Efficient decoupling-assisted evolutionary/metaheuristic framework for expensive reliability-based design optimization problems

Zeng Meng, Ali Rıza Yıldız, Seyedali Mirjalili

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number117640
JournalExpert Systems with Applications
Volume205
DOIs
Publication statusPublished - 1 Nov 2022

Keywords

  • Algorithm
  • Evolutionary algorithm
  • Fractional-order equilibrium optimizer algorithm
  • Metaheuristic
  • Optimization
  • Particle's memory saving strategy
  • Reliability-based design optimization

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