Algorithme de colonies de fourmis

Les algorithmes de colonies de fourmis (, ou ACO) sont des algorithmes inspirés du comportement des fourmis, ou d'autres espèces formant un superorganisme, et qui constituent une famille de métaheuristiques d’optimisation. Initialement proposé par Marco Dorigo dans les années 1990, pour la recherche de chemins optimaux dans un graphe, le premier algorithme s’inspire du comportement des fourmis recherchant un chemin entre leur colonie et une source de nourriture. L’idée originale s'est depuis diversifiée pour résoudre une classe plus large de problèmes et plusieurs algorithmes ont vu le jour, s’inspirant de divers aspects du comportement des fourmis. En anglais, le terme consacré à la principale classe d’algorithme est (ACO). Les spécialistes réservent ce terme à un type particulier d'algorithme. Il existe cependant plusieurs familles de méthodes s'inspirant du comportement des fourmis. En français, ces différentes approches sont regroupées sous les termes : , , ou diverses combin

Ant colony optimization for the single model U-type assembly line balancing problem

Arda Alp

An assembly line is a production line in which units move continuously through a sequence of stations. The assembly line balancing problem is defined as the allocation of tasks to an ordered sequence of stations subject to precedence constraints with the objective of optimizing a performance measure. In this paper, we propose ant colony algorithms to solve the single-model U-type assembly line balancing problem. We conduct an extensive experimental study in which the performance of the proposed algorithm is compared against best known algorithms reported in the literature. The results indicate that the proposed algorithms display very competitive performance against them. (C) 2009 Elsevier B.V. All rights reserved.

2009

Extension d'algorithmes d'ordre zéro pour l'optimisation stochastique

Pierre Huguenin

The electrical discharge machining process (EDM) was discovered in the 1950s, and was then used essentially to destroy unrecoverable damaged screws. Since then, huge progress has been achieved in making this process reliable and able to perform the most complex machining operations on the most sophisticated materials. Two main processes use electrical discharge machining. First, Die Sinking EDM (DSEDM) in which an electrode, moved along a usually vertical axis, makes an imprint into a mechanical element ; second, Wire EDM (WEDM), uses a wire as an electrode and makes it possible to perform cut operations. Two specific aspects of the EDM process make it particularly challenging for optimization. First, the process evolves with the machining position. In the DSEDM process, where the electrode sinks deeply into the material, the fragments spawn by erosion (contamination) are trapped, thus modifying the sparking conditions. In the WEDM process, the main factors that drive the evolution of the process are the machining operations. Second, the measurements are very noisy, which is due to underlying, mainly random, physical phenomenon ; this is particularly true to spark triggering. The process evolution influences the single criterion to be minimized : total machining time. However, the latter is only known once the operations are completed. As a result, the whole history of manipulated variables influences the final criterion to minimize in this case of dynamic optimization. A major contribution of this work is a proof that a first-order model of the DSEDM process, with the machining position as a state variable, makes it possible to transform a dynamic optimization problem into a static optimization problem. The tools used in this demonstration are Pontryagin's Minimum Principle as well as Parametric Programming. The conclusion is that in order to achieve minimum machining time, maximum speed must be sought all along the trajectory. The online search for maximum speed is another important contribution of this thesis. Noisy efficiency functions are, indeed, known to be a significant challenge to the reliability of optimization algorithms. To address this issue the Golden Ratio Search and the Nelder-Mead Simplex algorithms were chosen as the starting point, as they do not rely explicitly on the gradient of the efficiency function. The addition of a further dilation condition makes these algorithms more effective in stochastic mode. This condition is based on the detection of contradictory measurement samples as compared to the shape of the efficiency function, which is assumed to be unimodal. As a result of this modification, the density of the final optimization points is well centred relative to the theoretical optimum, and dispersion is small. Moreover, the size of the search region for both algorithms never approaches zero. Consequently, as the machining conditions evolve, the optimizer can target a new optimum. This adaptability proves to be a significant improvement over existing algorithms. In the case of the DSEDM process, a simple model of the efficiency surface as a function of the control variables, which has been calibrated on sample measures, has allowed for a validation of the static optimization. For the WEDM process, conclusive results for the modified algorithms have been obtained both by simulation and during machine-based tests.

EPFL2005

Extension d'algorithmes d'ordre zéro pour l'optimisation stochastique

Pierre Huguenin

EPFL2005

Algorithme de colonies de fourmis

Ant colony optimization for the single model U-type assembly line balancing problem

Extension d'algorithmes d'ordre zéro pour l'optimisation stochastique

Optimisation des stratégies d’exposition pour le frittage sélectif par laser

Ant colony optimization for the single model U-type assembly line balancing problem

Extension d'algorithmes d'ordre zéro pour l'optimisation stochastique

Optimisation des stratégies d’exposition pour le frittage sélectif par laser