A novel hybrid harmony search approach for the analysis of plane stress systems via total potential optimization

Kayabekir A. E., Toklu Y. C., BEKDAŞ G., NİGDELİ S. M., Yücel M., Geem Z. W.

Applied Sciences (Switzerland), cilt.10, sa.7, 2020 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 10 Sayı: 7
  • Basım Tarihi: 2020
  • Doi Numarası: 10.3390/app10072301
  • Dergi Adı: Applied Sciences (Switzerland)
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Agricultural & Environmental Science Database, Applied Science & Technology Source, Communication Abstracts, INSPEC, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
  • Anahtar Kelimeler: Flower pollination algorithm, Harmony search algorithm, Jaya algorithm, Plane stress analysis, Teaching-learning-based optimization, Total potential optimization using metaheuristic algorithm
  • İstanbul Gelişim Üniversitesi Adresli: Hayır

Özet

By finding the minimum total potential energy of a structural system with a defined degree of freedoms assigned as design variables, it is possible to find the equilibrium condition of the deformed system. This method, called total potential optimization using metaheuristic algorithms (TPO/MA), has been verified on truss and truss-like structures, such as cable systems and tensegric structures. Harmony Search (HS) algorithm methods perfectly found the analysis results of the previous structure types. In this study, TPO/MA is presented for analysis of plates for plane stress members to solve general types of problems. Due to the complex nature of the system, a novel hybrid Harmony Search (HHS) approach was proposed. HHS is the hybridization of local search phases of HS and the global search phase of the Flower Pollination Algorithm (FPA). The results found via HHS were verified with the finite element method (FEM). When compared with classical HS, HHS provides smaller total potential energy values, and needs less iterations than other new generation metaheuristic algorithms.