TY  - EJOU
AU  - Zhou, Hang 
AU  - Ding, Xiaojun 
AU  - Chen, Song 
AU  - Zhang, Qijun 

TI  - Reliability Topology Optimization Based on Kriging-Assisted Level Set Function and Novel Dynamic Hybrid Particle Swarm Optimization Algorithm
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 144
IS  - 2
SN  - 1526-1506

AB  - Structural Reliability-Based Topology Optimization (RBTO), as an efficient design methodology, serves as a crucial means to ensure the development of modern engineering structures towards high performance, long service life, and high reliability. However, in practical design processes, topology optimization must not only account for the static performance of structures but also consider the impacts of various responses and uncertainties under complex dynamic conditions, which traditional methods often struggle accommodate. Therefore, this study proposes an RBTO framework based on a Kriging-assisted level set function and a novel Dynamic Hybrid Particle Swarm Optimization (DHPSO) algorithm. By leveraging the Kriging model as a surrogate, the high cost associated with repeatedly running finite element analysis processes is reduced, addressing the issue of minimizing structural compliance. Meanwhile, the DHPSO algorithm enables a better balance between the population’s developmental and exploratory capabilities, significantly accelerating convergence speed and enhancing global convergence performance. Finally, the proposed method is validated through three different structural examples, demonstrating its superior performance. Observed that the computational that, compared to the traditional Solid Isotropic Material with Penalization (SIMP) method, the proposed approach reduces the upper bound of structural compliance by approximately 30%. Additionally, the optimized results exhibit clear material interfaces without grayscale elements, and the stress concentration factor is reduced by approximately 42%. Consequently, the computational results from different examples verify the effectiveness and superiority of this study across various fields, achieving the goal of providing more precise optimization results within a shorter timeframe.
KW  - Reliability topology optimization; kriging model; level set function; dynamic hybrid particle swarm optimization; engineering structure

DO  - 10.32604/cmes.2025.069198