Truss-sizing optimization attempts with CSA: a detailed evaluation

ÖZBAŞARAN H., ERYILMAZ YILDIRIM M.

SOFT COMPUTING, vol.24, no.22, pp.16775-16801, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 24 Issue: 22
  • Publication Date: 2020
  • Doi Number: 10.1007/s00500-020-04972-y
  • Journal Name: SOFT COMPUTING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, zbMATH
  • Page Numbers: pp.16775-16801
  • Keywords: Truss, Size optimization, Metaheuristics, CSA, Parameter tuning, PARTICLE SWARM OPTIMIZER, ANT COLONY OPTIMIZATION, CROW SEARCH ALGORITHM, SIZE OPTIMIZATION, OPTIMAL-DESIGN, DISCRETE OPTIMIZATION, HEURISTIC ALGORITHM, OPTIMUM DESIGN, STEEL TRUSSES, CONSTRAINTS
  • Eskisehir Osmangazi University Affiliated: Yes

Abstract

Thanks to the advent of powerful computers, searching for optimal solutions to engineering design problems becomes easier every day. Numerous researchers are still developing modern optimization algorithms, and the competition for "the most efficient optimization algorithm" continues apace. This study evaluates the performances of the Crow Search Algorithm (CSA) and a slightly modified variant (CSA(M)) in one of the most popular and controversial competitions in the structural optimization field for the first time. Unlike most of the works on structural optimization, this paper does not tell a success story. After days of computation to collect the sensitivity and convergence data, it is shown that both CSA and CSA(M) mostly fail compared to today's competitive algorithms. The findings of the study are discussed through tables and plots in detail to share the unfavorable experience on the truss optimization attempts, to review the difficulties of using parameter-controlled algorithms in structural optimization through CSA, and to save time for the researchers in the field.