Optimizing large-scale problems by combining chaotic neural network and self-organizing feature map
Xiu-Hong Wang
Qing-Li Qiao
Zheng-Ou Wang
Inst. of Syst. Eng., Tianjin Univ., China;
Abstract
A novel approach using transient chaotic neural network (TCNN) and self-organizing feature map (SOFM) process to solve large-scale combinatorial optimization problems has been proposed. With the clustering function of self-organizing feature map, the computational cost of a large-scale combinatorial optimization problem solved by TCNN is reduced. Numerical simulation of TSP shows that the proposed method is effective to solve large-scale optimization problems.
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