A Genetic Algorithm for the Multidimensional Knapsack Problem
Journal of Heuristics
A hybrid approach for the 0-1 multidimensional knapsack problem
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Combinations of local search and exact algorithms
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
The core concept for the multidimensional knapsack problem
EvoCOP'06 Proceedings of the 6th European conference on Evolutionary Computation in Combinatorial Optimization
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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In this article we investigate a new variant of Variable Neighborhood Search (VNS): Relaxation Guided Variable Neighborhood Search. It is based on the general VNS scheme and a new Variable Neighborhood Descent (VND) algorithm. The ordering of the neighborhood structures in this VND is determined dynamically by solving relaxations of them. The objective values of these relaxations are used as indicators for the potential gains of searching the corresponding neighborhoods. We tested this new approach on the well-studied multidimensional knapsack problem. Computational experiments show that our approach is beneficial to the search, improving the obtained results. The concept is, in principle, more generally applicable and seems to be promising for many other combinatorial optimization problems approached by VNS.