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HSF: The iOpt’s Framework to Easily Design Metaheuristic Methods

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Part of the book series: Applied Optimization ((APOP,volume 86))

Abstract

The Heuristic Search Framework (HSF) is aJava object-oriented framework allowing to easily implement single solution algorithms such as Local Search, population-based algorithms such as Genetic Algorithms, and hybrid methods being a combination of the two. The main idea in HSF is to break down any of these heuristic algorithms into a plurality of constituent parts. Thereafter, a user can use this library of parts to build existing or new algorithms. The main motivation behind HSF is to provide a “well-designed” framework dedicated to heuristic methods in order to offer representation of existing methods and to retain flexibility to build new ones. In addition, the use of the infra-structure of the framework avoid the need to re-implement parts that have already been incorporated in HSF and reduces the code necessary to extend existing components.

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Bibliography

  • A. Andreatta, S. Carvalho, and C. Ribeiro. An object-oriented framework for local search heuristics. In Proceedings of TOOLS USA’98, pages 33–45, 1998.

    Google Scholar 

  • J.E. Baker. Reducing bias and inefficiency in the selection algorithm. In John J Grefenstette, editor, 2nd International Conference on Genetic Algorithms, pages 14–21. Lawrence Erlbaum Associates, 1987.

    Google Scholar 

  • E. Bonsma, M. Shackleton, and R. Shipman. EOS: an evolutionary and ecosystem research platform. BT Technology Journal, 18: 24–31, 2000.

    Article  Google Scholar 

  • P. Collet, E. Lutton, M. Schoenauer, and J. Louchet. Take it EASEA. In Marc Schoenauer, Kalyanmoy Deb, Gunter Rudolph, Xin Yao, Evelyne Lutton, Juan Julian Merelo, and Hans-Paul Schwefel, editors, Parallel Problem Solving from Nature–PPSN VI, pages 891–901, Berlin, 2000. Springer.

    Chapter  Google Scholar 

  • R. Dorne and J.K. Hao. A new genetic local search algorithm for graph coloring. In Agoston E. Eiben, Thomas Bäck, Marc Schoenauer, and Hans-Paul Schwefel, editors, Parallel Problem Solving from Nature–PPSN V, pages 745–754, Berlin, 1998. Springer.

    Chapter  Google Scholar 

  • A. Fink and S. Voß. Hotframe: A heuristic optimization framework. In S. Vol? and D. Woodruff, editors, Optimization Software Class Libraries, OR/CS Interfaces Series, pages 81–154. Kluwer Academic Publishers, Boston, 2002.

    Google Scholar 

  • L.D. Di Gaspero and A. Schaerf. Easylocal++: An object-oriented framework for flexible design of local search algorithms. Technical Report UDMI/13/2000/RR, Università degli Studi di Udine, 2000.

    Google Scholar 

  • D.S. Johnson, C.R. Aragon, L.A. McGeoch, and C. Schevon. Optimization by simulated annealing: An experimental evaluation; part ii, graph coloring and number partitioning. Operations Research, 39 (3): 378–406, 1991.

    Article  MATH  Google Scholar 

  • M. Jones, G. McKeown, and V. Rayward-Smith. Templar: An object oriented framework for distributed combinatorial optimization. In UNICOM Seminar on Modern Heuristics for Decision Support, 1998.

    Google Scholar 

  • L. Michel and P. Van Hentenryck. Localizer: A modeling language for local search. INFORMS Journal of Computing, 11: 1–14, July 1999.

    Article  MATH  Google Scholar 

  • Taligent Inc. Leveraging object-oriented frameworks. A Taligent White Paper,1993.

    Google Scholar 

  • S. Voß and D. Woodruff, editors. Optimization Software Class Libraries. OR/CS Interfaces Series. Kluwer Academic Publishers, Boston, 2002.

    Google Scholar 

  • C. Voudouris and R. Dome. Integrating heuristic search and one-way constraints in the iopt toolkit. In S. Voß and D. Woodruff, editors, Optimization Software Class Libraries, OR/CS Interfaces Series, pages 177–191. Kluwer Academic Publishers, Boston, 2002.

    Google Scholar 

  • C. Voudouris, R. Dome, D. Lesaint, and A. Liret. iOpt: A software toolkit for heuristic search methods. In Springer-Verlag, editor, 7th International Conference on Principles and Practice of Constraint Programming (CP2001), Paphos, Cyprus, 2001.

    Google Scholar 

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Dorne, R., Voudouris, C. (2003). HSF: The iOpt’s Framework to Easily Design Metaheuristic Methods. In: Metaheuristics: Computer Decision-Making. Applied Optimization, vol 86. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-4137-7_11

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  • DOI: https://doi.org/10.1007/978-1-4757-4137-7_11

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-5403-9

  • Online ISBN: 978-1-4757-4137-7

  • eBook Packages: Springer Book Archive

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