Abstract. The architecture design process requires to define several design alternatives and to compare them in order to choose the most relevant system architecture given a set of objectives. Nevertheless, designers are generally constraint to restrict their studies to a small set of alternatives due to time constraints and combinatorial aspects of the problem. The objective of our method is to assist them by automatically generating a larger number of design alternatives. The proposed algorithm will first generate alternatives by adding components to the architecture and allocate them to functions. The originality of our approach is that it takes into account two rules that ensure the viability (component-to-component consistency) and the validity (function-to-component consistency) of the generated architectures. Once a set of consistent alternatives are generated, we use them as an input of a multi-objective genetic algorithm to propose a set of Pareto-optimal alternatives.

No abstract

We propose a new collaborative guidance platform for a team of robots that should protect a fixed ground target from one or several threats. The team of robots performs high-level behaviors. These are hand-coded since they consist in driving the robots to some given position. However, deciding when and how to use these behaviors is much more challenging. Scripting high-level interception strategies is a complex problem and applicable to few specific application contexts. We propose to use a gene regulatory network to regulate high-level behaviors and to enable the emergence of efficient and robust interception strategies.

The design of systems architectures often involve a combinatorial design-space made of technological and architectural choices. A complete or large exploration of this design space requires the use of a method to generate and evaluate design alternatives. This paper proposes an innovative approach for the design-space exploration of systems architectures. The SAMOA (System Architecture Model-based OptimizAtion) tool associated to the method is also introduced. The method permits to create a large number of various system architectures combining a set of possible components to address given system functions. The method relies on models that are used to represent the problem and the solutions and to evaluate architecture performances. An algorithm first synthesizes design alternatives (a physical architecture associated to a functional allocation) based on the functional architecture of the system, the system interfaces, a library of available components and user-defined design rules. Chains of components are sequentially added to an initially empty architecture until all functions are fulfilled. The design rules permit to guarantee the viability and validity of the chains of components and, consequently, of the generated architectures. The design space exploration is then performed in a smart way through the use of an evolutionary algorithm, the evolution mechanisms of which are specific to system architecting. Evaluation modules permit to assess the performances of alternatives based on the structure of the architecture model and the data embedded in the component models. These performances are used to select the best generated architectures considering constraints and quality metrics. This selection is based on the Pareto-dominance-based NSGA-II algorithm or, alternatively, on an interactive preference-based algorithm. Iterating over this evolution-evaluation-selection process permits to increase the quality of solutions and, thus, to highlight the regions of interest of the design-space which can be used as a base for further manual investigations. By using this method, the system designers have a larger confidence in the optimality of the adopted architecture than using a classical derivative approach as many more solutions are evaluated. Also, the method permits to quickly evaluate the trade-offs between the different considered criteria. Finally, the method can also be used to evaluate the impact of a technology on the system performances not only by a substituting a technology by another but also by adapting the architecture of the system.