2007年
SMMH - A parallel heuristic for combinatorial optimization problems
COMPUTATION IN MODERN SCIENCE AND ENGINEERING VOL 2, PTS A AND B
- ,
- ,
- ,
- ,
- 巻
- 2
- 号
- 開始ページ
- 1195
- 終了ページ
- +
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- 出版者・発行元
- AMER INST PHYSICS
The process of finding one or more optimal solutions for answering combinatorial optimization problems bases itself on the use of algorithms instances. Those instances usually have to explore a very large search spaces. Heuristics search focusing on the use of High-Order Hopfield neural networks is a largely deployed technique for very large search space. It can be established a very powerful analogy towards the dynamics evolution of a physics spin-glass system while minimizing its own energy and the energy function of the network. This paper presents a new approach for solving combinatorial optimization problems through parallel simulations, based on a High-Order Hopfield neural network using MPI specification.
- リンク情報
- ID情報
-
- ISSN : 0094-243X
- Web of Science ID : WOS:000252602900294