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International audienc

In this paper, we study the problem offinding the Origin-Destination (0-0) shortest path in intermodal transportation networks, aiming at minimizing the travel time. The transportation network and the corresponding data are modeled by means of a multi-label graph. The intermodal shortest path problem and its definition are briefly described. The algorithm developed to find the path is presented, especially by detailing o label correcting approach that updotes some labels associated with the graph nodes. The implementafion of this approach and the results it provides show its va1idiQ.

Abstract. This paper proposes a new multi-objective genetic algorithm, called GAME, to solve constrained optimization problems. GAME uses an elitist archive, but it ranks the population in several Pareto fronts. Then, three types of fitness assignment methods are defined: the fitness of individuals depends on the front they belong to. The crowding distance is also used to preserve diversity. Selection is based on two steps: a Pareto front is first selected, before choosing an individual among the solutions it contains. The probability to choose a given front is computed using three parameters which are tuned using the design of experiments. The influence of the number of Pareto fronts is studied experimentally. Finally GAME's performance is assessed and compared with three other algorithms according to the conditions of the CEC 2009 competition.

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