Elsevier

Applied Mathematical Modelling

Volume 62, October 2018, Pages 1-20
Applied Mathematical Modelling

New simulation-based frameworks for multi-objective reliability-based design optimization of structures

https://doi.org/10.1016/j.apm.2018.05.015Get rights and content
Under an Elsevier user license
open archive

Highlights

  • Two new simulation-based frameworks are presented for MORBDO problems.

  • Only one simulation run is needed during entire multi-objective optimization process.

  • The Pareto optimal set is enhanced by iterative local search of the proposed NSWSM.

  • WSM evaluates reliability of design candidates to conduct NSGA-II to true Pareto-set.

  • New simulations are suitable for complex problems with discrete and continuous variables.

Abstract

In the present study, two new simulation-based frameworks are proposed for multi-objective reliability-based design optimization (MORBDO). The first is based on hybrid non-dominated sorting weighted simulation method (NSWSM) in conjunction with iterative local searches that is efficient for continuous MORBDO problems. According to NSWSM, uniform samples are generated within the design space and, then, the set of feasible samples are separated. Thereafter, the non-dominated sorting operator is employed to extract the approximated Pareto front. The iterative local sample generation is then performed in order to enhance the accuracy, diversity, and increase the extent of non-dominated solutions. In the second framework, a pseudo-double loop algorithm is presented based on hybrid weighted simulation method (WSM) and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) that is efficient for problems including both discrete and continuous variables. According to hybrid WSM-NSGA-II, proper non-dominated solutions are produced in each generation of NSGA-II and, subsequently, WSM evaluates the reliability level of each candidate solution until the algorithm converges to the true Pareto solutions. The valuable characteristic of presented approaches is that only one simulation run is required for WSM during entire optimization process, even if solutions for different levels of reliability be desired. Illustrative examples indicate that NSWSM with the proposed local search strategy is more efficient for small dimension continuous problems. However, WSM-NSGA-II outperforms NSWSM in terms of solutions quality and computational efficiency, specifically for discrete MORBDOs. Employing global optimizer in WSM-NSGA-II provided more accurate results with lower samples than NSWSM.

Keywords

Reliability-based design
Multi-objective optimization
Flexible hybrid algorithm
Weighted simulation method
Non-dominated Sorting Genetic Algorithm II (NSGA-II)

Cited by (0)