Due to the limited transmission resources for data relay in the tracking and data relay satellite system （TDRSS）, there are many job requirements in busy days which will be discarded in the conventional job scheduli...Due to the limited transmission resources for data relay in the tracking and data relay satellite system （TDRSS）, there are many job requirements in busy days which will be discarded in the conventional job scheduling model. Therefore, the improvement of scheduling efficiency in the TDRSS can not only help to increase the resource utilities, but also to reduce the scheduling failure ratio. A model of nonhomogeneous parallel machines scheduling problems with time window （NPM-TW） is firstly built up for the TDRSS, considering the distinct features of the variable preparation time and the nonhomogeneous transmission rates for different types of antennas on each tracking and data relay satellite （TDRS）. Then, an adaptive subsequence adjustment （ASA） framework with evolutionary asymmetric path-relinking （EvAPR） is proposed to solve this problem, in which an asymmetric progressive crossover operation is involved to overcome the local optima by the conventional job inserting methods. The numerical results show that, compared with the classical greedy randomized adaptive search procedure （GRASP） algorithm, the scheduling failure ratio of jobs can be reduced over 11% on average by the proposed ASA with EvAPR.展开更多
The p-center problem consists of choosing a subset of vertices in an undirected graph as facilities in order to minimize the maximum distance between a client and its closest facility. This paper presents a greedy ran...The p-center problem consists of choosing a subset of vertices in an undirected graph as facilities in order to minimize the maximum distance between a client and its closest facility. This paper presents a greedy randomized adaptive search procedure with path-relinking （GRASP/PR） algorithm for the p-center problem, which combines both GRASP and path-relinking. Each iteration of GRASP/PR consists of the construction of a randomized greedy solution, followed by a tabu search procedure. The resulting solution is combined with one of the elite solutions by path-relinking, which consists in exploring trajectories that connect high-quality solutions. Experiments show that GRASP/PR is competitive with the state-of-the-art algorithms in the literature in terms of both solution quality and computational efficiency. Specifically, it virtually improves the previous best known results for 10 out of 40 large instances while matching the best known results for others.展开更多
Traveling Salesman Problem (TSP) is one of the most widely studied real world problems of finding the shortest (minimum cost) possible route that visits each node in a given set of nodes (cities) once and then returns...Traveling Salesman Problem (TSP) is one of the most widely studied real world problems of finding the shortest (minimum cost) possible route that visits each node in a given set of nodes (cities) once and then returns to origin city. The optimization of cuboid areas has potential samples that can be adapted to real world. Cuboid surfaces of buildings, rooms, furniture etc. can be given as examples. Many optimization algorithms have been used in solution of optimization problems at present. Among them, meta-heuristic algorithms come first. In this study, ant colony optimization, one of meta-heuristic methods, is applied to solve Euclidian TSP consisting of nine different sized sets of nodes randomly placed on a cuboid surface. The performance of this method is shown in tests.展开更多
基金supported by the National Natural Science Foundation of China(6113200291338101+3 种基金91338108)the National S&T Major Project(2011ZX03004-001-01)the Research Fund of Tsinghua University(2011Z05117)the Co-innovation Laboratory of Aerospace Broadband Network Technology
文摘Due to the limited transmission resources for data relay in the tracking and data relay satellite system （TDRSS）, there are many job requirements in busy days which will be discarded in the conventional job scheduling model. Therefore, the improvement of scheduling efficiency in the TDRSS can not only help to increase the resource utilities, but also to reduce the scheduling failure ratio. A model of nonhomogeneous parallel machines scheduling problems with time window （NPM-TW） is firstly built up for the TDRSS, considering the distinct features of the variable preparation time and the nonhomogeneous transmission rates for different types of antennas on each tracking and data relay satellite （TDRS）. Then, an adaptive subsequence adjustment （ASA） framework with evolutionary asymmetric path-relinking （EvAPR） is proposed to solve this problem, in which an asymmetric progressive crossover operation is involved to overcome the local optima by the conventional job inserting methods. The numerical results show that, compared with the classical greedy randomized adaptive search procedure （GRASP） algorithm, the scheduling failure ratio of jobs can be reduced over 11% on average by the proposed ASA with EvAPR.
基金The research was supported by the National Natural Science Foundation of China under Grant Nos. 61370183 and 61262011.
文摘The p-center problem consists of choosing a subset of vertices in an undirected graph as facilities in order to minimize the maximum distance between a client and its closest facility. This paper presents a greedy randomized adaptive search procedure with path-relinking （GRASP/PR） algorithm for the p-center problem, which combines both GRASP and path-relinking. Each iteration of GRASP/PR consists of the construction of a randomized greedy solution, followed by a tabu search procedure. The resulting solution is combined with one of the elite solutions by path-relinking, which consists in exploring trajectories that connect high-quality solutions. Experiments show that GRASP/PR is competitive with the state-of-the-art algorithms in the literature in terms of both solution quality and computational efficiency. Specifically, it virtually improves the previous best known results for 10 out of 40 large instances while matching the best known results for others.
文摘Traveling Salesman Problem (TSP) is one of the most widely studied real world problems of finding the shortest (minimum cost) possible route that visits each node in a given set of nodes (cities) once and then returns to origin city. The optimization of cuboid areas has potential samples that can be adapted to real world. Cuboid surfaces of buildings, rooms, furniture etc. can be given as examples. Many optimization algorithms have been used in solution of optimization problems at present. Among them, meta-heuristic algorithms come first. In this study, ant colony optimization, one of meta-heuristic methods, is applied to solve Euclidian TSP consisting of nine different sized sets of nodes randomly placed on a cuboid surface. The performance of this method is shown in tests.
