Proceedings of the KIEE Conference (대한전기학회:학술대회논문집)
- 1995.07b
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- Pages.983-985
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- 1995
Self Organizing Feature Map Type Neural Computation Algorithm for Travelling Salesman Problem
SOFM(Self-Organizing Feature Map)형식의 Travelling Salesman 문제 해석 알고리즘
- Seok, Jin-Wuk (Dept. of Electrical & Control Eng., Hong Ik University) ;
- Cho, Seong-Won (Dept. of Electrical & Control Eng., Hong Ik University) ;
- Choi, Gyung-Sam (Dept. of Electrical & Control Eng., Hong Ik University)
- Published : 1995.07.20
Abstract
In this paper, we propose a Self Organizing Feature Map (SOFM) Type Neural Computation Algorithm for the Travelling Salesman Problem(TSP). The actual best solution to the TSP problem is computatinally very hard. The reason is that it has many local minim points. Until now, in neural computation field, Hopield-Tank type algorithm is widely used for the TSP. SOFM and Elastic Net algorithm are other attempts for the TSP. In order to apply SOFM type neural computation algorithms to the TSP, the object function forms a euclidean norm between two vectors. We propose a Largrangian for the above request, and induce a learning equation. Experimental results represent that feasible solutions would be taken with the proposed algorithm.
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