Genetic algorithms and tabu search: hybrids for optimization
Computers and Operations Research - Special issue on genetic algorithms
Agent theories, architectures, and languages: a survey
ECAI-94 Proceedings of the workshop on agent theories, architectures, and languages on Intelligent agents
An experimental environment for exchanging engineering design knowledge by cognitive agents
Proceedings of the IFIP TC5/WG5.2 international conference on Knowledge intensive CAD volume 2
On agent-based software engineering
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Self-Organizing Maps
Web-based and agent-based approaches for collaborative product design: an overview
International Journal of Computer Applications in Technology
Information Sciences: an International Journal
The agent-based collaboration information system of product development
International Journal of Information Management: The Journal for Information Professionals
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This study is related to the application of Artificial Intelligence approaches for the design of complex systems. The purpose is to propose methods and tools in order to help designers to optimize and to evaluate design parameters according to technical specifications during the embodiment design phase. For this purpose, multi-agent systems are interesting because of their ability to virtually duplicate the process followed by designers' teams. Because of the high number of parameters and possible combinations, a hybrid search approach based on metaheuristic mechanisms is proposed for optimization. More particularly when the task is a multiple objective combinatorial optimization and preference order cannot be defined, the objective functions of the criteria to optimize cannot be weighted and optimization cannot be resumed to a single-objective one. We specified a hybrid algorithm deriving the best (not dominated) solutions set: the Pareto front, from the possible solutions set. Self-Organizing Maps are then used to analyze and evaluate the obtained front. Our approach is illustrated in the case of the design of a 2-Degrees Of Freedom robot.