EasyLocal++ is a framework for modeling and solving combinatorial optimization problems through local search metaheuristics. It is entirely written in C++ and extensively uses template metaprogramming to separate concerns and improve performance.
Typically, to solve a problem, it is sufficient to implement the necessary methods to compute the problem-specific cost function and to enumerate the problem-specific local search moves. The framework takes care of calling the user-defined hook methods to solve the problem using one of the implemented meta-heuristics (e.g. simulated annealing, tabu search, hill climbing, ...).
This repository contains the last iteration (currently 4.0) of the EasyLocal++ framework. A seed project to use as a starting point for EasyLocal++ projects will soon be available.
The build system of EasyLocal++ is based on CMake (Cross Platform Make). CMake allows one to generate build scripts for most platforms and development environments, including Unix Makefiles, Visual Studio, Xcode, Eclipse, etc. (see CMake Generators for more information on the supported IDEs). The framework is available in the form of a header-only library. Therefore, no compilation is needed. The CMakeLists.txt will provide for suitable compiler options, including coroutines and concepts.
In general, to avoid mixing temporary CMake files with the project sources, it is advised to create a dedicated build subdirectory where all the building activities happen.
We have invested a lot of time and effort in creating EasyLocal++; if you find this software useful and if you use it for research purposes, we would be grateful if you could cite EasyLocal++ in your publications.
The reference papers about EasyLocal++ are:
- Luca Di Gaspero and Andrea Schaerf. EasyLocal++: An object-oriented framework for flexible design of local search algorithms. Software — Practice & Experience, 33(8):733–765, July 2003.
- Luca Di Gaspero and Andrea Schaerf. Writing local search algorithms using EasyLocal++. In S. Voß and D.L. Woodruff, editors, Optimization Software Class Libraries, OR/CS. Kluwer Academic Publisher, Boston (MA), USA, 2002.
Copyright (c) 2001-2024 Sara Ceschia, Francesca Da Ros, Luca Di Gaspero, Andrea Schaerf, Tommaso Urli - University of Udine, Italy
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