Open Metaheuristic (oMetah) is a library aimed at the conception of metaheuristics (i.e. genetic/evolutionnary algorithms, tabu search, simulated annealing, ant colony algorithms, etc.). Open Metaheuristic library follows the "adaptive learning search" approach in the design of metaheuristics (an approach inspired from the "adaptive memory programming").

The oMetah core code design is separated in three components : algorithms, problem and communication. You can easily add your own metaheuristic and test it on the implemented problems, or add your own problem, and use the implemented metaheuristics to solve it.

You also have the choice of the way the algorithm and the problem are linked (static link, network, webservice, etc.).

It is mainly designed to handle continuous or mixed-integer problems, often met in engineering applications.

oMetah is written in C++, using the Standard Template Library.

What's New in This Release:

· The Python and network communication protocols between metaheuristics and problems are now usable.



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