Optimization by Move--Class Deflation

Reimer Kuehn; Yu-Cheng Lin; Gerhard Poeppel

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Optimization by Move--Class Deflation

Reimer Kuehn·Yu-Cheng Lin·Gerhard Poeppel

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Abstract

A new approach to combinatorial optimization based on systematic move-class deflation is proposed. The algorithm combines heuristics of genetic algorithms and simulated annealing, and is mainly entropy-driven. It is tested on two problems known to be NP hard, namely the problem of finding ground states of the SK spin--glass and of the 3-$D$ $\pm J$ spin-glass. The algorithm is sensitive to properties of phase spaces of complex systems other than those explored by simulated annealing, and it may therefore also be used as a diagnostic instrument. Moreover, dynamic freezing transitions, which are well known to hamper the performance of simulated annealing in the large system limit are not encountered by the present setup.

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BibTeX RIS

Reimer Kuehn, Yu-Cheng Lin, Gerhard Poeppel. 1998-05-12. Optimization by Move--Class Deflation. https://arxiv.org/abs/cond-mat/9805137

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