Combining Variants of Iterative Flattening Search (Articolo in rivista)

Type
Label
  • Combining Variants of Iterative Flattening Search (Articolo in rivista) (literal)
Anno
  • 2008-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1016/j.engappai.2008.03.003 (literal)
Alternative label
  • Oddi, A., Cesta, A., Policella, N., and Smith, S.F. (2008)
    Combining Variants of Iterative Flattening Search
    in Engineering applications of artificial intelligence
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Oddi, A., Cesta, A., Policella, N., and Smith, S.F. (literal)
Pagina inizio
  • 283 (literal)
Pagina fine
  • 290 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.sciencedirect.com/science/article/pii/S0952197608000328 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 21(5) (literal)
Rivista
Note
  • Scopu (literal)
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Cesta, Oddi -- CNR Policella -- ESA, European Space Agency, Darmstadt, Germany Smith -- CMU, Carnegie Mellon University (USA) (literal)
Titolo
  • Combining Variants of Iterative Flattening Search (literal)
Abstract
  • Iterative flattening search (IFS) is an iterative improvement heuristic schema for makespan minimization in scheduling problems. Given an initial solution, IFS iteratively interleaves a relaxation-step, which randomly retracts some search decisions, and an incremental solving step (or flattening-step) to recompute a new solution. The process continues until a stop condition is met and the best solution found is returned. In recent work we have created a uniform software framework to analyze component techniques that have been proposed in IFS approaches. In this paper we combine basic components to obtain hybrid variants and perform a detailed experimental evaluation of their performance. Specifically, we examine the utility of: (1) operating with different relaxation strategies and (2) using different searching strategies to built a new solution. We present a two-step experimental evaluation: (a) an extensive explorative evaluation with a spectrum of parameter combination; (b) a time-intensive evaluation of the best IFS combinations emerged from the previous. The experimental results shed light on weaknesses and strengths of the different variants improving the current understanding of this family of meta-heuristics. (C) 2008 Elsevier Ltd. All rights reserved. (literal)
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