title: Lambda-opt neural approaches to quadratic assignment problems.
abstract: In this article, we propose new analog neural approaches to combinatorial optimization problems, in particular, quadratic assignment problems (QAPs). Our proposed methods are based on an analog version of the lambda-opt heuristics, which simultaneously changes assignments for lambda elements in a permutation. Since we can take a relatively large lambda value, our new methods can achieve a middle-range search over possible solutions, and this helps the system neglect shallow local minima and escape from local minima. In experiments, we have applied our methods to relatively large-scale (N = 80 - 150) QAPs. Results have shown that our new methods are comparable to the present champion algorithms; for two benchmark problems, they are obtain better solutions than the previous champion algorithms.
authors.affiliation: Nara Institute of Science and Technology, Ikoma-shi, Nara, 630-0101 Japan and ATR Human Information Processing Research Laboratories, Soraku-gun, Kyoto, 619-0288 Japan.
authors.forename: S
authors.initials: S
authors.lastname: Ishii
authors.forename: H
authors.initials: H
authors.lastname: Niitsuma
mesh_terms: D000465:Algorithms; D008959:Models, Neurological; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011336:Probability