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Swarm Intelligence Metaheuristics Bioinformatics Business Intelligence
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Metaheuristics

Metaheuristics are widely used to solve important practical optimization problems. A metaheuristic can be seen as a general algorithmic framework which can be applied to different optimization problems with relatively few modifications to make them adapted to a specific problem. Examples of metaheuristics include ant colony optimization, evolutionary computation, iterated local search, simulated annealing, and tabu search.

Our research focus is on specific metaheuristics like ant colony optimization or iterated local search, and on their application to NP-hard and to continuous optimization problems. We are particularly interested in optimization problems which are dynamic, multi-objective and stochastic. A central point in our research is the application of a sound experimental methodology and the development of tools for the empirical study and configuration of metaheuristics.

Metaheuristics are an important class of algorithmic methods belonging to the stochastic local search framework. We are particularly interested in developing an engineering methodology for the design and implementation of stochastic local search algorithms and, hence, also of metaheuristics.

Projects

List of the relevant Iridia projects in the thematic area:

  • ANTS: Towards a Foundation of Ant Algorithm
  • COMEX: Combinatorial Optimization: Metaheuristics and Exact Methods
  • COMP2SYS: COMPutational intelligence methods for COMPlex SYStems
  • MetaHeuristics: Metaheuristics network
  • MIBISOC: Medical Imaging Using Bio-inspired and Soft Computing

Publications

List of publications concerning this thematic area:

Links


Updated: 2017-03-27