The purpose of this work is that of presenting a version of the Reactive Tabu Search method (RTS) that is suitable for constrained problems, and that of testing RTS on a series of constrained and unconstrained Combinatorial Optimization tasks. The benchmark suite consists of many instances of the N-K model and of the Multiknapsack problem with various sizes and difficulties, defined with portable random number generators. The performance of RTS is compared with that of Repeated Local Minima Search, Simulated Annealing, Genetic Algorithms, and Neural Networks. In addition, the effects of different hashing schemes and of the presence of a simple "aspiration" criterion in the RTS algorithm are investigated
Local Search with Memory: Benchmarking RTS
1995-01-01
Abstract
The purpose of this work is that of presenting a version of the Reactive Tabu Search method (RTS) that is suitable for constrained problems, and that of testing RTS on a series of constrained and unconstrained Combinatorial Optimization tasks. The benchmark suite consists of many instances of the N-K model and of the Multiknapsack problem with various sizes and difficulties, defined with portable random number generators. The performance of RTS is compared with that of Repeated Local Minima Search, Simulated Annealing, Genetic Algorithms, and Neural Networks. In addition, the effects of different hashing schemes and of the presence of a simple "aspiration" criterion in the RTS algorithm are investigatedI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
