Spacecraft design optimization is a difficult problem, due to the complexity of optimization cost surfaces and the human expertise in optimization that is necessary in order to achieve good results. In this paper, we propose the use of a set of generic, metaheuristic optimization algorithms (e.g., genetic algorithms, simulated annealing), which is configured for a particular optimization problem by an adaptive problem solver based on artificial intelligence and machine learning techniques. We describe work in progress on OASIS, a system for adaptive problem solving based on these principles.


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    Title :

    Automating the process of optimization in spacecraft design


    Contributors:
    Fukunaga, A.S. (author) / Chien, S. (author) / Mutz, D. (author) / Sherwood, R.L. (author) / Stechert, A.D. (author)

    Published in:

    Publication date :

    1997-01-01


    Size :

    1555653 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Automating the Process of Optimization in Spacecraft Design

    Fukunaga, A. S. / Chien, S. / Mutz, D. et al. | British Library Conference Proceedings | 1997