Self-adjusting multidisciplinary design of hydraulic engine mount using bond graphs and inductive genetic programming

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@Article{Tabatabaei:2016:EAAI,
  author =       "S. Karim Tabatabaei and Saeed Behbahani and 
                 Clarence W. {de Silva}",
  title =        "Self-adjusting multidisciplinary design of hydraulic
                 engine mount using bond graphs and inductive genetic
                 programming",
  journal =      "Engineering Applications of Artificial Intelligence",
  volume =       "48",
  pages =        "32--39",
  year =         "2016",
  ISSN =         "0952-1976",
  DOI =          "doi:10.1016/j.engappai.2015.10.010",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0952197615002390",
  abstract =     "This paper presents a novel approach in
                 multidisciplinary design of mechatronic systems, using
                 an inductive genetic programming (IGP) along with a
                 bond graph modelling tool (BG). The proposed design
                 algorithm dynamically explores the space of finding
                 optimal design solutions through using two navigated
                 steps for simultaneous optimization of both topology
                 and parameters. In the first step, an IGP tool is
                 applied on the bond graph embryo model of the system
                 for topology synthesis. In the second step, an
                 optimization tool that incorporates an artificial
                 immune system (AIS) is implemented for optimization of
                 the parameter values. A supervisory loop statistically
                 analyses the efficiency of the different mechatronic
                 elements in improving the system times's performance.
                 By acquiring knowledge and learning from prior trials,
                 the evolution parameters are automatically and
                 dynamically adjusted, with the aim to achieve more
                 efficient evolution progress. The developed method is
                 practically compared with an available bond
                 graph-genetic programming (BGGP) method via designing
                 an aerospace engine mount system. Results show that
                 more navigated and accurate design results are acquired
                 from the proposed method.",
  keywords =     "genetic algorithms, genetic programming,
                 Multidisciplinary design, Bond graphs, Artificial
                 immune system, Evolutionary design",
}

Genetic Programming entries for S Karim Tabatabaei Saeed Behbahani Clarence W de Silva

Citations