic algorithm but also has own explosive, transient, simple, local coverage, and distributed parallel characteristics. Moreover, individuals of the fireworks algorithm are independent of each other and each operator is capable of carrying out searching locally and independently, so that parallel processing is realized well and a problem of slow convergence on the condition of large node scale is solved. Meanwhile, compared with random distribution or other heuristic algorithms, the method enables the high coverage rate of the three-dimensional area to be realized. Because of full consideration of the landform and terrain, connectivity among all nodes of the network is enhanced on the premise that network effectiveness is guaranteed.

The invention discloses a meta-heuristic-algorithm-based dynamic marshalling scheduling optimization method, thereby solving a technical problem of poor practicality of the existing dynamic marshalling scheduling optimization method. The method comprises: according to a shunting instruction designated by a user, a stage shunting plan is established; a solution space is determined based on a lane and parking space and a fitness function of a genetic algorithm is designed based on the sum of lengths of paths for completing all scheduling work by a motor tractor; with the global search ability of the meta-heuristic algorithm, optimal solutions or approximate optimal solutions of a tractor target lane and parking space in each work are found out; and then on the basis of the stage shunting plan, a scheduling work plan is provided. The test demonstrates that the method is suitable for marshalling scheduling optimization of all marshalling stations to improve the marshalling scheduling efficiency of the marshalling stations; and the practicability is high.

The invention relates to a bearings-only passive positioning method based on a metaheuristic algorithm, and aims at the situations that the sensor distribution is not fixed, the performance differencebetween sensors is larger. The positioning effect of the method has significant advantages compared with a traditional least square positioning method, and a target function needs to revised only when an observing environment changes. A paradigm of using the metaheuristic algorithm to perform target counting information fusion is provided to use the capability of the metaheuristic algorithm in solving non-convex problems, and the paradigm can solve a plurality of problems of target counting information fusion under an observing environment which can be modeled.

The invention provides a building height optimization configuration method and device. The method comprises the following steps: determining a building height suitability optimization model and an urban thermal environment fairness optimization model; based on the building height suitability optimization model and the urban thermal environment fairness optimization model, determining a building height configuration optimization model; inputting building data into the building height configuration optimization model, and processing the building height configuration optimization model by adopting a meta-heuristic algorithm to obtain a target solution meeting a preset optimization condition, wherein the building height suitability degree in the regional space corresponding to the target solution is the maximum, and the sky opening degree index difference is the minimum; and determining a building space layout optimization configuration result according to the target solution. By adopting the method disclosed by the invention, the influence of the building height on the urban thermal environment can be reasonably measured, and the fairness and accuracy of optimal configuration of the building space layout are improved.

The invention discloses a structural damage identification method based on ALO-INM and a weighted trace norm. The method comprises the steps of: building a structural finite element model comprising Nel units according to a model correction theory and a finite element principle, and calculating the first Nm-order inherent frequency and vibration mode of the model; respectively establishing an original objective function O([alpha]), a first conjugate objective function and a second conjugate objective function, namely O*(alpha) and O**([alpha]), of the structural damage identification constraint optimization problem according to the frequency relative change rate and the modal confidence criterion; and solving O**([alpha]) by using an ALO-INM algorithm to obtain a structural damage identification result. According to the invention, an INM local search strategy is introduced on the basis of a meta-heuristic algorithm, the global optimization capability of the algorithm is enhanced to a certain extent, a weighting strategy and trace sparse regularization are introduced into a target function, so that the recognition precision and the noise robustness are improved, the influence of damage sensitivity and noise of different structures on the recognition precision can be reduced, and the method has relatively strong global optimization capability, relatively high recognition precision and relatively good noise robustness.

The invention discloses a mechanical product service optimization method in a cloud manufacturing platform. First of all, according to user requirements, a user demand is decomposed into a plurality of subtasks; wherein each subtask corresponds to a plurality of service parties; the service time and the service cost are used as indexes; the k optimal service combination schemes are recommended tothe user by using the k optimal selection algorithm, the calculation complexity is reduced, the problem that the meta-heuristic algorithm is not easy to converge is avoided, and a reliable selection range can be provided for the user more efficiently and accurately, so that the time cost of the user in work is saved, and the enterprise competitiveness is improved.

The invention provides a crowd information clustering method based on a hyper-element heuristic algorithm. The crowd information clustering method solves the problem that it is difficult to establish crowd clustering in a dynamic environment in the prior art. The crowd information clustering method comprises the following steps: establishing a social graph model according to a social network of a crowd; obtaining an adjacent matrix A and elements a_{ij} of the matrix according to the model; obtaining the grade of the node i; by analyzing and judging the grade of the node i, determining a strong sensory group and a weak sensory group, so that a clustering target of the crowd information is defined, and the clustering target comprises a clustering target NRA and a clustering target RC; and designing a hyper-element heuristic algorithm to carry out clustering processing on the clustering target. According to the crowd information clustering method, the clustering capability of the algorithm in the dynamic network environment of the crowd information can be effectively improved, and the clustering quality is improved.

An optimization apparatus for repeatedly obtaining a constraint violating solution, a constraint satisfying solution, and an approximate solution in a resource-constrained project scheduling problem performs setting an end time of a target optimization period at a point between a first finish time equal to a finish time of a latest version of the constraint violating solution and a second finish time equal to a finish time of a latest version of the constraint satisfying solution, followed by obtaining the approximate solution using a metaheuristic algorithm, making a determination as to whether the approximate solution violates constraint or satisfy the constraint, and performing, based on the determination, updating the first finish time with a finish time of the approximate solution when the approximate solution violates the constraint, and updating the second finish time with a finish time of the approximate solution when the approximate solution satisfies the constraint.

The invention provides a military dog-based cognitive industrial Internet of Things big data clustering parallel algorithm, which specifically comprises the following steps: (1) preparing clustering data, (2) distributing tasks to different machines in an MR-MHBC-Map stage to simulate a search process of a military dog on suspicious targets to perform clustering and update a clustering center, and (3) on each machine, performing clustering on the suspicious targets in the MR-MHBC-Map stage. The optimal clustering center of each data point is solved during each iteration, and (4) the decomposed tasks are combined in the MR-MHBC-Reduce stage, and whether the termination condition of the algorithm is met is judged. According to the method, the advantages of MapReduce are utilized, and a new clustering method based on meta-heuristic is provided to solve the problem of big data. According to the method, the potential of searching a suspicious target by a military dog is fully utilized, and a big data set is processed by adopting a MapReduce structure. The MR-MHBC algorithm is superior to other existing algorithms in the aspect of clustering the big data set, and has important practical significance.

The invention provides a novel offshore wind plant reactive power optimization method based on mean value variance mapping. The method follows a predictive optimization scheme (i.e., day-ahead, intra-day applications). Predictive optimization is based on the principle of minimizing actual power loss and reducing the number of times of operation of an on-load tap changer (OLTC) in a daily time range (discretization is 24 hours). A new meta-heuristic algorithm mean-variance mapping optimization (MVMO) is utilized to solve the problems of mixed integer properties and limited calculation budget ofthe problem. By introducing a new mapping function, the evolutionary mechanism of the MVMO is enhanced, and the global search capability of the MVMO is improved. Practical investigation on an offshore wind power plant with HVDC connection proves that the MVMO is effective in finding a solution for ensuring minimum loss, minimum influence on OLTC life and optimal power grid specification conformance.

The disclosure relates to systems and methods for routing vehicles and scheduling vehicle rides. In a system embodiment, a computer processor operable to execute computer-executable instructions, anda memory comprising computer-executable instructions can be provided. The computer-executable instructions can be operable to, prior to a predefined cutoff time and based at least in part on the starttime and the end time for a first driver, generate a sequenced list of vehicle rides;; determine, prior to the predefined cutoff time, a revenue potential for each hour increment between the start time and the end time; modify, based at least in part on implementation of a metaheuristic algorithm, and after the predefined cutoff time, the sequenced list; and output, via a graphical user interface, the modified sequenced list to the first driver, wherein the graphical user interface comprises a list of optimized vehicle rides.

The vehicle including an active suspension system (22) parameterized by a set of adjustment parameters. The railway track is cut into segments. For each segment (T), the method includes campaigns for optimization of the set of parameters, such that: during the first campaign, to each passage of the suspension system (22) on the segment (T), a first set of parameters, specific to this passage, is predefined and applied to the suspension system (22), and a comfort quality index is calculated, and then a metaheuristic algorithm is applied for determining second sets of parameters, and during each following optimization campaign, at each passage of the suspension system over the segment, one of the determined sets of parameters by the previous optimization campaign is applied to the suspension system, and the comfort quality index is calculated, and then the metaheuristic algorithm is applied in order to determine new sets of parameters.