2020-05-25T14:36:41Z
http://digital.csic.es/dspace-oai/request
oai:digital.csic.es:10261/164840
2018-08-02T08:36:17Z
com_10261_60
com_10261_4
col_10261_439
Lizarraga, Evelia
Blesa, Maria J.
Blum, Christian
2018-05-16T11:19:13Z
2018-05-16T11:19:13Z
2017-04-20
17th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2017; LNCS 10197: 60- 74 (2017)
http://hdl.handle.net/10261/164840
http://dx.doi.org/10.13039/501100002809
http://dx.doi.org/10.13039/501100003141
http://dx.doi.org/10.13039/501100003329
Both, Construct, Merge, Solve and Adapt (CMSA) and Large Neighborhood Search (LNS), are hybrid algorithms that are based on iteratively solving sub-instances of the original problem instances, if possible, to optimality. This is done by reducing the search space of the tackled problem instance in algorithm-specific ways which differ from one technique to the other. In this paper we provide first experimental evidence for the intuition that, conditioned by the way in which the search space is reduced, LNS should generally work better than CMSA in the context of problems in which solutions are rather large, and the opposite is the case for problems in which solutions are rather small. The size of a solution is hereby measured by the number of components of which the solution is composed, in comparison to the total number of solution components. Experiments are conducted in the context of the multi-dimensional knapsack problem. © Springer International Publishing AG 2017.
eng
closedAccess
Construct, merge, solve and adapt versus large neighborhood search for solving the multi-dimensional knapsack problem: Which one works better when?
artículo