Some contributions to the adaptation of discrete metaheuristics for continuous optimization

Summary form only given. In this tutorial, we firstly present the general frame of "difficult" continuous optimization: after a short description of a few typical applications, we point out the difficulties peculiar to continuous problems. Then we describe some pitfalls of adapting metaheuristics to continuous variable problems. In a second part, we present, as an illustration, the methods that we have proposed to adapt some metaheuristics: simulated annealing, tabu search, genetic algorithms and ant colony algorithms. We outline some perspectives or works in progress, particularly dealing with particle swarm optimization. Lastly, we show, as an example, an application in the field of biomedical engineering of a continuous ant colony algorithm: the registration of retinal angiograms.