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308 results about "Discrete particle" patented technology

Discrete element method. The discrete element method (DEM): is intended for modeling events in which large numbers of discrete particles contact each other; models each particle with a single-node element that has a rigid spherical shape, which may represent an individual grain, tablet, shot peen, or other simple body;

Group and place recommendation method based on location and social relationship

The invention discloses a group and place recommendation method based on locations and social relationships. The group and place recommendation method comprises the steps of: acquiring user check-in information in an LBSN, removing places and user data with poor effectiveness, and finally acquiring check-in data of users; utilizing a Pearson correlation coefficient, measuring check-in similarity based on common check-in data of the users, calculating a check-in similar degree among the users, and establishing a user check-in similar degree network; identifying different communities by utilizing a discrete particle swarm optimization method according to the user check-in similar degree network; and acquiring a friend list according to accounts of a user social network, forming social adjacent relationships of users in communities, finally generating a social group, recommending the social group to target users and recommending places to the target users by adopting the collaborative filtering recommendation method. The community finding method is simple and easy to operate, and the community dividing speed is fast. By adopting the method of combining the social group with place recommendation, the complexity of the method is reduced, and the recommendation precision is improved.
Owner:NANJING UNIV OF POSTS & TELECOMM

Intelligent logistics distribution and delivery based on discrete particle swarm optimization algorithm

The invention discloses intelligent logistics distribution and delivery based on a discrete particle swarm optimization algorithm and aims at scheduling the path of a transportation vehicle so as to save the transportation cost. A coding mode and an operator based on the set and the probability are introduced on the basis of a framework of a standard particle swarm optimization algorithm, the particle swarm optimization algorithm which is originally suitable for a continuous space can be introduced into a discrete combined optimized space, so that the problem that the path of the vehicle is scheduled is solved and the advantages of high operation efficiency, strong optimization capacity, strong robustness, and the like endowed by the traditional particle swarm algorithm can be maintained. In addition, by using heuristic information to construct the positions of particles and introduce a local operator search, features of the problem and information contained in the data are utilized, and thereby the solving result of the algorithm is further enhanced. By adopting normalization weighting and decision idea to deal with the target, the transportation path is strived to be shortest while the number of transportation vehicles are required to be minimum, therefore, the transportation cost of logistics distribution and delivery businessmen can be reduced to the maximum extent.
Owner:SUN YAT SEN UNIV

Task allocation method for formation of unmanned aerial vehicles in certain environment

The invention discloses a task allocation method for formation of unmanned aerial vehicles in a certain environment, belonging to the technical field of unmanned aerial vehicles. The task allocation method comprises the following steps of determining a coding sequence of a task allocation algorithm; determining a preponderant function of the unmanned aerial vehicles formed to execute a task; determining a speed update formula and a position update formula of a discrete particle swarm optimization; determining the flow of a tabu search; and determining the flow of hybrid optimization. According to the task allocation method for the formation of the unmanned aerial vehicles in the certain environment, the continuous particle swarm optimization is discretized, the algorithm is simply and conveniently operated on the premise that optimizing property can be guaranteed, and the effectiveness of the discrete particle swarm method is indicated through simulation. According to the task allocation method for the formation of the unmanned aerial vehicles in the certain environment, a supplement strategy of the tabu search algorithm is provided, and the local optimizing capacity of the algorithm is enhanced when the inertia weight [omega] of the particle swarm optimization is larger, i.e. the particle swarm embodies stronger variety, so that the original two algorithms realize complementing each other's advantages, the searching performance can be improved, and the judgment can be verified in multiple groups of simulated tests.
Owner:BEIHANG UNIV

Multi-unmanned aerial vehicle cooperation sequential coupling task distribution method of mixing gravitation search algorithm

The present invention provides a multi-unmanned aerial vehicle cooperation sequential coupling task distribution method of a mixing gravitation search algorithm, and relates to the unmanned aerial vehicle cooperation task distribution field. The method comprises: a multi-unmanned aerial vehicle cooperation task distribution model is constructed in the time coupling constraint, a fitness function and a task constraint are obtained, in the gravitation search algorithm based on genetic operators, the individual discretization coding and the population are initialized, the individual is decoded, and the fitness function is employed to calculate the fitness and perform individual update. Because the genetic operators are added in the gravitation search algorithm, the multi-unmanned aerial vehicle cooperation sequential coupling task distribution method of the mixing gravitation search algorithm has good general applicability, the number of times of long-term simulation tests and data statistics constructs a more improved database to allow the model to be more improved; and compared to the discrete particle swarm algorithm, the mixing gravitation search algorithm can be rapidly converged, the searching optimization result is optimal, the iteration process is brief, and the convergence speed is fast.
Owner:NORTHWESTERN POLYTECHNICAL UNIV
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