MIC’2001 - 4th Metaheuristics International Conference 423 HSF: A Generic Framework to Easily Design Meta-Heuristic Methods Rapha¨ el Dorne * Christos Voudouris * * Intelligent Complex Systems Research Group BT Laboratories, Adastral Park, PP12/MLB1, Martlesham Heath Ipswich, IP5 3RE, Suffolk, United Kingdom Email: {raphael.dorne@bt.com,chris.voudouris@bt.com} 1 Introduction For some years now, Meta-Heuristic methods have demonstrated their ability to tackle large-scale opti- misation problems. Up to now, several frameworks have been implemented for this family of methods. Some of them are either dedicated to Local Search such as EasyLocal++[5], Localizer[8], LocalSearch framework[1], Templar[7], HotFrame[4] or to Evolutionary Computation such as EOS[2], EASEA[3]. These tend to provide templates with the user having to define, for each problem addressed, the move operators and/or evolutionary operators with the need to construct tedious and hard-code move eval- uation mechanisms. Furthermore, because of their structure most of the frameworks are limited in the choice of techniques provided. Therefore, it appears difficult using such existing frameworks to model efficient Meta-Heuristic methods as Hybrid Methods, combining evolutionary algorithms with local search methods. Such methods require more generalisation and more flexibility. Thus the main motivation behind the Heuristic Search Framework is to develop a Java object-oriented framework allowing to combine population and single solution algorithms offering generalisation, flexi- bility and efficiency in order to tackle academic problems and real applications. 2 Heuristic Search Framework The Heuristic Search Framework (HSF) was created as a generic framework for the family of optimisa- tion techniques known as Heuristic Search. The motivation here is to provide representation of existing methods, retain flexibility to build new ones and use the generalisation so as to avoid re-implementing concepts that have already been created in HSF. Three main concepts are the basis of HSF: Heuristic Search, Heuristic Solution and Heuristic Problem. • Heuristic Search is the concept used to define a search algorithm. • Heuristic Solution is the solution representation of an optimisation problem manipulated inside HSF. At the present moment, a vector of variables and a set of sequences are readily available. • Heuristic Problem is only an interface between an optimisation problem and HSF. In HSF, we make use of the realisation that many Heuristic Search algorithms can be broken down into a plurality of constituent parts, and that at least some of those parts are common between algorithms. Porto, Portugal, July 16-20, 2001