Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems

Gupta S., Abderazek H., YILDIZ B. S., YILDIZ A. R., Mirjalili S., Sait S. M.

EXPERT SYSTEMS WITH APPLICATIONS, vol.183, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 183
  • Publication Date: 2021
  • Doi Number: 10.1016/j.eswa.2021.115351
  • Journal Indexes: 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
  • Keywords: Optimization, Metaheuristic algorithms, Mechanical design problems, Exploration, Exploitation, DIFFERENTIAL EVOLUTION
  • Bursa Uludag University Affiliated: Yes


Determining the solution for real mechanical design problems is a challenging task when using the newly developed and efficient swarm intelligence algorithms. There are so many difficulties to be addressed, including but not limited to mixed decision variables, diverse constraints, inherent errors, conflicting objectives, and numerous locally optimal solutions. This work analyzes the behavior of nine metaheuristic algorithms, namely, salp swarm algorithm (SSA), multi-verse optimizer (MVO), moth-flame optimizer (MFO), atom search optimi-zation (ASO), ecogeography-based optimization (EBO), queuing search algorithm (QSA), equilibrium optimizer (EO), evolutionary strategy (ES) and hybrid self-adaptive orthogonal genetic algorithm (HSOGA). The efficiency of these algorithms is evaluated on eight mechanical design problems using the solution quality and convergence analysis, which verifies the wide applicability of these algorithms to real-world application problems.