A multi-strategy boosted prairie dog optimization algorithm for global optimization of heat exchangers

GÜRSES D., Mehta P., Sait S. M., Kumar S., YILDIZ A. R.

MATERIALS TESTING, cilt.65, sa.9, ss.1396-1404, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 65 Sayı: 9
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1515/mt-2023-0082
  • Dergi Adı: MATERIALS TESTING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex
  • Sayfa Sayıları: ss.1396-1404
  • Anahtar Kelimeler: heat exchangers, metaheuristics, prairie dog optimization algorithm, thermal system optimizations, DESIGN OPTIMIZATION, ROBUST DESIGN, METAHEURISTIC ALGORITHM, ECONOMIC OPTIMIZATION, GENETIC ALGORITHM, SEARCH ALGORITHM, TOPOLOGY DESIGN, HYBRID APPROACH, CRASHWORTHINESS, PARAMETERS
  • Bursa Uludağ Üniversitesi Adresli: Evet

Özet

In this article, a new prairie dog optimization algorithm (PDOA) is analyzed to realize the optimum economic design of three well-known heat exchangers. These heat exchangers found numerous applications in industries and are an imperative part of entire thermal systems. Optimization of these heat exchangers includes knowledge of thermo-hydraulic designs, design parameters and critical constraints. Moreover, the cost factor is always a challenging task to optimize. Accordingly, total cost optimization, including initial and maintenance, has been achieved using multi strategy enhanced PDOA combining PDOA with Gaussian mutation and chaotic local search (MSPDOA). Shell and tube, fin-tube and plate-fin heat exchangers are a special class of heat exchangers that are utilized in many thermal heat recovery applications. Furthermore, numerical evidences are accomplished to confirm the prominence of the MSPDOA in terms of the statistical results. The obtained results were also compared with the algorithms in the literature. The comparison revealed the best performance of the MSPDOA compared to the rest of the algorithm. The article further suggests the adaptability of MSPDOA for various real-world engineering optimization cases.