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

This paper presents a novel approach in multidisciplinary design of mechatronic systems, using an inductive genetic programming (IGP) along with a bond graph modeling tool (BG). The proposed design algorithm dynamically explores the space of finding optimal design solutions through utilizing 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 analyzes the efficiency of the different mechatronic elements in improving the system'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. We propose a methodology for optimal design of multidisciplinary systems.We propose inductive genetic programming along bond graph for topology optimization.We suggest learning capability and dynamically self-tuning design procedure.We extract knowledge through the use of suggested algorithm.In a case study we show the superiority of proposed method comparing previous work.