• Documents
  • Authors
  • Tables
  • Log in
  • Sign up
  • MetaCart
  • DMCA
  • Donate

CiteSeerX logo

Advanced Search Include Citations
Advanced Search Include Citations

DMCA

Greedy Randomized Adaptive Search Procedures (2002)

Cached

  • Download as a PDF

Download Links

  • [www.optimization-online.org]
  • [www.research.att.com]
  • [www2.research.att.com]
  • [public.research.att.com]
  • [www2.research.att.com]
  • [www.research.att.com]
  • [www2.research.att.com]
  • [www2.research.att.com]
  • [www.research.att.com]
  • [www2.research.att.com]
  • [www.optimization-online.org]
  • [www2.research.att.com]
  • [www.research.att.com]
  • [www.research.att.com]
  • [www.optimization-online.org]
  • [www.research.att.com]
  • [www2.ic.uff.br]
  • [www.research.att.com]
  • [www2.research.att.com]
  • [www2.research.att.com]
  • [www2.research.att.com]

  • Other Repositories/Bibliography

  • DBLP
  • Save to List
  • Add to Collection
  • Correct Errors
  • Monitor Changes
by Mauricio G. C. Resende , Celso C. Ribeiro
Citations:644 - 82 self
  • Summary
  • Citations
  • Active Bibliography
  • Co-citation
  • Clustered Documents
  • Version History

BibTeX

@MISC{Resende02greedyrandomized,
    author = {Mauricio G. C. Resende and Celso C. Ribeiro},
    title = {Greedy Randomized Adaptive Search Procedures },
    year = {2002}
}

Share

Facebook Twitter Reddit Bibsonomy

OpenURL

 

Abstract

GRASP is a multi-start metaheuristic for combinatorial problems, in which each iteration consists basically of two phases: construction and local search. The construction phase builds a feasible solution, whose neighborhood is investigated until a local minimum is found during the local search phase. The best overall solution is kept as the result. In this chapter, we first describe the basic components of GRASP. Successful implementation techniques and parameter tuning strategies are discussed and illustrated by numerical results obtained for different applications. Enhanced or alternative solution construction mechanisms and techniques to speed up the search are also described: Reactive GRASP, cost perturbations, bias functions, memory and learning, local search on partially constructed solutions, hashing, and filtering. We also discuss in detail implementation strategies of memory-based intensification and post-optimization techniques using path-relinking. Hybridizations with other metaheuristics, parallelization strategies, and applications are also reviewed.

Keyphrases

greedy randomized adaptive search procedure    local search    basic component    numerical result    feasible solution    different application    post-optimization technique    parallelization strategy    successful implementation technique    local minimum    alternative solution construction mechanism    combinatorial problem    multi-start metaheuristic    detail implementation strategy    construction phase    reactive grasp    cost perturbation    memory-based intensification    local search phase    overall solution    bias function    parameter tuning strategy   

Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2019 The Pennsylvania State University