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

Tetsuo Kawamura, Yoshihiko Horio, Mikio Hasegawa

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)592-599
Number of pages8
JournalIEEJ Transactions on Electronics, Information and Systems
Volume131
Issue number3
DOIs
Publication statusPublished - 2011
Externally publishedYes

Keywords

  • Chaotic neural network
  • High-dimensional chaotic dynamics
  • Mutual information
  • QAP
  • Tabu search

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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