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Iterated Greedy

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Handbook of Heuristics

Abstract

Iterated greedy is a search method that iterates through applications of construction heuristics using the repeated execution of two main phases, the partial destruction of a complete candidate solution and a subsequent reconstruction of a complete candidate solution. Iterated greedy is based on a simple principle, and methods based on this principle have been proposed and published several times in the literature under different names such as simulated annealing, iterative flattening, ruin-and-recreate, large neighborhood search, and others. Despite its simplicity, iterated greedy has led to rather high-performing algorithms. In combination with other heuristic optimization techniques such as a local search, it has given place to state-of-the-art algorithms for various problems. This paper reviews the main principles of iterated greedy algorithms, relates the basic technique to the various proposals based on this principle, discusses its relationship with other optimization techniques, and gives an overview of problems to which iterated greedy has been successfully applied.

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Notes

  1. 1.

    Ruin-and-recreate is protected by US patent Optimization with ruin recreate No. 6418398; see http://www.patentstorm.us/patents/6418398-fulltext.html.

  2. 2.

    Note that the number of solution components removed in a destruction step may be different from the number of solution components added in the construction step and so we refrain from talking of k-exchange neighborhoods here. A common example where this happens is subset problems such as the SCP we discussed earlier.

  3. 3.

    Various applications of iterative flattening to scheduling problems have been referenced in section “IG Applications: Historical Development”.

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Acknowledgements

This work received support from the COMEX project (P7/36) within the Interuniversity Attraction Poles Programme of the Belgian Science Policy Office. Thomas Stützle acknowledges support from the Belgian F.R.S.-FNRS, of which he is a research director. Rubén Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness, under the project “SCHEYARD – Optimization of Scheduling Problems in Container Yards” (No. DPI2015-65895-R) financed by FEDER funds.

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Authors and Affiliations

  1. IRIDIA, Université Libre de Bruxelles (ULB), Brussels, Belgium

    Thomas Stützle

  2. Grupo de Sistemas de Optimización Aplicada, Instituto Tecnológico de Informática, Ciudad Politécnica de la Innovación, Edificio 8G, Acc. B. Universitat Politécnica de València, València, Spain

    Rubén Ruiz

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  1. Thomas Stützle
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Correspondence to Thomas Stützle .

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  1. Statistics and Operations Research Department, University of Valencia, Valencia, Spain

    Rafael Martí

  2. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  3. Amazon.com, Inc. and University of Washington, Seattle, Washington, USA

    Mauricio G. C. Resende

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Stützle, T., Ruiz, R. (2018). Iterated Greedy. In: Martí, R., Pardalos, P., Resende, M. (eds) Handbook of Heuristics. Springer, Cham. https://doi.org/10.1007/978-3-319-07124-4_10

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