Heterogeneous-Objective Robust Optimization of Complex Mechatronic Components Considering Interval Uncertainties

Abstract

Structural optimization of complex mechatronic components may involve heterogeneous competing performance indices, including the cost, fixation, benefit, and deviation ones. However, such optimization problems with heterogeneous objectives have not been investigated so far. In this paper, a novel interval heterogeneous-objective robust optimization approach is proposed for complex mechatronic components. First, a unified interval heterogeneous-objective robust optimization model is constructed for mechatronic components with the uncertainties described as interval variables. Subsequently, a new interval robust equilibrium optimization algorithm is proposed to solve the interval heterogeneous-objective robust optimization model. Specifically, the unified formulas for assessing the robustness of interval heterogeneous-objective performance indices and the robust equilibrium among them are derived at first. Then, the preferential guidelines considering the robust equilibrium among all the objective and constraint performance indices are proposed for the direct ranking of various design vectors, and finally, the heterogeneous-objective robust equilibrium optimization of complex mechatronic components is realized by integrating the Kriging technique and the nested genetic algorithm. The feasibility and effectiveness of the proposed heterogeneous-objective robust optimization approach are verified by a numerical example and a case study. INDEX TERMS Heterogeneous-objective robust optimization, complex mechatronic component, interval, robust equilibrium, closeness coefficient, group ranking.

Novel Interval Parameter Identification Method Using Augmented Fourier Series-Based Polynomial Surrogate Model

“…In recent years, interval theory-based non-probabilistic parameter identification methods have been applied to various fields and succeeded to some extent [14]- [17]. Khodaparast et al [18] first proposed the parameter vertex method, which was valid only for the particular case of parameter identification; afterwards, they presented a global optimization method for the general case based on sensitivity analysis of the Kriging predictor.…”