IRA assisted MMC-based topology optimization method

TL;DR

This paper introduces an IRA-assisted MMC-based topology optimization method that significantly reduces computational costs while maintaining accuracy, using a hybrid optimizer and benchmark evaluations.

Contribution

It integrates IRA with MMC-based topology optimization and proposes a hybrid optimizer to improve convergence and avoid local optima.

Findings

Significant reduction in computational time compared to classical methods.

Maintained accuracy in topology optimization results.

Effective avoidance of local optima with the hybrid optimizer.

Abstract

An Iterative Reanalysis Approximation (IRA) is integrated with the Moving Morphable Components (MMCs) based topology optimization (IRA-MMC) in this study. Compared with other classical topology optimization methods, the Finite Element (FE) based solver is replaced with the suggested IRA method. In this way, the expensive computational cost can be significantly saved by several nested iterations. The optimization of linearly elastic planar structures is constructed by the MMC, the specifically geometric parameters of which are taken as design variables to acquire explicitly geometric boundary. In the suggested algorithm, a hybrid optimizer based on the Method of Moving Asymptotes (MMA) approach and the Globally Convergent version of the Method of Moving Asymptotes (GCMMA) is suggested to improve convergence ratio and avoid local optimum. The proposed approach is evaluated by some classical benchmark problems in topology optimization, where the results show significant time saving without compromising accuracy.