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Controlled Pool Maintenance for Metaheuristics

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Metaheuristic Optimization via Memory and Evolution

Part of the book series: Operations Research/Computer Science Interfaces Series ((ORCS,volume 30))

Abstract

Recent metaheuristic developments have proved to be successful especially in cases where their fundamental concepts are complemented with pool-oriented approaches such as scatter search. These algorithms maintain a reference set of high quality solutions which are repeatedly used during the search in order to guarantee a fruitful balance between diversification and intensification. However, maintaining such a pool is not a trivial undertaking. The main problem is to find a balance between the attempt to collect a number of high quality solutions, which often results in similar solution properties, and the need to guarantee a certain degree of diversity in the pool. In the present study we highlight the advantage of pool-oriented design while focusing on controlled input and output operations. We analyze the main pool components for metaheuristics and present a review of the literature on relevant strategies in tabu search, scatter search, and path relinking. Additionally, we provide a synthesis of the literature that highlights the critical aspects of effective pool maintenance.

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Author information

Authors and Affiliations

  1. Institut für Industrie und Fertigungswirtschaft, Karl-Franzens-University Graz, Universitätss-traße 15/G2, A-8010, Graz, Austria

    Peter Greistorfer

  2. Institute of Information Systems (Wirtschaftsinformatik), University of Hamburg, Von-Melle-Park 5, D-20146, Hamburg, Germany

    Stefan Voß

Authors
  1. Peter Greistorfer
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  2. Stefan Voß
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Editor information

Editors and Affiliations

  1. Oklahoma State University, USA

    Ramesh Sharda

  2. Universität Hamburg, Germany

    Stefan Voß

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© 2005 Kluwer Academic Publishers

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Greistorfer, P., Voß, S. (2005). Controlled Pool Maintenance for Metaheuristics. In: Sharda, R., Voß, S., Rego, C., Alidaee, B. (eds) Metaheuristic Optimization via Memory and Evolution. Operations Research/Computer Science Interfaces Series, vol 30. Springer, Boston, MA. https://doi.org/10.1007/0-387-23667-8_18

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