Mutual information analyses of neuron selection techniques in synchronous exponential chaotic tabu search for quadratic assignment problems

Tetsuo Kawamura, Yoshihiko Horio, Mikio Hasegawa

研究成果: Article査読

1 被引用数 (Scopus)

抄録

The tabu search was implemented on a neural network with chaotic neuro-dynamics. This chaotic exponential tabu search shows great performance in solving quadratic assignment problems (QAPs). To exploit inherent parallel processing abilities of analog hardware systems, a synchronous updating scheme, where all the neurons in the network are updated at the same time, was proposed. However, several neurons may fire simultaneously with the synchronous updating. As a result, we cannot determine only one candidate for the 2-opt exchange from the many fired neurons. To solve this problem, several neuron selection methods, which select one specific neuron among the fired neurons, were proposed. These neuron selection methods improved the performance of the synchronous updating scheme. In this paper, we analyze the dynamics of the chaotic neural network with the neuron selection methods by means of the spatial and temporal mutual information. Through the analyses, the network solution search dynamics of the exponential chaotic tabu search with different neuron selection methods are evaluated.

本文言語English
ページ(範囲)592-599
ページ数8
ジャーナルIEEJ Transactions on Electronics, Information and Systems
131
3
DOI
出版ステータスPublished - 2011
外部発表はい

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

フィンガープリント 「Mutual information analyses of neuron selection techniques in synchronous exponential chaotic tabu search for quadratic assignment problems」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル