HSF: The iOpt’s Framework to Easily Design Metaheuristic Methods
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.
KeywordsHeuristic search framework Local search Evolutionary algorithms Hybrid algorithms iOpt.
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- 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
- 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
- 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
- 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
- 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