HSF: A Generic Framework to Easily Design Meta-Heuristic Methods
نویسندگان
چکیده
For some years now, Meta-Heuristic methods have demonstrated their ability to tackle large-scale optimisation 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 evaluation 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.
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