A Framework for Alternation of Meta-heuristic Algorithms - Malek, Richard
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This work presents a new approach based on the alternation of meta-heuristic algorithms solving the problems of combinatorial optimization. In accordance with the described proposal, a framework realizing the alternation have been designed and implemented. The functionality of the framework and the validity of implemented problems have been proved. It has been turned out that proposed approach improves the convergence and the quality of found solutions in many cases. The main advantage of the framework is good scalability and extensibility for other algorithms and problems as well.

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Produktbeschreibung
This work presents a new approach based on the alternation of meta-heuristic algorithms solving the problems of combinatorial optimization. In accordance with the described proposal, a framework realizing the alternation have been designed and implemented. The functionality of the framework and the validity of implemented problems have been proved. It has been turned out that proposed approach improves the convergence and the quality of found solutions in many cases. The main advantage of the framework is good scalability and extensibility for other algorithms and problems as well.
Autorenporträt
Richard Malek gained his master degree in computer science at Czech Technical University in Prague, Czech Republic. Currently he is a PhD student in Artificial Intelligence at the same university. His research interests are: optimization problems, meta-heuristics, multi-agent systems and many others.