资 源 简 介
Abstract
The Quadratic Assignment Problem (QAP) is one of the most complex NP-Hard combinatorial optimization problems remaining intractable for n>30. However, due to its applicability to many important scientific domains, many meta-heuristic approaches have been successfully implemented. The Guided Local Search (GLS) is one such meta-heuristic that guides a local search over a modified solution landscape induced by an augmented objective function. This paper details a series of enhancements to the basic GLS and is shown to be competitive if not better than GLS.
Conclusion
