TY - JOUR
T1 - Efficient decoupling-assisted evolutionary/metaheuristic framework for expensive reliability-based design optimization problems
AU - Meng, Zeng
AU - Rıza Yıldız, Ali
AU - Mirjalili, Seyedali
N1 - Funding Information:
The supports of the National Natural Science Foundation of China (Grant No. 11972143), the Fundamental Research Funds for the Central Universities of China (Grant No. JZ2020HGPA0112), and the Foundation of State Key Laboratory of Structural Analysis for Industrial Equipment from Dalian University of Technology (Grant No. GZ21101) are much appreciated.
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/11/1
Y1 - 2022/11/1
N2 - Reliability-based design optimization (RBDO) algorithm is to minimize the objective under the probabilistic factors. While gradient-based and classical evolutionary RBDO algorithms provide promising performance on simple optimization problems, they are likely to perform poorly on challenging problems, including the multimodal functions, discrete design spaces, non-differential problems, etc. This paper proposes a unified framework to improve the performance of existing RBDO algorithms for complex RBDO problems. Our framework is based on three new strategies: generalized decoupling evolutionary and metaheuristic RBDO framework, particle's memory saving strategy, and adaptive fractional-order equilibrium optimizer algorithm. The proposed algorithm is characterized by a decoupling strategy to enable the parallel operation of the inner reliability computation and outer deterministic optimization, a particle's memory saving strategy to provide effective guidance from the previous iteration, and the adaptive fractional-order equilibrium optimizer algorithm to enhance the search efficiency and global convergence capacity. To evaluate the performance of the proposed algorithm, a wide range of experiments are conducted on different types of use cases. The experimental results demonstrate that our algorithm provides superior performance over other comparative algorithms.
AB - Reliability-based design optimization (RBDO) algorithm is to minimize the objective under the probabilistic factors. While gradient-based and classical evolutionary RBDO algorithms provide promising performance on simple optimization problems, they are likely to perform poorly on challenging problems, including the multimodal functions, discrete design spaces, non-differential problems, etc. This paper proposes a unified framework to improve the performance of existing RBDO algorithms for complex RBDO problems. Our framework is based on three new strategies: generalized decoupling evolutionary and metaheuristic RBDO framework, particle's memory saving strategy, and adaptive fractional-order equilibrium optimizer algorithm. The proposed algorithm is characterized by a decoupling strategy to enable the parallel operation of the inner reliability computation and outer deterministic optimization, a particle's memory saving strategy to provide effective guidance from the previous iteration, and the adaptive fractional-order equilibrium optimizer algorithm to enhance the search efficiency and global convergence capacity. To evaluate the performance of the proposed algorithm, a wide range of experiments are conducted on different types of use cases. The experimental results demonstrate that our algorithm provides superior performance over other comparative algorithms.
KW - Algorithm
KW - Evolutionary algorithm
KW - Fractional-order equilibrium optimizer algorithm
KW - Metaheuristic
KW - Optimization
KW - Particle's memory saving strategy
KW - Reliability-based design optimization
UR - http://www.scopus.com/inward/record.url?scp=85131661764&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2022.117640
DO - 10.1016/j.eswa.2022.117640
M3 - Article
AN - SCOPUS:85131661764
VL - 205
JO - Expert Systems with Applications
JF - Expert Systems with Applications
SN - 0957-4174
M1 - 117640
ER -