Constrained neural approaches to quadratic assignment problems.
In this paper, we discuss analog neural approaches to the quadratic assignment problem (QAP). These approaches employ a hard constraints scheme to restrict the domain space, and are able to obtain much improved solutions over conventional neural approaches. Since only a few strong heuristics for QAP have been known to date, our approaches are good alternatives, capable of obtaining fairly good solutions in a short period of time. Some of them can also be applied to large-scale problems, say of size N>/=300.
Ishii
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Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma-shi, Nara, Japan
Sato
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