Abstract. Hydrological phenomena are often very dynamic and depend on numerous criteria. The STAFF software is an adaptive model for flood forecast based on self-organizing multiagent systems. It is operational since 2002 in the Midi-Pyrenees region in France. The aim of this paper is to show the relevance of our approach to model complex natural systems by focusing on the results, architecture and self-organization mechanisms of a real world application. The main idea is to let the artificial system self-adapt towards the adequate model by confronting it to real data, thus ensuring that the resulting model represents reality. Moreover, since the MAS is constantly adapting, we obtain a dynamic and autonomous system that can take into account any future dynamics (strong perturbations, sensor breakdowns…) and able to provide decision-makers with usable information anytime.

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MultiDisciplinary Optimization (MDO) problems represent one of the hardest and broadest domains of continuous optimization. By involving both the models and criteria of different disciplines, MDO problems are often too complex to be tackled by classical optimization methods. We propose an approach which takes into account this complexity using a new representation (NDMO-Natural Domain Modeling for Optimization) and a self-adaptive multi-agent algorithm. Our method agentifies the different elements of the problem (such as the variables, the models, the objectives). Each agent is in charge of a small part of the problem and cooperates with others to find equilibrium on conflicting values. Despite the fact that no agent of the system has a complete view of the entire problem, the mechanisms we provide allow the emergence of a coherent solution. Evaluations on several academic and industrial test cases are provided.

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