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

  • Raphaël Dorne
  • Christos Voudouris
Part of the Applied Optimization book series (APOP, volume 86)


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.


Heuristic search framework Local search Evolutionary algorithms Hybrid algorithms iOpt. 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 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
  2. 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
  3. E. Bonsma, M. Shackleton, and R. Shipman. EOS: an evolutionary and ecosystem research platform. BT Technology Journal, 18: 24–31, 2000.CrossRefGoogle Scholar
  4. 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.CrossRefGoogle Scholar
  5. 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.CrossRefGoogle Scholar
  6. 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
  7. 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
  8. 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.zbMATHCrossRefGoogle Scholar
  9. 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
  10. L. Michel and P. Van Hentenryck. Localizer: A modeling language for local search. INFORMS Journal of Computing, 11: 1–14, July 1999.zbMATHCrossRefGoogle Scholar
  11. Taligent Inc. Leveraging object-oriented frameworks. A Taligent White Paper,1993.Google Scholar
  12. S. Voß and D. Woodruff, editors. Optimization Software Class Libraries. OR/CS Interfaces Series. Kluwer Academic Publishers, Boston, 2002.Google Scholar
  13. 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
  14. 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

Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Raphaël Dorne
    • 1
  • Christos Voudouris
    • 1
  1. 1.Intelligent Complex Systems Research GroupBTexact TechnologiesSuffolkUK

Personalised recommendations