Solving CSP by Lagrangian Method with Importance of Constraints
Takahiro Nakano, Masahiro Nagamatu · 2006
We proposed a neural network called LPPH-CSP for solving constraint satisfaction problem (CSP). The LPPH-CSP is not trapped by any point which is not a solution of the CSP, and it can update all neurons simultaneously. In this paper, we propose two methods to improve the efficiency of the LPPH-CSP. Though the LPPH-CSP can deal with several types of constraints of the CSP, it treats all constraints evenly. One of the proposed methods distinguishes the types of constraints for solving the CSP more efficiently. Another one of the proposed methods applies fast local search (FLS) to the LPPH-CSP. Experimental results show the effectiveness of our proposals.