Mechatronic Design Evolution Using Bond Graphs and Hybrid Genetic Algorithm With Genetic Programming

A typical mechatronic problem (modeling, identification, and design) entails finding the best system topology as well as the associated parameter values. The solution requires concurrent and integrated methodologies and tools based on the latest theories. The experience on natural evolution of an engineering system indicates that the system topology evolves at a much slower rate than the parametric values. This paper proposes a two-loop evolutionary tool, using a hybrid of genetic algorithm (GA) and genetic programming (GP) for design optimization of a mechatronic system. Specifically, GP is used for topology optimization, while GA is responsible for finding the elite solution within each topology proposed by GP. A memory feature is incorporated with the GP process to avoid the generation of repeated topologies, a common drawback of GP topology exploration. The synergic integration of GA with GP, along with the memory feature, provides a powerful search ability, which has been integrated with bond graphs (BG) for mechatronic model exploration. The software developed using this approach provides a unified tool for concurrent, integrated, and autonomous topological realization of a mechatronic problem. It finds the best solution (topology and parameters) starting from an abstract statement of the problem. It is able to carry out the process of system configuration realization, which is normally performed by human experts. The performance of the software tool is validated by applying it to mechatronic design problems.