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

Meng Z., YILDIZ A. R., Mirjalili S.

EXPERT SYSTEMS WITH APPLICATIONS, cilt.205, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 205
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1016/j.eswa.2022.117640
  • Dergi Adı: EXPERT SYSTEMS WITH APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Computer & Applied Sciences, INSPEC, Metadex, Public Affairs Index, Civil Engineering Abstracts
  • Anahtar Kelimeler: Reliability-based design optimization, Fractional-order equilibrium optimizer algorithm, Metaheuristic, Particle's memory saving strategy, Evolutionary algorithm, Optimization, Algorithm, PERFORMANCE-MEASURE APPROACH, SINGLE-LOOP APPROACH, DIFFERENTIAL EVOLUTION, SEQUENTIAL OPTIMIZATION, STRUCTURAL DESIGN, ROBUST DESIGN, ALGORITHM, APPROXIMATION, LINEARIZATION, GRADIENT
  • Bursa Uludağ Üniversitesi Adresli: Evet

Özet

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.