1998 | OriginalPaper | Buchkapitel
Using Surrogate Constraints in Genetic Algorithms for Solving Multidimensional Knapsack Problems
verfasst von : Christian Haul, Stefan Voß
Erschienen in: Advances in Computational and Stochastic Optimization, Logic Programming, and Heuristic Search
Verlag: Springer US
Enthalten in: Professional Book Archive
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
In the multidimensional knapsack problem (or multiconstraint zero-one knapsack problem) one has to decide on how to make efficient use of an entity which consumes multiple resources. The problem is known to be NP-hard, thus heuristics come into consideration for a solution. In this paper we investigate genetic algorithms as a solution approach. Surrogate constraints are generated by several different methods and are utilized as one of the stages in genetic algorithms for solving the multidimensional knapsack problem. This approach as a standalone method does not improve results but in conjunction with a greedy local search strategy results may be improved for problem instances with small object-constraint ratio.