In this paper, we discuss the relationship between the sparse symmetric Broyden (SPSB) method [1, 2] and m-time secant-like multi-projection (SMP) method [3] and prove that when m goes to infinity, the SMP method is c...In this paper, we discuss the relationship between the sparse symmetric Broyden (SPSB) method [1, 2] and m-time secant-like multi-projection (SMP) method [3] and prove that when m goes to infinity, the SMP method is corresponding to the SPSB method.展开更多
Multi-project multi-site location problems are multi-objective combinational optimization ones with discrete variables which are hard to solve. To do so, the case of particle swarm optimization is considered due to it...Multi-project multi-site location problems are multi-objective combinational optimization ones with discrete variables which are hard to solve. To do so, the case of particle swarm optimization is considered due to its useful char- acteristics such as easy implantation, simple parameter settings and fast convergence. First these problems are trans- formed into ones with continuous variables by defining an equivalent probability matrix in this paper, then multi-objective particle swarm optimization based on the minimal particle angle is used to solve them. Methods such as continuation of discrete variables, update of particles for matrix variables, normalization of particle position and evalua- tion of particle fitness are presented. Finally the efficiency of the proposed method is validated by comparing it with other methods on an eight-project-ten-site location problem.展开更多
文摘In this paper, we discuss the relationship between the sparse symmetric Broyden (SPSB) method [1, 2] and m-time secant-like multi-projection (SMP) method [3] and prove that when m goes to infinity, the SMP method is corresponding to the SPSB method.
基金Project 60304016 supported by the Nationa Natural Science Foundation of China
文摘Multi-project multi-site location problems are multi-objective combinational optimization ones with discrete variables which are hard to solve. To do so, the case of particle swarm optimization is considered due to its useful char- acteristics such as easy implantation, simple parameter settings and fast convergence. First these problems are trans- formed into ones with continuous variables by defining an equivalent probability matrix in this paper, then multi-objective particle swarm optimization based on the minimal particle angle is used to solve them. Methods such as continuation of discrete variables, update of particles for matrix variables, normalization of particle position and evalua- tion of particle fitness are presented. Finally the efficiency of the proposed method is validated by comparing it with other methods on an eight-project-ten-site location problem.
