Machine design optimization by the Monte Carlo annealing method

The paper presents the application of the Monte Carlo annealing method for machine design optimization. The method generates the finite inhomogeneous Markov chain in the space of discrete design parameters with the transition probability dependent on the objective function and given by the Boltzmann-Gibbs distribution of equilibrium statistical mechanics. In this approach the search for the global minimum is simulated by a relaxation towards an equilibrium of the thermodynamic sysem with the energy proportional to an objective function. The method is applied for the cost optimization of a speed reductor. The performance and prospects of the method for machine design optimization are discussed.